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Affinity Workshop: Queer in AI Workshop 1

Rebuilding Trust: Queer in AI Approach to Artificial Intelligence Risk Management

William Agnew · Arjun Subramonian · Juan Pajaro Velasquez · Ashwin S


AI, machine learning, and data science methods are already pervasive in our society and technology, affecting all of our lives in many subtle ways. Trustworthy AI has become an important topic because trust in AI systems and their creators has been lost, or was never present in the first place. Researchers, corporations, and governments have long and painful histories of excluding marginalized groups from technology development, deployment, and oversight. As a direct result of this exclusion, these technologies have long histories of being less useful or even harmful to minoritized groups. This infuriating history illustrates that industry cannot be trusted to self-regulate and why trust in commercial AI systems and development has been lost. We argue that any AI development, deployment, and monitoring framework that aspires to trust must incorporate both feminist, non-exploitative participatory design principles and strong, outside, and continual monitoring and testing. We additionally explain the importance of considering aspects of trustworthiness beyond just transparency, fairness, and accountability, specifically, to consider justice and shifting power to the people and disempowered as core values to any trustworthy AI system. Creating trustworthy AI starts by funding, supporting, and empowering groups like Queer in AI so the field of AI has the diversity and inclusion to credibly and effectively develop trustworthy AI. Through our years of work and advocacy, we have developed expert knowledge around questions of if and how gender, sexuality, and other aspects of identity should be used in AI systems and how harms along these lines should be mitigated. Based on this, we discuss a gendered approach to AI, and further propose a queer epistemology and analyze the benefits it can bring to AI.

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