Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

TLDR

Machine‑learning and AI systems increasingly drive everyday decisions, yet they are often opaque black boxes that compromise interpretability, fairness, accuracy, accountability, and transparency. This paper proposes guidelines and documents to address these ethical concerns, focusing on AI applications in criminal‑justice risk assessment and autonomous vehicles. The authors outline potential governance strategies to implement ethical oversight of AI systems.

Abstract

Abstract Decision-making on numerous aspects of our daily lives is being outsourced to machine-learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. ML approaches—one of the typologies of algorithms underpinning artificial intelligence—are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AI-driven decision-making are outlined: (a) risk assessment in the criminal justice system, and (b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined.