Human Feedback Analysis

Human Feedback Analysis is a scholarly methodological framework within Human-Centered Data Analysis that systematically captures, interprets, and integrates qualitative and quantitative input from human users to refine data models, processes, or systems, distinguishing itself by its focus on iterative human-in-the-loop validation and improvement.

44

Publications

6.6K

Citations

287

Authors

62

Institutions

Publications per year

2017–2026

33

Authors

287

  1. AA
    Amanda Askell

    OpenAI (United States), United States

    5 publications · 4.6K citations

    5

    h-index

  2. DA
    Dario Amodei

    Stanford University, United States

    4 publications · 341 citations

    4

    h-index

  3. SM
    Sam McCandlish

    Johns Hopkins University, United States

    4 publications · 341 citations

    4

    h-index

  4. SK
    Saurav Kadavath

    University of California, Berkeley, United States

    4 publications · 341 citations

    4

    h-index

  5. EP
    Ethan Perez

    Rice University, United States

    4 publications · 341 citations

    4

    h-index

1–5 of 287

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 4.8K citations

    10

    h-index

  2. Nature

    1 publications · 157 citations

    1

    h-index

  3. Regulatory Toxicology and Pharmacology

    RTP · 1 publications · 48 citations

    1

    h-index

  4. Reliability Engineering & System Safety

    1 publications · 33 citations

    1

    h-index

  5. IUI Workshops

    1 publications · 19 citations

    1

    h-index

1–5