Ai Explainability Strategy

An "AI Explainability Strategy" is a structured methodological framework within artificial intelligence research that systematically enables the interpretation and justification of AI model behaviors, decisions, or predictions by specifying mechanisms, representations, or processes that enhance transparency, accountability, and trustworthiness for human stakeholders.

63

Publications

15.1K

Citations

269

Authors

145

Institutions

Publications per year

2017–2026

63

Authors

269

  1. ND
    Natalia Díaz-Rodríguez

    Universidad de Granada, Spain

    3 publications · 10.6K citations

    3

    h-index

  2. JD
    Javier Del Ser

    University of the Basque Country, Spain

    3 publications · 10.6K citations

    3

    h-index

  3. FH
    Francisco Herrera

    Universidad de Granada, Spain

    3 publications · 10.6K citations

    3

    h-index

  4. SG
    Salvador García

    Universidad de Granada, Spain

    2 publications · 9.1K citations

    2

    h-index

  5. SG
    Sergio Gil-López

    Instituto de Astrofísica de Andalucía, Spain

    2 publications · 9.1K citations

    2

    h-index

1–5 of 269

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 352 citations

    5

    h-index

  2. Technological Forecasting and Social Change

    2 publications · 101 citations

    2

    h-index

  3. Information Fusion

    2 publications · 10.5K citations

    2

    h-index

  4. Finance research letters

    2 publications · 240 citations

    2

    h-index

  5. Discover Artificial Intelligence

    2 publications · 59 citations

    2

    h-index

1–5