References (WIP)

General AI Ethics Books

Various authors. Markus D. Dubber (ed.), Frank Pasquale (ed.), Sunit Das (ed.). The Oxford Handbook of Ethics of AI. (Oxford University Press, 2020)

L. Floridi. The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities (Oxford University Press, 2023)

M. Coeckelberg. AI Ethics. (MIT Press, 2020)

Leslie, D. Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector. (The Alan Turing Institute, 2019).

UNESCO. Recommendation on the Ethics of Artificial Intelligence. United Nations. 2022.

Human Dignity

Jaron Lanier Fixes the Internet – NYT Opinion

Data Dignity and the Inversion of AI – Jaron Lanier

Jaron Lanier. Who owns the future? Penguin Books, 2013. Princeton

Eric A. Posner Glen Weyl. Radical Markets: Uprooting Capitalism and Democracy for a Just Society.

Bias & Fairness

Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, Aram Galstyan. A Survey on Bias and Fairness in Machine Learning. 2023. ACM Surveys.

Pedro Saleiro, Benedict Kuester, Loren Hinkson, Jesse London, Abby Stevens, Ari Anisfeld, Kit T. Rodolfa, Rayid Ghani. Aequitas: A Bias and Fairness Audit Toolkit. ArXiv.

Jeff Larson, Surya Mattu, Lauren Kirchner and Julia Angwin. How We Analyzed the COMPAS Recidivism Algorithm. May23, 2016

Interpretability / Explainability / Explicability

Serg Masis. Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples. Editorial Packt Publishing.

Branka Hadji Misheva, Joerg Osterrieder, Ali Hirsa, Onkar Kulkarni, Stephen Fung Lin. Explainable AI in Credit Risk Management. ArXiv

Christoph Molnar.  Interpretable Machine Learning. A Guide for Making Black Box Models Explainable.

Human-centered AI

Human-centered artificial intelligence (AI) focuses on designing AI systems that prioritize human experience, ensuring they are ethical, inclusive, and accountable. This approach aims to make AI technology work well for people by creating systems that are intuitive, accessible, and beneficial for all users, not just experts. Examples include user-friendly interfaces, natural language interactions, inclusive design principles, and ethical safeguards to prevent biases and protect privacy.

Human-Centered AI – Maximize AI With People | SS&C Blue Prism

Auernhammer, J. (2020) Human-centered AI: The role of Human-centered Design Research in the development of AI, in Synergy – DRS International Conference 2020

Mohammad Tahaei, Marios Constantinides, Daniele Quercia, and Michael Muller. 2023. A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI. Schmager, S; Pappas, I; and Vassilakopoulou, P., “Defining Human-Centered AI: A Comprehensive Review of HCAI Literature” (2023). MCIS 2023 Proceedings.