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.
tinyletter.com/debias-ai
For worksheets, freebies and updates. Infrequent emails, important updates only.
ethical-litmus.site
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.
fairXiv.org
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.
Debias AI brought to you by @summerscope