Toward ethical, transparent and fair AI/ML: a critical reading list

**stopped maintaining in 2019

In the past 5 years there’s been a lot of enthusiasm about AI and specifically machine learning and deep learning. As we continuously deploy AI models in the wild we are forced to re-examine what are the effects of knowledge symbolisation, generalisation and classification on the historical, political and social conditions of human life. We also need to remind ourselves that algorithms don’t exercise their power over us. People do.

This reading list is made for engineers, scientists, designers, policy makers and those interested in machine learning and AI. It’s an open ended document that examines machine learning as a sociotechnical system and contextualises its critical discourse. For suggestions and comments please tweet @irinimalliaraki or drop me an email at e.malliaraki16@imperial.ac.uk

These sections aren’t in any particular order. There’s overlap and interaction between these topics that you can jump around as much as you want; Reading “out of order” could lead to interesting connections.

CRITICAL AI

Must

Optional

AI ACCOUNTABILITY & GOVERNANCE

Must

Optional

AI TRANSPARENCY, EXPLAINABILITY & BIAS

Bias

Must

Optional

Transparency and Explainability

Must

Optional

AI FAIRNESS

Must

Optional

AI ETHICS

Must

Optional

AI and LABOUR

Automation and inequality

Must

Optional

Discrimination

AI and SOCIAL IMPACT

Must

Optional

AI POLICY & LAW

AI AND DESIGN

Must

Optional

AI AUDITING & SECURITY

Must

Optional

PEOPLE & ORGANISATIONS

There are plenty of research groups and initiatives both in academia and in the industry start thinking about the relevance of ethics and safety in AI:

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