Research scientist Timnit Gebru discusses trends in fairness and AI ethics, highlighting the diversification of NeurIPS with groups like Black in AI. They explore the evolution of ethics and fairness in AI, balancing democratization and complexity in AI tools, and the debate on whether fairness work should intersect with activism and diversity efforts.
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Quick takeaways
Shift from band-aid solutions to evaluating technical impact on fairness and ethics.
Inclusivity in AI communities and growth of affinity organizations like 'Black in AI'.
Deep dives
Advances in Fairness and Ethics in AI
The past year has seen significant progress in the conversation around fairness and ethics in AI. Discussions have shifted towards evaluating the impact of technical approaches on fairness and ethics, rather than merely applying band-aid solutions. Notable papers like 'Delayed Impacts of Fairness' and 'Lipstick on a Pig' highlight the limitations of current debiasing methods. There is a growing emphasis on the socio-technical nature of addressing fairness challenges, with a focus on systemic issues and the intersectionality of factors affecting bias.
Diversity and Inclusion in AI Communities
The podcast episode underlines the importance of inclusivity in AI communities, particularly highlighting the growth of affinity organizations like 'Black in AI' and the increasing diversity at conferences like NeurIPS. Initiatives such as data consortiums and partnerships with underrepresented communities are crucial in ensuring a holistic approach to addressing fairness and bias.
Practical Implementations of Fairness Solutions
Commercial and open-source entities are releasing tools like IBM's 'Fairness 360' and Google's 'Facets' to operationalize fairness assessments in AI models. The proliferation of such toolkits aims to make fairness more tangible and accessible to practitioners, but caution remains against viewing them as standalone solutions. A balance between simplifying access and acknowledging the inherent complexity of bias mitigation is essential.
Future Predictions and Challenges
Forecasts for 2020 and beyond in the AI ethics landscape include discussions on governance standards, increased focus on data consortiums, and enforcement of ethics guidelines. The conversation is evolving towards supporting marginalized voices, but there's a growing tension between theoretical work in fairness and activism. Ensuring interdisciplinary collaboration and addressing systemic biases within AI research communities are crucial for meaningful progress in the field.
Today we keep the 2019 AI Rewind series rolling with friend-of-the-show Timnit Gebru, a research scientist on the Ethical AI team at Google. A few weeks ago at NeurIPS, Timnit joined us to discuss the ethics and fairness landscape in 2019. In our conversation, we discuss diversification of NeurIPS, with groups like Black in AI, WiML and others taking huge steps forward, trends in the fairness community, quite a few papers, and much more.
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