Algorithmic Fairness from a Sociotechnical Lens

  • Speaker
  • William Isaac, Ph.D.Research Scientist, DeepMind
Date & Time


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Books, films and television increasingly portray artificial intelligence as poised to reshape modern society. Yet many real-world AI systems deployed so far have manifested salient societal risks with the potential for acute harms to historically marginalized peoples. In response, various stakeholders have sought to propose ethical guidelines and technical mitigations to serve as ethical guardrails for future technology deployment and development.

In this talk, William Isaac will propose that in order for ethical frameworks and technical mitigations to be effective in realigning technology for socially beneficial outcomes, social and historical insights must be meaningfully taken into account. Using examples from multiple domains in the context of algorithmic fairness, he will discuss how critical evaluations which blend insight from data science and the social sciences can lead to a dynamic sociotechnical “lens” to mitigate societal harms and identify socially beneficial applications. He will conclude the lecture by discussing potential challenges to adopting a sociotechnical lens in practice and highlight open questions for future research.

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About the Speaker

Isaac is a staff research scientist at DeepMind, an advisory board member of the Human Rights Data Analysis Group, and a research affiliate at Oxford University Centre for the Governance of AI. His research focuses on the societal impact and governance of emerging technologies.

Before DeepMind, William served as an Open Society Foundations fellow. His research has been featured in publications such as ScienceThe New York Times and the MIT Technology Review.

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