Debias AI

Set the standards by writing the standards

The scale and pattern-matching tendencies of machine learning technologies are automating and amplifying existing structural inequality.

Technologists: it's time to interrogate how we got to this point and where we want to go next. We need tools for introspection, risk assessment and harm mitigation. We need to do this work as individuals, as businesses, and as an industry, and we need to be ready to be held accountable. Arm yourself with tools to assess, quantify, and deploy fairer technology for a fairer tomorrow.

Sweet Summer Child Score — risk assessment quiz for automation and machine learning systems.
Ethics Litmus Tests — available for free PDF download or pre-order.

Mapping Fair ML
A map of resources, activities, worksheets, approaches, and groups working on fair ML and data ethics.
Discover academic papers on fair and ethical machine learning.

Fair ML Reading Group
A multi-disciplinary group reading papers on the topic of fairness and ethics in Machine Learning and Data Science. Based in Naarm / Melbourne, Australia.


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