Webinar: Addressing the gender bias in artificial intelligence data

29 March 2021, 14:00 – 15:00 (Paris)

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As part of the March on Gender, the OECD Directorate for Science, Technology and Innovation and the International Transport Forum (ITF) held a virtual event on gender bias in artificial intelligence (AI) data (March 29, 2021).

📹 👀 Watch the recorded version on Youtube:

This virtual event brought together technical and policy experts to address questions around gender bias in AI, such as:

  • What are the best ways to combat gender bias in AI training data?

  • How can data governance help to overcome challenges related to gender bias in data and other gender-related AI concerns?

  • How can AI help to identify and eliminate gender bias?

  • How can implementing the OECD AI Principles help to address gender bias?

Moderator: Audrey Plonk, Head of Digital Economy Policy Division, Directorate for Science, Technology and Innovation, OECD

Opening remarks by:

Mr. Ulrik Vestergaard Knudsen, Deputy Secretary-General, OECD

Dr. Young Tae Kim, Secretary-General of the International Transport Forum

Panel discussion and live Q&A with:

Dr. Antonella Santuccione Chadha, Co-founder and CEO of the Women’s Brain Project (WBP), and Head of Stakeholder Engagement at Biogen

Mr. Lee Glazier, Head of Service Integrity, Civil Aerospace, Rolls-Royce Plc

Dr. Aleksandra Mojsilović, IBM Fellow, Head of AI Foundations at IBM Research, and Co-Director of IBM Science for Social Good

Ms. Cristina Pombo, Principal Advisor and Head of the Digital and Data Cluster, Social Sector, Inter-American Development Bank

Dr. Jeni Tennison, Vice President and Chief Strategy Adviser, Open Data Institute and Co-Chair, Working Group on Data Governance – The Global Partnership on AI (GPAI)

Data is the lifeblood of the digital economy and artificial intelligence, so if data processes yield data that are non-representative then the resulting algorithms will almost certainly have biased outcomes. Algorithms that do not take pre-existing gender-based inequalities into account will also be biased.

While AI systems run the risk of perpetuating or even exacerbating gender-based inequalities, AI can also help to identify and correct human bias.

Background documents

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