Table of Contents
Overview
Definition and Scope
Science and Technology Studies (STS) is an interdisciplinary field that examines the intricate relationships between science, technology, and society. It encompasses a broad range of academic inquiry, connecting the sciences, social sciences, and humanities to address significant questions regarding the development and impact of scientific and technological advancements across different cultures and historical contexts.[1.1] The discipline challenges traditional notions of knowledge as being universal and neutral, advocating for a critical perspective that views science and technology as socially embedded phenomena.[3.1] STS investigates how these constructs influence and are influenced by societal factors, raising various epistemological, political, and ethical questions.[4.1] This field not only focuses on the analysis of scientific and technological change but also emphasizes the importance of understanding the cultural and rational frameworks that shape these developments.[2.1]Importance in Contemporary Society
In contemporary society, the importance of public engagement in science and technology (PEST) has gained significant attention, particularly as technological advancements and societal challenges continue to evolve. Critics have raised concerns regarding the limitations of case study-based research in PEST, arguing that such studies often lack a global, holistic perspective on public engagement phenomena, which is essential for informed science policy formulation.[5.1] Despite these criticisms, public engagement is increasingly recognized as a vital component of science policy and communication, serving as a mechanism to enhance democratic discussions surrounding science and technology.[6.1] A comprehensive analysis of literature from leading science communication journals and various science policy documents reveals a growing ambiguity in the definitions of 'engagement' and the 'public' involved, alongside a diverse array of motives driving public participation rhetoric.[7.1] This discourse has evolved significantly, particularly within the research policies of the European Commission, highlighting the need for clarity and systematic approaches to public engagement.[8.1] However, challenges remain in achieving meaningful public input into science and technology policy, as social science has yet to provide definitive guidance on optimal forms and conditions for engagement.[9.1] The rapid evolution of technology has also intensified debates surrounding ethical considerations in science and technology. Ethical frameworks are increasingly necessary to guide the responsible development and deployment of new technologies, ensuring that they align with societal values and public expectations.[10.1] UNESCO has played a pivotal role in fostering international dialogue on the ethics of life sciences since the 1970s, promoting collaboration among various stakeholders to develop sound policies addressing ethical issues in science and technology.[11.1] Moreover, the rise in public scrutiny of scientific practices necessitates greater transparency and ethical accountability from researchers and institutions. This scrutiny reinforces the importance of ethical standards in maintaining public trust and ensuring participant safety in research.[12.1] As technology continues to advance, addressing ethical challenges—such as algorithmic bias and data privacy—becomes crucial for balancing innovation with societal responsibility.[13.1] Ultimately, the interplay between societal values and technological innovation is increasingly recognized as a critical factor in addressing contemporary global challenges, such as climate change and public health crises. Scholars emphasize the need to incorporate values early in the design phase of new technologies to mitigate potential ethical issues.[15.1] Thus, the role of science and technology studies in contemporary society is not only to advance knowledge but also to ensure that this knowledge is applied ethically and responsibly in the face of ongoing societal transformations.In this section:
History
Early Developments in Science and Technology Studies
The early developments in Science and Technology Studies (STS) emerged as a distinct academic discipline that seeks to understand the intricate relationships between science, technology, and society. This field connects various domains, including the sciences, social sciences, and humanities, addressing significant questions regarding the evolution and impact of scientific and technological advancements across different cultures and historical periods, including the recent past.[38.1] One of the foundational ideas of STS is the assertion that the content of science and engineering—comprising scientific facts and technological objects—is subject to social analysis. This perspective contrasts with earlier approaches in the sociology, history, or economics of science and technology, which often regarded scientific knowledge as a privileged form of understanding nature. Instead, STS posits that both the social institutions of science and the very content of scientific knowledge are open to scrutiny and analysis.[39.1] The discipline has evolved to encompass a broader chronological and thematic scope, with increasing attention given to modern and contemporary science, as well as the practices of science rather than solely its theories. This shift has led to a focus on the material culture of science and the lived experiences of scientists, thereby enriching the understanding of how scientific knowledge is constructed and disseminated.[40.1] Institutions such as the Program on Science, Technology and Society at Harvard University have played a pivotal role in formalizing STS as an academic field. The program emphasizes the importance of understanding the nature of scientific controversies, the causes of technological change, and the interplay between culture and rationality. Through its courses, STS aims to foster a deeper comprehension of how societies produce and utilize science and technology.[41.1]Key Theoretical Contributions
Key theoretical contributions to Science and Technology Studies (STS) have emerged from various philosophical and sociological perspectives, particularly focusing on the relationship between scientific facts and social constructs. A significant aspect of this discourse is the notion that scientific knowledge is not merely a reflection of objective reality but is instead constructed through social processes. This perspective is notably articulated in the works of scholars like Latour and Woolgar, who emphasize the social construction of scientific facts, suggesting that scientific phenomena are shaped by the contexts in which they are produced rather than being direct manifestations of reality.[63.1] The framework of social constructivism plays a crucial role in STS, positing that there is no clear distinction between the production and consumption of knowledge. This approach democratizes epistemology by challenging traditional hierarchies in knowledge authority, thereby allowing for a more inclusive understanding of how knowledge is created and validated.[64.1] Furthermore, the integration of cognitive and socio-cultural factors into the study of scientific evolution has encouraged a more nuanced exploration of how scientific practices are influenced by societal values and cultural contexts.[54.1] Additionally, the evolution of the social contract between science and society, particularly in the context of globalization, has been a focal point in STS discussions. This changing relationship reflects a shift from viewing scientific knowledge as a public good to recognizing its role in wealth creation and market dynamics.[53.1] Such transformations underscore the importance of understanding the ethical implications of scientific advancements and the societal responsibilities of scientists and technologists.Methodologies
Qualitative Approaches
Qualitative approaches in Science and Technology Studies (STS) encompass a variety of research methods aimed at gaining in-depth insights into individuals' experiences and perspectives regarding science and technology. Common qualitative research methods include interviews, focus groups, observations, and content analysis, each designed to explore complex social phenomena in a nuanced manner.[79.1] The qualitative research tradition has a long-standing history within the social sciences and humanities, emphasizing diverse theoretical foundations and research strategies.[80.1] One of the primary goals of qualitative research is to provide deeper insights into real-world problems, moving beyond mere numerical data to understand the context and meaning behind participants' experiences.[81.1] This approach is particularly valuable in STS, where the interplay between technology and society is often intricate and multifaceted. However, challenges arise in ensuring that qualitative methods effectively capture the diverse perspectives of various stakeholders. Best practices for conducting qualitative interviews include creating a comfortable setting, beginning with easy questions to build rapport, and ensuring confidentiality to foster trust.[84.1] Moreover, the integration of qualitative and quantitative methods, known as mixed methods research, is increasingly recognized for its potential to enhance the understanding of complex phenomena in STS. This approach allows researchers to draw on the strengths of both qualitative and quantitative data, providing a more comprehensive view of the research questions at hand.[88.1] For instance, mixed methods have been employed to explore nuanced topics such as electronic personal health records and patient-physician communication, demonstrating the value of integrating diverse data sources.[89.1] Despite the strengths of qualitative methods in generating rich, detailed insights, they also have limitations, which can be mitigated by the strengths of quantitative approaches. The combination of these methodologies can lead to more robust findings that are both reliable and contextually rich, ultimately contributing to a deeper understanding of the dynamics within science and technology.[90.1]In this section:
Key Themes
Social Construction of Technology
The Social Construction of Technology (SCOT) framework is a pivotal theory within the field of Science and Technology Studies (STS) that challenges traditional notions of technological determinism. SCOT posits that technological innovation is not a linear process dictated solely by scientific advancements; rather, it is a complex interplay of various social groups competing to define what constitutes "superior technology" during the phase of interpretive flexibility. This perspective emphasizes that multiple stakeholders, each with their own values and interests, influence the design and implementation of technologies, thereby undermining the idea that technology evolves in a predetermined manner.[129.1] By illustrating how societal values shape technological advancements, SCOT advocates for broader public engagement in the innovation process. It argues that understanding technology requires acknowledging the diverse interpretations and meanings that different social groups ascribe to technological artifacts.[129.1] This approach not only highlights the role of social context in technological development but also calls for inclusive participation from various stakeholders to ensure that technological outcomes reflect a wider array of societal needs and values.[128.1] The SCOT framework has significant implications for policy-making and public engagement strategies. It suggests that policymakers should actively involve relevant social groups in discussions about technology to harness collective intelligence and maintain public trust.[121.1] This participatory approach can lead to more equitable and socially responsive technological solutions, as it recognizes that technology is co-constructed through social interactions rather than being an isolated product of scientific endeavor.[129.1]Ethics in Science and Technology
Recent developments in Science and Technology Studies (STS) have increasingly emphasized the importance of ethics in the design and implementation of technologies. A notable trend is the integration of "embedded ethics," which reflects a growing commitment to incorporate social and ethical considerations into computer science education and the early stages of artificial intelligence (AI) and robotics development. For instance, the interdisciplinary project Responsible Robotics at the Technical University of Munich exemplifies this approach by uniting expertise from mechanical engineering, ethics, and social sciences to address ethical concerns in technology.[124.1] The Tech for Good initiative further illustrates how ethical frameworks can be effectively integrated into technology development. This initiative encourages organizations to prioritize social impact alongside profitability, thereby fostering responsible tech development that benefits society.[125.1] Additionally, establishing ethical AI frameworks within organizations is crucial for ensuring that AI technologies remain innovative and ethically sound. This involves developing clear ethical guidelines, training teams in responsible AI practices, conducting bias audits, and regularly monitoring AI systems to ensure compliance with ethical standards.[126.1] Bias in scientific research and technological design is another critical ethical concern. Bias can manifest at various stages of the research process, including study design, participant selection, data collection, and analysis. For example, a controversial study linking the measles-mumps-rubella vaccine to autism was retracted due to significant biases in its research process, highlighting the importance of transparency and rigorous methodology in scientific inquiry.[138.1] Understanding and addressing these biases is essential for maintaining the integrity of scientific findings and public trust in science.[137.1] Incorporating intersectional feminist perspectives into STS can enhance our understanding of biases present in both scientific research and technological design. This approach emphasizes the interconnectedness of social forces and historical contexts, which can lead to more inclusive and equitable knowledge production.[140.1] By recognizing the diverse experiences and challenges faced by different groups, researchers can develop more comprehensive strategies to mitigate bias and promote ethical practices in science and technology. Case studies play a vital role in exploring the ethical implications of technology. Initiatives like the Ethics, Society, and Technology Case Study Program aim to produce high-quality, open-source case studies that examine the intersection of ethics and technology in real-world scenarios. These case studies facilitate in-depth discussions about moral and practical trade-offs, providing valuable lessons for guiding future innovations.[146.1] Through such explorations, STS can contribute to a more ethically aware and socially responsible technological landscape.In this section:
Recent Advancements
Emerging Trends in Research
Recent advancements in science and technology studies have highlighted several emerging trends in research across various fields. One significant area of development is the application of CRISPR-Cas9 gene-editing technology, which has led to the approval of therapies such as Casgevy by the U.S. FDA. This therapy represents a breakthrough in the treatment of genetic disorders, with numerous CRISPR-based therapies now entering drug discovery pipelines and clinical trials targeting a wide range of diseases.[155.1] The integration of artificial intelligence (AI) with CRISPR technology is also noteworthy, as AI is increasingly utilized to enhance synthetic applications, enabling chemists to identify and prioritize synthetic pathways more effectively.[155.1] In addition to gene editing, advancements in single-cell analysis technologies have surged, facilitating significant progress in early disease detection, prenatal screening, biomarker testing, and biologic drug development.[155.1] This trend reflects a broader movement towards precision medicine, which leverages genomic data to tailor healthcare to individual genetic profiles. Researchers are developing innovative CRISPR tools that improve gene editing and disease modeling, further advancing the field of personalized genomics.[162.1] Moreover, the One Health approach has gained traction in pandemic preparedness, emphasizing the importance of interdisciplinary collaboration among scientists, technologists, and policymakers. This framework aims to address health challenges at the human-animal-ecosystem interface, promoting proactive measures against emerging infectious diseases and zoonoses.[171.1] By fostering collaboration and multisectoral actions, the One Health paradigm seeks to prevent zoonotic disease outbreaks and enhance environmental health.[172.1]Impact of AI and Technology on STS
Artificial intelligence (AI) is increasingly recognized as a transformative force within the field of Science and Technology Studies (STS), significantly influencing both research methodologies and theoretical frameworks. The integration of AI into scientific inquiry is expected to enhance research processes by facilitating collaboration among scientists from various disciplines, such as biology, physics, and computer science, thereby fostering innovation that transcends traditional boundaries.[157.1] Current and future developments in AI systems are poised to revolutionize the research process, serving as collaborators that streamline and conduct research more efficiently.[158.1] AI's capabilities extend to performing complex methodological tasks, enhancing research accessibility, and improving scientific communication, making it an indispensable tool in contemporary research.[159.1] The release of advanced AI models, such as ChatGPT 3.5 in November 2022, has further accelerated interest in AI applications within scientific inquiry.[159.1] Case studies have demonstrated successful implementations of AI, showcasing its potential to enhance research methodologies and outcomes.[160.1] Moreover, AI is being utilized to augment and accelerate scientific discovery by assisting researchers in generating hypotheses, designing experiments, and interpreting large datasets.[161.1] Breakthroughs in AI, including self-supervised learning and generative AI methods, have enabled scientists to analyze diverse data modalities and create innovative solutions, such as small-molecule drugs and proteins.[161.1] As AI continues to evolve, ethical considerations surrounding its use in scientific research are also gaining prominence. Researchers are urged to adopt clear frameworks for ethical and responsible AI usage, particularly in scholarly environments.[174.1] The development of responsible AI governance frameworks is essential to address issues such as plagiarism, bias, privacy, and transparency, which require precise norms and human oversight.[176.1] The sheer number of existing frameworks can complicate the selection process for organizations, highlighting the need for a structured approach to implementing responsible AI practices.[175.1]Interdisciplinary Connections
Relationship with Sociology
The relationship between Science, Technology, and Society Studies (STS) and sociology is characterized by a shared interest in understanding the social dimensions of scientific and technological practices. STS emerged as an interdisciplinary field during the social upheavals of the 1960s and 1970s, drawing from various disciplines, including sociology, to analyze science and technology as complex social constructs that influence and are influenced by societal factors.[192.1] A fundamental aspect of STS is its critique of traditional sociological approaches to science, which often focused solely on the social institutions surrounding scientific practice. In contrast, STS posits that the content of science and engineering—including scientific facts and technological objects—is also subject to social analysis, challenging the notion that scientific knowledge is a privileged form of understanding nature.[194.1] This perspective aligns with sociological inquiries into how social contexts shape knowledge production and the implications of technological advancements on society. Moreover, interdisciplinary collaboration is a key feature of STS, as it often involves scholars from diverse fields, including sociology, philosophy, law, and public health, working together to address complex societal challenges.[195.1] Such collaborations are essential for expanding the understanding of how scientific and technological developments intersect with social issues, thereby enhancing the relevance and applicability of research findings.[221.1] The dynamics within scientific communities can either facilitate or hinder these interdisciplinary efforts. Factors such as institutional support, funding, and the availability of resources play a critical role in fostering successful collaborations.[219.1] Research indicates that interdisciplinary science positively influences knowledge production and innovation, highlighting the importance of collaborative networks in enhancing the effectiveness of research teams.[224.1]Influence on Policy and Governance
The influence of Science and Technology Studies (STS) on policy and governance is profound, as it emphasizes the interplay between societal values, political decisions, and scientific research. STS highlights how extrainstitutional factors, such as states, industries, and social movements, shape scientific research fields and technological design choices, thereby affecting policy outcomes and governance structures.[196.1] This diversification in STS has led to a renewed focus on the role of public opinion and social movements in determining research priorities and technological advancements, illustrating how societal needs can direct the trajectory of scientific inquiry.[198.1] Public engagement in science and technology policy is increasingly recognized as essential for democratic governance. Research indicates that public opinion can significantly influence funding priorities and the perception of various fields of study, which in turn affects policy decisions that allocate resources to specific scientific agendas.[206.1] The integration of public engagement into science policy is seen as a means to ensure that scientific endeavors align with societal values and needs, fostering a more inclusive approach to governance.[8.1] Moreover, the challenges posed by technology, such as the digital divide and cultural homogenization, underscore the necessity for policies that are informed by diverse cultural perspectives and ethical considerations.[205.1] As scientists and policymakers navigate these complexities, they must acknowledge that science is not neutral; rather, it is shaped by ethical and social dimensions that must be considered in policy formulation.[205.1] This recognition is crucial for developing governance frameworks that are responsive to the dynamic relationship between science, technology, and society.Prominent Scholars And Works
Influential Figures in STS
Influential figures in Science and Technology Studies (STS) have significantly shaped the field through their theoretical contributions and empirical research. Among the most notable scholars are Donald MacKenzie and Judy Wajcman, who played a pivotal role in establishing the framework of the Social Shaping of Technology (SST). Their seminal work, The Social Shaping of Technology (1985), emphasized the importance of social context in understanding technological development, challenging the deterministic views that often dominate discussions about technology.[243.1] This collection of articles laid the groundwork for subsequent research that interrogated the interplay between society and technology, marking a shift towards recognizing the fluid and situated nature of the gender-technology relationship.[242.1] MacKenzie’s perspective on technological development advocates for a sociological approach that views technology as a product of social construction rather than a natural trajectory.[241.1] This idea is further supported by Wajcman's feminist lens, which critiques how technological advancements can perpetuate existing inequalities and power dynamics.[240.1] Their contributions have not only influenced academic discourse but have also prompted broader public engagement in technological innovation processes.[272.1] Another significant contribution to STS is the Social Construction of Technology (SCOT) theory, developed by Wiebe Bijker and Trevor Pinch. SCOT posits that the acceptance or rejection of technology is deeply rooted in social contexts, emphasizing the role of "relevant social groups" in shaping technological outcomes.[273.1] Their work highlights the concept of interpretive flexibility, where various social groups compete to define what constitutes "superior technology," thereby undermining the notion of technological determinism.[272.1] This framework has been instrumental in advancing contemporary STS research and policy debates, illustrating the necessity of considering diverse social perspectives in technological development.[273.1]In this section:
Future Directions
Challenges and Opportunities
The integration of artificial intelligence (AI) into scientific research presents both challenges and opportunities for the future of Science and Technology Studies (STS). AI is recognized as a transformative technology that enhances various fields, including Science, Technology, Engineering, and Medicine (STEM), by mimicking human intelligence to perform tasks at unprecedented speeds.[281.1] The Royal Society's Disruptive Technology for Research project highlights the importance of understanding AI's role across different scientific domains, emphasizing the need for a comprehensive literature review on its applications.[282.1] As AI continues to evolve, it is increasingly utilized to augment scientific discovery, enabling researchers to generate hypotheses, design experiments, and analyze large datasets more efficiently than traditional methods allow.[283.1] However, the ethical implications of AI, particularly concerning biases inherent in the datasets used for training AI models, pose significant challenges. These biases can lead to unfair or misinformed conclusions, raising concerns about the integrity of research outcomes.[284.1] Interdisciplinary collaboration is essential in addressing these challenges. Recent initiatives, such as the Interdisciplinary Collaboration Committee at Temple University, aim to identify barriers to collaboration and propose strategies to enhance interdisciplinary opportunities in research.[285.1] Such collaborations can lead to significant outcomes by bringing together diverse perspectives and expertise, which is crucial for navigating the complexities of emerging technologies.[286.1] Moreover, understanding the ethical implications of these technologies is vital for effective policy formulation. Emerging technologies shape policy development and governance, necessitating a comprehensive understanding of their benefits, risks, and ethical considerations.[288.1] However, challenges such as high teaching workloads, a discouraging research culture for cross-disciplinary work, and unfamiliarity with research processes can hinder effective collaboration.[287.1]Potential Areas for Further Research
Emerging trends in science and technology studies indicate several potential areas for further research that could significantly impact various fields. One notable area is the integration of artificial intelligence (AI) and machine learning in biomedical research. These technologies are transforming the landscape by bridging the gap between research and real-world applications, thereby accelerating innovation through open-access platforms and data-sharing initiatives.[275.1] The clinical validation of CRISPR technology also presents a promising avenue for exploration, particularly as it has led to the development of therapies for previously untreatable conditions, such as sickle cell disease and angioedema.[279.1] Moreover, the application of big data analytics in precision agriculture and plant health is another critical area ripe for investigation. Research communities are increasingly utilizing machine learning algorithms to tackle global challenges related to food security and environmental sustainability.[278.1] This intersection of big data and AI not only enhances agricultural practices but also raises important ethical and regulatory questions that warrant further examination.[304.1] Additionally, the rise of green chemistry and advancements in battery technology are emerging fields that could benefit from deeper inquiry. These areas are crucial for addressing environmental concerns and improving energy efficiency, respectively.[279.1] The ongoing development of single-cell analysis technologies also offers significant potential for early disease detection and personalized medicine, highlighting the need for research into their applications and implications.[279.1] Finally, the evolving landscape of data-driven organizational cultures, as evidenced by the increasing confidence in generative AI's productivity gains, suggests a need for research into how these cultural shifts affect innovation and operational efficiency within organizations.[277.1] Collectively, these areas represent a rich tapestry of opportunities for future research in science and technology studies, each with the potential to drive significant advancements across multiple disciplines.In this section:
References
[1] About History of Science & Science Studies - Harvard University — About History of Science & Science Studies . History of Science, Technology, and Medicine is an academic discipline of great scope and international reach that connects the sciences, social sciences, and humanities. It deals with important questions about the rise and impact of science, medicine, and technology, both east and west, and at all
[2] Harvard STS Program » About » What is STS? - Harvard University — Program on Science, Technology and Society at HarvardHarvard Kennedy School of Government | Harvard UniversityHomeAboutPeopleNews & EventsResearchAcademic ProgramsResourcesConnectAbout »Mission StatementWhat is STS?FellowsScience and Democracy NetworkCollaborationsContact InformationWhat is STS?Science and Technology Studies (STS) is a relatively new academic field. STS courses in these areas enable students to form more robust understandings of the nature of controversy, the causes of scientific and technological change, the relationship of culture and reason, and the limits of rational analytic methods in characterizing complex problems.In sum, STS explores in rich and compelling ways what difference it makes to human societies that we, collectively, are producers and users of science and technology.
[3] Science, Technology and Society: An Introduction | SpringerLink — Science, Technology and Society: An Introduction provides students with an accessible overview of the interdisciplinary field of Science and Technology Studies (STS). The discipline breaks down traditional conceptions of knowledge as universal, neutral and ahistorical, and takes a more critical approach to science and technology as social embedded phenomena.
[4] Science, Technology, and Society Studies - Encyclopedia.com — Science, Technology, and Society Studies | Encyclopedia.com Science, Technology, and Society Studies, or STS, is an interdisciplinary field of academic teaching and research, with elements of a social movement, having as its primary focus the explication and analysis of science and technology as complex social constructs with attendant societal influences entailing myriad epistemological, political, and ethical questions. Arguing in support of the objective nature of scientific evidence and science as a special way of knowing, a number of such individuals led by Paul Gross and Norman Levitt (1994) and Alan Sokol (1996a, 1996b, 1998) took issue with some of the more relativist-oriented STS scholars, such as Bruno Latour (1987), and launched a series of sharp attacks in print and at academic conferences.
[5] Mapping Case Studies of Public Engagement and Participation in Science ... — In recent years, increasing criticism has been levelled against case study based research on public engagement and participation in science and technology (PEST). Most critics argue that such case studies are highly contextual and fail to provide global, holistic and systemic views of public engagement phenomena. In this study, we mapped the case study literature on PEST by identifying a
[6] Experiments in engagement: Designing public engagement with science and ... — Public engagement with science and technology is now widely used in science policy and communication. Touted as a means of enhancing democratic discussion of science and technology, analysis of public engagement with science and technology has shown that it is often weakly tied to scientific governa …
[7] (PDF) Public engagement with science—Origins, motives and impact in ... — By way of a content analysis of articles published in three leading science communication journals and a selection of science policy documents from the United Kingdom (UK), the United States of America (USA), the European Union (EU), and South Africa (SA), the variety of motives underlying this rhetoric, as well as the impact it has on science policies, are analyzed. The analysis of the science communication journals reveals an increasingly vague and inclusive definition of ‘engagement’ as well as of the ‘public’ being addressed, and a diverse range of motives driving the rhetoric. This article investigates how a discourse about the role and value of public participation in science, technology, and innovation emerged and evolved in the research policies of the European Commission.
[8] "Public Engagement for Informing Science and Technology Policy: What Do ... — This article examines social science relevant to public engagements and identifies the challenges to the goal of meaningful public input into science and technology policy. Specifically, when considering "which forms, features, and conditions of public engagement are optimal for what purposes, and why?" we find social science has not clarified matters. We offer a model to guide systematic
[9] PDF — The Future of Public Engagement Social Science As our which, for what, and why analysis suggests, major barriers to the advancement of the social science of public engagement in general (see especially, Rowe, Horlick-Jones, Walls, Poortinga, & Pidgeon, 2008), and the problems that impede successful public contributions to nanotechnology policy in particular, include the following: the large diversity of approaches within and across engagement practices; the lack of agreement on definitions of “effective” engagement, whether the focus is on the public participants or the policy makers or in the case of scientists, others who might use the input; and the lack of theoretical or empirical attention to the reasons why or mechanisms by which certain public engagement features appear to connect to various outcomes.1 Earlier, we suggested some general ideas concerning dealing with these barriers including applying strategies for identifying features of public engagement worthy of experimental examination, focusing on various effectiveness criteria that relate to specific preparation and execution phases of engagement, and appropriately applying well-established theories from other fields, especially from social and learning sciences, in order to advance theoretical understanding of public engagement activities and outcomes.
[10] PDF — The rapid evolution of technology in recent decades has significantly impacted various aspects of human life, sparking intense debates on ethics and safety. ... We discuss the importance of ethical considerations in technology science, emphasizing the need for robust ethical frameworks to guide the responsible development, deployment, and
[11] Ethics of Science and Technology - UNESCO — Since its involvement in promoting international reflection on the ethics of life sciences in the 1970s, UNESCO continues to build and reinforce linkages among ethicists, scientists, policy-makers, judges, journalists, and civil society to assist Member States in enacting sound and reasoned policies on ethical issues in science and technology.
[12] Ethics in scientific research: a lens into its importance, history, and ... — Furthermore, the rise in public perception and scrutiny of scientific practices, fueled by a more informed and connected populace, demands greater transparency and ethical accountability from researchers and institutions. It buttresses the fact that ethics in scientific research is vital for maintaining the trust of the public, ensuring the safety of participants, and legitimizing scientific findings. Before a study commences, the IRB reviews the research proposal to ensure it adheres to ethical guidelines. For example, ethical guidelines in medical research emphasize the need to balance scientific advancement with patient welfare, ensuring that new treatments are both effective and safe. When the public perceives that researchers are committed to ethical standards, it reinforces their confidence in the scientific process and its outcomes.
[13] The Ethics of Technology: Balancing Innovation and Responsibility — The Ethics of Technology: Balancing Innovation and Responsibility - Telecom Review The Ethics of Technology: Balancing Innovation and Responsibility The Ethics of Technology explores topics such as algorithmic bias, data privacy, autonomous systems, genetic engineering, social media influence and the ethical implications of Big Data. By considering the below points, we can strive to strike a balance between technological innovation and the responsibility to ensure that technology is used in an ethical, fair and beneficial manner for society as a whole: By proactively addressing ethical challenges, fostering collaboration, implementing robust frameworks and promoting awareness, we can best ensure that technology continues to drive innovation while being used in a way that is ethical, fair and beneficial to society as a whole. Technology Innovation collaboration Awareness Responsibility ethical challenges
[15] Value Change and Technological Design - IEEE Technology and Society — Therefore, in recent decades, the societal impact of technology has come to the center of attention. To deal with potential ethical issues related to technology, many scholars have emphasized the importance of addressing values early on, during the design phase of new technology. ... The kinds of value change we have outlined are important for
[38] About History of Science & Science Studies - Harvard University — History of Science, Technology, and Medicine is an academic discipline of great scope and international reach that connects the sciences, social sciences, and humanities. It deals with important questions about the rise and impact of science, medicine, and technology, both east and west, and at all periods, including the very recent past.
[39] Science and Technology Studies - an overview - ScienceDirect — The constitutive idea in contrast to its precursors in the sociology, history, or economics of science and technology is an understanding that also the content of science and engineering (scientific facts, technologies, objects) is open to social analysis and not the result of a privileged form of knowledge about nature. At its basis is the claim that not only the social institution of science or the impact of technological change on society is open to social analysis but also the very content of science and engineering, i.e., scientific facts and technological objects. This article examines the research on scientific controversies, largely carried out within the field of science and technology studies (STS).
[40] History of Science - an overview | ScienceDirect Topics — The 'History of Science' refers to the discipline that studies the development and evolution of scientific knowledge and practices throughout different time periods and cultures. The developments of recent decades have expanded the scope of the history of science both chronologically (ever more studies are devoted to modern and contemporary science) and thematically (embracing the human as well as the natural sciences); shifted the emphasis from scientific theories to scientific practices (especially experiment); directed attention to the material culture of science and the embodiment of scientists; and addressed the history of supposedly transhistorical entities such as experience, truth, and objectivity.
[41] Harvard STS Program » About » What is STS? - Harvard University — Program on Science, Technology and Society at HarvardHarvard Kennedy School of Government | Harvard UniversityHomeAboutPeopleNews & EventsResearchAcademic ProgramsResourcesConnectAbout »Mission StatementWhat is STS?FellowsScience and Democracy NetworkCollaborationsContact InformationWhat is STS?Science and Technology Studies (STS) is a relatively new academic field. STS courses in these areas enable students to form more robust understandings of the nature of controversy, the causes of scientific and technological change, the relationship of culture and reason, and the limits of rational analytic methods in characterizing complex problems.In sum, STS explores in rich and compelling ways what difference it makes to human societies that we, collectively, are producers and users of science and technology.
[53] Changing Social Relations between Science and Society: Contemporary ... — Globalization and the changing social contract between science and society 7 Globalization 7 Social contract between science and society - post-war experience 8 Changing Social Contract between science and society - post-1990s 10 Public good to market good 11 Advancing knowledge to creation of wealth 11
[54] Outline of Synthesis of Cognitive and Socio-cultural Foundations of ... — Scientific knowledge in this case was viewed as the result of social construction. Significance of studies lies in the fact that, for the first time, cognitive and socio-cultural factors and foundation for scientific evolution were presented as an integral and consistent research, thus encouraging the development of micro-sociological research.
[63] (PDF) How could scientific facts be socially constructed ... — This essay examines the philosophical conflict between constructivists and rationalists regarding the nature of scientific facts. It argues for a relativistic view where scientific phenomena are constructed post hoc by scientists rather than being direct manifestations of objective reality.
[64] Social Construction of Scientific Knowledge - Encyclopedia.com — Social constructivists therefore do not recognize a sharp distinction between the production and the consumption of knowledge. Thus, social constructivism has a "democratizing" effect on epistemology by leveling traditional differences in the authority granted to differently placed knowers. ... In philosophical terms social constructivism is a
[79] Research Methodology (Methods, Approaches And Techniques) — Research methods in qualitative studies include interviews, focus groups, observations, and content analysis. The aim of qualitative research is to g ain in depth in sights in to individuals'
[80] Qualitative Research Methods in Science and Higher Education — The research possibilities of a qualitative approach have a long tradition in the social sciences and humanities, including different perspectives on theoretical foundations, research strategies
[81] Qualitative Study - StatPearls - NCBI Bookshelf — Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences
[84] PDF — Choose a comfortable setting for the interview that is free from distractions 3 Open the interview with easy questions that the interviewee can answer confidently, or even begin with friendly, off-topic conversation Explain in broad terms the goals of the research, particularly if you can frame it in terms of solving a problem that is important to the interviewee Make sure the interviewee understands the confidentiality agreement of the interview o Interviewees can be allowed to speak “off the record,” but be clear about what this means to you and the interviewee Generating trust early on can be important for acquiring interviews and making them worthwhile o Self-disclosure can be effective, such as highlighting shared experiences or goals that are shared with the interviewee o Mutual acquaintances (including previous interviewees) can generate trust o Trust is particularly important if the interview covers sensitive topics Question order is important!
[88] Mixed Methods Research: Combining both qualitative and quantitative ... — Mixed Methods Research: Combining both qualitative and quantitative approaches Mixed methods research integrates both qualitative and quantitative approaches to provide a comprehensive understanding of complex phenomena. Abstract: Mixed methods research integrates both qualitative and quantitative approaches to provide a comprehensive Keywords: Mixed Methods Research | Qualitative Approaches | Quantitative Approaches | Triangulation | Data The integration of qualitative and quantitative data enhances the applicability of research findings to real-world Data Collection in Mixed Methods Research Data Analysis in Mixed Methods Research Data Analysis in Mixed Methods Research Fei, Y., Cong, S., & Bian, B. Also, mixed method design may combine certain elements of research designs such as the research question, data collection or data analysis.
[89] Integrating Quantitative and Qualitative Results in Health Science ... — Researchers have used the mixed methods approach to examine nuanced topics, such as electronic personal health records,3 knowledge resources,4 patient-physician communication,5 and insight about intervention feasibility and implementation practices.6 Mixed methods research is the collection and analysis of both qualitative and quantitative data and its integration, drawing on the strengths of both approaches.7,8 We examined joint displays as a way to represent and facilitate integration of qualitative and quantitative data in mixed methods studies. For each article, we extracted the following information: (1) the design; (2) the study purpose; (3) the mixed methods rationale, (4) quantitative data sources; (5) qualitative data sources; (6) integration approaches used at the methods level: explaining, building, merging, and embedding; and (7) analytic strategies at the interpretation and reporting level: narrative, data transformation, and joint display.15 Individually, each author analyzed each joint display for what it uniquely communicated or represented (ie, mixed methods analysis) that is better captured visually than by words alone.
[90] Toward Integrating Qualitative and Quantitative Methods: An ... - JSTOR — Both the qualitative and quantitative paradigms have weaknesses which, to a certain extent, are compensated for by the strengths of the other. As indicated in this article, the strengths of quantitative methods are that they produce factual, reliable outcome data that are usually generalizable to some larger population. The strengths of qualitative methods are that they generate rich, detailed
[121] PDF — Work in the open, to make sure that it harnesses the 'collective intelligence' of society as a whole when addressing problems and maintains public trust. Government should set up a new body focused on involving the public in policy making more formally - with an immediate focus on conversations about government's use of digital technology.
[124] Toward best practices in embedded ethics: Suggestions for ... — Accordingly, a trend toward “embedded ethics” is seen in recent research, reflecting an increase in efforts to integrate social and ethical considerations in computer science education and early in the development phases of AI and robotics. The interdisciplinary project Responsible Robotics brings together researchers from three different departments at the Technical University of Munich, thus joining expertise in mechanical engineering and robotics, ethics, and social science. At the Institute for History and Ethics in Medicine, her work examines the integration of citizen science in biomedicine and biotechnology, ethical concerns surrounding artificial intelligence and robotics in clinical contexts, and the broader context of technology-driven changes in sharing practices, forms of scientific labor, and research organization in medicine and bioscience.
[125] Responsible Tech Frameworks: 9 Ethical Developments That Are Reshaping ... — The Tech for Good initiative highlights the potential of technology to address social challenges. This framework encourages companies to prioritize social impact alongside profitability. By focusing on the intersection of technology and social good, organizations can drive responsible tech development that benefits society. 9. The Ethical OS
[126] Implementing Ethical AI Frameworks in Industry - University of San ... — AI ethics refers to the set of moral principles and guidelines that govern the development and use of artificial intelligence technologies. Tackling these concerns requires collaboration among policymakers, developers and organizations to ensure AI technologies remain innovative and ethically sound. While internal ethical frameworks are essential for guiding AI development, external regulations play a crucial role in ensuring that AI systems adhere to universal standards of fairness, transparency and accountability. Establishing ethical AI frameworks within organizations requires a proactive and structured approach to ensure that certain principles are integrated throughout the AI development lifecycle. Organizations can establish AI ethics by developing clear ethical guidelines, training teams in responsible AI practices, conducting bias audits and regularly monitoring AI systems to ensure compliance with ethical standards.
[128] Social construction of technology (SCOT) - STSWiki — Social construction of technology (SCOT) - STSWiki In contrast to the linear model of technological innovation, which imagines a mythical, linear succession of basic science, applied science, development, and commercialization (Madhjoudi, 1997), SCOT sees a variety of groups (called relevant social groups) competing to control a design, which at this point is far from preordained (SCOT calls this the phase of interpretive flexibility). By depicting new technological artifacts as the result of a process in which several social groups each had their own idea about what “superior technology” means, SCOT radically undermines the central premise of technological determinism and, at the same time, makes a convincing case for broader public engagement in technological innovation processes.
[129] Social construction of technology (SCOT) - STSWiki — In sum, SCOT argues that technological innovation is not the result of mythical men who introduce new 'technologies' and release them into 'society,' starting a series of (un)expected impacts; rather, technological innovation is a complex process of co-construction in which technology and society, to the degree that they could even be
[137] Understanding Bias in Scientific Research and Its Impact — Understanding Bias in Scientific Research and Its Impact Understanding Bias in Scientific Research and Its Impact/ Understanding Bias in Scientific Research and Its Impact Understanding the various types of bias in research is crucial for both the integrity of scientific findings and public trust in science. Case Studies of Bias in Scientific Research Bias in scientific research presents a significant challenge to the integrity and reliability of findings. Strategies such as adopting transparent practices and promoting open data accessibility are vital steps toward minimizing bias in scientific research. Understanding how institutional pressures can shape research outcomes is critical in the context of bias within scientific disciplines. Building Public Trust: When bias is minimized in scientific research, the credibility of science flourishes.
[138] Understanding sources of bias in research | Evidence-Based Nursing — Second, bias can occur at each stage of the research process, from study design, participant selection, data collection and analysis, and the interpretation and reporting of findings. The seminal example of the consequences of bias is the controversial study that suggested a link between the measles-mumps-rubella vaccine and autism in children.2 A rare retraction of the published study occurred because of media reports that highlighted significant bias in the research process.3 Bias occurred on several levels: the process of selecting participants was misrepresented; the sample size was too small to infer firm conclusions from the analysis of the data; and the results were overstated, which suggested caution against widespread vaccination and an urgent need for further research. Examples of potential sources of bias across research processes in relation to study design, participant selection, data collection and analysis, reporting of findings and publication bias are presented in table 1.
[140] Intersectionality and Science and Technology Studies - Patrick R ... — Emerging from critical race and feminist studies, intersectionality has many shared analytic priorities with science and technology studies (STS), including an emphasis on co-emergent social forces, historical contingency, and interventions that challenge and enhance knowledge production.
[146] Case Studies - Princeton Dialogues on AI and Ethics — Case Studies – Princeton Dialogues on AI and Ethics Skip to content Princeton Dialogues on AI and Ethics Princeton University Menu Home About People Steering Committee Affiliates Past Affiliates Events News Case Studies Case Study PDFs Methodology Contact Case Studies Princeton Dialogues on AI and Ethics Case Studies The development of artificial intelligence (AI) systems and their deployment in society gives rise to ethical dilemmas and hard questions. By situating ethical considerations in terms of real-world scenarios, case studies facilitate in-depth and multi-faceted explorations of complex philosophical questions about what is right, good and feasible. Case studies provide a useful jumping-off point for considering the various moral and practical trade-offs inherent in the study of practical ethics. Case Study PDFs: The Princeton Dialogues on AI and Ethics has released six long-format case studies exploring issues at the intersection of AI, ethics and society. Methodology: The Princeton Dialogues on AI and Ethics case studies are unique in their adherence to five guiding principles: 1) empirical foundations, 2) broad accessibility, 3) interactiveness, 4) multiple viewpoints and 5) depth over brevity.
[155] Top scientific discoveries and breakthroughs for 2025 | CAS — Recent developments in these areas span diverse subjects like AI in research, precision medicine, and new battery technology. Casgevy was the first therapy to be approved by the U.S. FDA that was developed using CRISPR-Cas9 gene-editing technology, and many new CRISPR-based therapies targeting a broad range of diseases have entered drug discovery pipelines and trials since. In combination with emerging AI-based synthetic applications that are already helping chemists identify and prioritize synthetic pathways, these new synthetic approaches could drive a multi-fold increase in chemical innovation over the next decade. Investment in new single-cell analysis technologies has exploded in recent years, and these techniques are now being applied to advance critical progress in early disease detection, prenatal screening tests, biomarker testing, liquid biopsies, and biologic drug development. About CAS
[157] The Future of AI in Scientific Research:- Breakthroughs and Discoveries — By facilitating collaboration among scientists from diverse fields—such as biology, physics, and computer science—AI fosters innovation that transcends traditional boundaries.
[158] The Future of Research in an Artificial Intelligence-Driven World — Current and future developments in artificial intelligence (AI) systems have the capacity to revolutionize the research process for better or worse. On the one hand, AI systems can serve as collaborators as they help streamline and conduct our research.
[159] Navigating the inevitable: artificial intelligence and the future of ... — Artificial intelligence (AI) has become a transformative force in science and is set to become an indispensable tool owing to its vast capabilities that can perform complex methodological tasks, enhance research accessibility, and assist in scientific communication. While AI technology has been around for some time, interest exploded with the release of ChatGPT 3.5 by OpenAI in November 2022
[160] (PDF) AI in Scientific Research: Empowering Researchers with ... — Case studies illustrate successful implementations of AI in scientific inquiry, demonstrating its potential to enhance research methodologies and outcomes.
[161] Scientific discovery in the age of artificial intelligence — Advertisement View all journals Search Log in Explore content About the journal Publish with us Subscribe Sign up for alerts RSS feed nature review articles article Review Published: 02 August 2023 Scientific discovery in the age of artificial intelligence Hanchen Wang ORCID: orcid.org/0000-0002-1691-024X1,2 na1 nAff37 nAff38, Tianfan Fu3 na1, Yuanqi Du4 na1, Wenhao Gao5, Kexin Huang6, Ziming Liu7, Payal Chandak ORCID: orcid.org/0000-0003-1097-803X8, Shengchao Liu ORCID: orcid.org/0000-0003-2030-23679,10, Peter Van Katwyk ORCID: orcid.org/0000-0002-3512-066511,12, Andreea Deac9,10, Anima Anandkumar2,13, Karianne Bergen11,12, Carla P. Gomes ORCID: orcid.org/0000-0002-4441-72254, Shirley Ho14,15,16,17, Pushmeet Kohli ORCID: orcid.org/0000-0002-7466-799718, Joan Lasenby1, Jure Leskovec ORCID: orcid.org/0000-0002-5411-923X6, Tie-Yan Liu19, Arjun Manrai20, Debora Marks ORCID: orcid.org/0000-0001-9388-228121,22, Bharath Ramsundar23, Le Song24,25, Jimeng Sun26, Jian Tang9,27,28, Petar Veličković18,29, Max Welling30,31, Linfeng Zhang32,33, Connor W. Coley ORCID: orcid.org/0000-0002-8271-87235,34, Yoshua Bengio ORCID: orcid.org/0000-0002-9322-35159,10 & … Marinka Zitnik ORCID: orcid.org/0000-0001-8530-722820,22,35,36 Show authorsNature volume 620, pages 47–60 (2023)Cite this article 145k Accesses 547 Citations 750 Altmetric Metrics details Subjects Computer science Machine learning Scientific community Statistics A Publisher Correction to this article was published on 30 August 2023 This article has been updated Abstract Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.
[162] CRISPR and Personalized Genomics: The Future of Precision Medicine in ... — CRISPR and Personalized Genomics: The Future of Precision Medicine in 2025 - Omics tutorials CRISPR and Personalized Genomics: The Future of Precision Medicine in 2025 As of 2025, the fields of CRISPR and personalized genomics (often referred to as precision medicine) are advancing rapidly, driven by innovations in gene-editing technologies and their applications in tailoring medical treatments to individual genetic profiles. Researchers at Yale have developed a new CRISPR tool that allows for more seamless gene editing and better disease modeling. Precision medicine leverages genomic data to customize healthcare, and recent developments highlight its growing integration with CRISPR: See also Integration of Artificial Intelligence (AI) with CRISPR Technology Data Integration: Precision medicine relies on vast datasets (genomic, proteomic, environmental), requiring advanced AI and machine learning to interpret and apply this information effectively.
[171] An argument for pandemic risk management using a multidisciplinary One ... — We discuss the adoption of a comprehensive and interdisciplinary 'One Health' approach to pandemic risk management in Australia. A key goal of the One Health approach is to be proactive in countering threats of emerging infectious diseases and zoonoses through a recognition of the interdependence between human, animal, and environmental health.
[172] One Health: A Holistic Approach to Tackling Global Health Issues — By promoting collaboration, interdisciplinary research, and multisectoral actions, One Health has the potential to prevent zoonotic disease outbreaks, tackle antimicrobial resistance, safeguard environmental health, and ensure food safety and security.
[174] Utilizing AI in Research: A Responsible Approach for Effective Outcomes — To maximize AI benefits, researchers must adopt clear frameworks for ethical and responsible usage in scholarly environments. Artificial Intelligence (AI) is revolutionizing the academic realm, particularly in areas like writing and research.
[175] A Matrix for Selecting Responsible AI Frameworks — Process frameworks provide a blueprint for organizations implementing responsible artificial intelligence (AI), but the sheer number of frameworks, along with their loosely specified audiences, can make it difficult for organizations to select ones that meet their needs. This report presents a matrix that organizes approximately 40 public process frameworks according to their areas of focus
[176] (PDF) Artificial intelligence in writing and research: ethical ... — In many fields of AI applications, ethical considerations, including plagiarism, bias, privacy, responsibility, and transparency, need precise norms and human oversight.
[192] Science, technology, and society studies as an interdisciplinary ... — Background of the Field The interdisciplinary field of science, technology, and society studies-now widely recognized by the STS acronym -emerged from the widespread social upheavals of the 1960s and early 1970s.
[194] Science and Technology Studies - an overview - ScienceDirect — The constitutive idea in contrast to its precursors in the sociology, history, or economics of science and technology is an understanding that also the content of science and engineering (scientific facts, technologies, objects) is open to social analysis and not the result of a privileged form of knowledge about nature. At its basis is the claim that not only the social institution of science or the impact of technological change on society is open to social analysis but also the very content of science and engineering, i.e., scientific facts and technological objects. This article examines the research on scientific controversies, largely carried out within the field of science and technology studies (STS).
[195] Interdisciplinary research: putting the methods under the microscope — For example, in developing a large-scale collaboration on genomics policy among scholars from philosophy, law, management, medicine, public health sciences, social sciences, and molecular biology, we developed a research methods template shown in Figure 1, which represents a new approach to bioethics research . Funding agencies, academic institutions and journals could promote this by requesting that interdisciplinary research teams document and reflect on their collaborations, as part of their documentation of methods and in the discussion sections of papers, respectively. This group was awarded a U.S. National Science Foundation grant in 2002 to conduct a one-year pilot study of interdisciplinary research methods http://hybridvigor.net
[196] Undone Science: Charting Social Movement and Civil Society Challenges ... — A corresponding diversification in science and technology studies (STS) has led to renewed attention to the role of extrainstitutional factors such as states, industries, and social movements in the shaping of scientific research fields and technological design choices (Klein and Kleinman 2002; Frickel and Moore 2006a, 2006b). Among the changes
[198] Society, Politics, and Economics in Shaping Scientific Endeavors — The intersection of society, politics, and economics with the realm of science is a complex and multifaceted interplay that influences the trajectory of scientific endeavors. Government Funding: Political decisions influence the allocation of government funding for scientific research, shaping national scientific agendas and directing resources towards specific fields. A1: Public opinion can influence scientific research by shaping funding priorities, impacting the perception of certain fields of study, and influencing policy decisions that allocate resources to specific scientific agendas. The intricate dance between society, politics, and economics shapes the trajectory of scientific research in profound ways. The integration of societal values, political decisions, and economic structures creates a tapestry that influences research priorities, funding allocations, and the overall landscape of scientific inquiry.
[205] Science and ethics: As research and technology are changing society and ... — Science and ethics: As research and technology are changing society and the way we live, scientists can no longer claim that science is neutral but must consider the ethical and social aspects of their work - PMC As research and technology are changing society and the way we live, scientists can no longer claim that science is neutral but must consider the ethical and social aspects of their work As this document was approved following thorough consultation with all UNESCO member states and informed discussion with their respective scientific communities, it can be considered a useful reference to identify and deal with ethical problems that stem from scientific research in a general context.
[206] Experiments in engagement: Designing public engagement with science and ... — Introducing a recent special issue of this journal on "Public Engagement in Science," Stilgoe et al. (2014) reflect on the past 20 years of research and experiments in public engagement with science and technology (PEST). While they have a "normative commitment to the idea of democratic science policy" (Stilgoe et al., 2014: 5) and see public engagement as part of this, the account
[219] PDF — collaboration process. Resource Factors This categoryhighlights resources needed by scientists to support interdisciplinary science. Specific resource factors that emerged from the data analysis are: support from funding agencies, support from scientists' institutions, literature, scientific publishing, students, and time.
[221] Factors associated with disciplinary and interdisciplinary research ... — Factors associated with disciplinary and interdisciplinary research collaboration. ... p.16).' A variety of scholars have argued that interdisciplinary science has a positive influence on knowledge production and innovation (e.g. Gibbons et al ... The distinctive feature of interdisciplinary collaboration is rather that scientists bring in
[224] Understanding the assembly of interdisciplinary teams and its impact on ... — The increasing importance of interdisciplinary teams in science has prompted scholars to investigate the key factors behind effective collaboration among such team members (Fiore, 2008, Olson et al., 2008, Stokols et al., 2008).This research showed how interdisciplinary scientific teams benefit from understanding the importance of collaboration networks and proposed ways to efficiently
[240] Feminism Confronts AI: The Gender Relations of Digitalisation — Wajcman, Judy, and Erin Young, 'Feminism Confronts AI: ... or 'data extractivism', algorithms and predictive risk models could entrench existing inequalities and power dynamics (Eubanks 2018). This is about the danger of encoding—and amplifying—offline inequities into online structures, as these technologies carry over the social norms
[241] Contingency of technology and strong program of Donald MacKenzie — This article deal with the idea of contingency in technological development as it presented in works of Donald Angus MacKenzie. From his point of view, the main focus in sociological studies of technological development should be shifted from understanding it through the lenses of "natural trajectories" to grasp it as a result of social construction.
[242] MacKenzie and Wajcman (editors): The Social Shaping of Technology — In 1985, Mackenzie and Wajcman produced what was to become a seminal contribution to theorising the relationship between technology and society. The First Edition of The Social Shaping of Technology broke new ground in focussing attention on the social context of technological development, implementation and use, and questioning the overly
[243] Chapter 6 - The Social Shaping of Technology (SST) — The social shaping of technology (SST) was one of the new analytical frameworks articulated in the 1980s that sought a more effective conceptualization of the relationship between technology and society. MacKenzie and Wajcman (1985) coined the SST concept in their 1985 edited collection, The Social Shaping of Technology: How the Refrigerator
[272] Social construction of technology (SCOT) - STSWiki — Social construction of technology (SCOT) - STSWiki In contrast to the linear model of technological innovation, which imagines a mythical, linear succession of basic science, applied science, development, and commercialization (Madhjoudi, 1997), SCOT sees a variety of groups (called relevant social groups) competing to control a design, which at this point is far from preordained (SCOT calls this the phase of interpretive flexibility). By depicting new technological artifacts as the result of a process in which several social groups each had their own idea about what “superior technology” means, SCOT radically undermines the central premise of technological determinism and, at the same time, makes a convincing case for broader public engagement in technological innovation processes.
[273] 3 Social Construction of Technology - Oxford Academic — The chapter focuses on the Social Construction of Technology approach (SCOT) by Trevor Pinch and Wiebe Bijker, introducing the reader to its initial formulation (1984), and to the subsequent extensions - and sometimes reformulations - elaborated in more than 30 year of empirical research.
[275] Trend Overview 2025: Key Developments in Biomedical Science & Research — Trend Overview 2025: Key Developments in Biomedical Science & Research Trend Overview 2025: Key Developments in Biomedical Science & Research. AI and Machine Learning Transform Biomedical Research Trend Overview 2025: Key Developments in Biomedical Science & Research AI and Machine Learning Transform Biomedical Research Digital health technologies are becoming integral to biomedical science, bridging the gap between research and real-world application. Open-access platforms and data-sharing initiatives are breaking down silos, enabling researchers worldwide to pool resources and accelerate innovation. American Journal of Biomedical Science & Research (ISSN: 2642-1747) is an Open access online Journal dedicated in advancing the latest scientific knowledge of science, medicine, technology and its related disciplines.
[277] Five Trends in AI and Data Science for 2025 - MIT Sloan Management Review — Data and AI leaders in Randy’s 2025 AI & Data Leadership Executive Benchmark Survey said they are confident that GenAI value is being generated: Fifty-eight percent said that their organization has achieved exponential productivity or efficiency gains from AI, presumably mostly from generative AI. In our trend article last year, we noted that Randy’s survey found that the percentage of company respondents who said that their organization had “created a data and AI-driven organization” and “established a data and AI-driven organizational culture” both doubled over the prior year (from 24% to 48% for creating data- and AI-driven organizations, and from 21% to 43% for establishing data-driven cultures).
[278] Seven Research Topics on the emerging technologies disrupting the world ... — From AI-powered plant disease detection to the future of digital health and big data in medicine, these research communities are tackling critical worldwide challenges across diverse fields. This Research Topic explores how big data, machine, and deep learning algorithms are being applied to precision agriculture and plant health. This topic brings together researchers from diverse fields and specializations, such as plant bioinformatics, computer engineering, computer science, agricultural engineering, environmental engineering, food engineering, information technology, and mathematics. This Research Topic provides a comprehensive overview of the current trends, scientific potential, regulatory and professional challenges, and ethical and social implications of digital health and big data in medicine, including prevention, clinical care, research, management, regulation, and health policy perspectives. This Research Topic explores advanced AI methods for plant disease and pest recognition for real-world applications.
[279] Top scientific discoveries and breakthroughs for 2025 | CAS — Recent developments in these areas span diverse subjects like AI in research, precision medicine, and new battery technology. Casgevy was the first therapy to be approved by the U.S. FDA that was developed using CRISPR-Cas9 gene-editing technology, and many new CRISPR-based therapies targeting a broad range of diseases have entered drug discovery pipelines and trials since. In combination with emerging AI-based synthetic applications that are already helping chemists identify and prioritize synthetic pathways, these new synthetic approaches could drive a multi-fold increase in chemical innovation over the next decade. Investment in new single-cell analysis technologies has exploded in recent years, and these techniques are now being applied to advance critical progress in early disease detection, prenatal screening tests, biomarker testing, liquid biopsies, and biologic drug development. About CAS
[281] The role of artificial intelligence in generating original scientific ... — Artificial intelligence (AI) is a ground-breaking technology that is driving advancements in both technology and society in many fields (Briganti and Le Moine, 2020, Palagi and Fischer, 2018, Wang et al., 2022b, Wang et al., 2023b).Its primary goal is to mimic human intelligence and, as a result, to carry out human tasks (Xu et al., 2021), but at a much faster pace than humans can achieve.
[282] PDF — The Royal Society's Disruptive Technology for Research project aims to understand the landscape of data-driven and artificial intelligence-based technologies (AI) across different fields of scientific research. This document first provides a literature review of AI use in Science, Technology, Engineering and Medicine (STEM).
[283] Scientific discovery in the age of artificial intelligence — Advertisement View all journals Search Log in Explore content About the journal Publish with us Subscribe Sign up for alerts RSS feed nature review articles article Review Published: 02 August 2023 Scientific discovery in the age of artificial intelligence Hanchen Wang ORCID: orcid.org/0000-0002-1691-024X1,2 na1 nAff37 nAff38, Tianfan Fu3 na1, Yuanqi Du4 na1, Wenhao Gao5, Kexin Huang6, Ziming Liu7, Payal Chandak ORCID: orcid.org/0000-0003-1097-803X8, Shengchao Liu ORCID: orcid.org/0000-0003-2030-23679,10, Peter Van Katwyk ORCID: orcid.org/0000-0002-3512-066511,12, Andreea Deac9,10, Anima Anandkumar2,13, Karianne Bergen11,12, Carla P. Gomes ORCID: orcid.org/0000-0002-4441-72254, Shirley Ho14,15,16,17, Pushmeet Kohli ORCID: orcid.org/0000-0002-7466-799718, Joan Lasenby1, Jure Leskovec ORCID: orcid.org/0000-0002-5411-923X6, Tie-Yan Liu19, Arjun Manrai20, Debora Marks ORCID: orcid.org/0000-0001-9388-228121,22, Bharath Ramsundar23, Le Song24,25, Jimeng Sun26, Jian Tang9,27,28, Petar Veličković18,29, Max Welling30,31, Linfeng Zhang32,33, Connor W. Coley ORCID: orcid.org/0000-0002-8271-87235,34, Yoshua Bengio ORCID: orcid.org/0000-0002-9322-35159,10 & … Marinka Zitnik ORCID: orcid.org/0000-0001-8530-722820,22,35,36 Show authorsNature volume 620, pages 47–60 (2023)Cite this article 145k Accesses 547 Citations 750 Altmetric Metrics details Subjects Computer science Machine learning Scientific community Statistics A Publisher Correction to this article was published on 30 August 2023 This article has been updated Abstract Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.
[284] Artificial Intelligence: Ethical Considerations In Academia — Here, we take a closer look at LLMs and the ethical considerations of AI in academia. LLM AI models—such as ChatGPT—use natural language processing to analyse and learn patterns in human-created texts. There is a lot of inherent bias in the datasets AI is trained on; being written by humans, many texts will be opinionated, unfair, or misinformed. With humans being unable to review these processes in place of AI, it is impossible to determine if conclusions have been drawn fairly or whether biased, outdated, or problematic material informed the final decision. MDPI is committed to research integrity and the ethical use of AI and AI-assisted technology in manuscript preparation. To learn more about Artificial Intelligence, take a look at the research of AI and previous blog articles.
[285] Interdisciplinary Collaboration | Temple University — In April 2023, the Interdisciplinary Collaboration Committee was charged with identifying practical, financial, historical and administrative barriers to interdisciplinary collaboration at Temple University and provide recommendations to enrich and expand interdisciplinary opportunities and collaboration at the faculty, college, and University levels in research and academic programs. In
[286] Interdisciplinary collaboration from diverse science teams can produce ... — Loading metrics
[287] Full article: Enhancing research collaboration within a large ... — In doing so, the study identified four key themes surrounding research collaboration: (1) high teaching workloads with limited time to dedicate to research; (2) a research culture that discourages cross fertilisation; (3) unfamiliarity in research and funding processes; and (4) the value of technology in enhancing research collaboration.
[288] Research Proposal: Title: The Impact of Emerging Technologies on Policy ... — Expected Outcomes: A deeper understanding of how emerging technologies shape policy development, implementation, and governance in contemporary society. Comprehensive insights into the benefits, risks, and ethical implications associated with emerging technologies and their impact on policy formulation.
[304] The impact of big data on research methods in information science — The impact of big data on research methods in information science - ScienceDirect The impact of big data on research methods in information science Emerging big data trends inevitably have an impact on research methods in information science. The authors of this paper discuss the impact of big data on research methods in information science. This paper addresses these challenges and opportunities through the lens of research methods, ranging from data processing, to sampling, to information visualization, to temporal analysis, to sentiment analysis, to correlation, to cause-effect relationship, to data accessibility, to data privacy, and data ethics issues.