Applying Reflexivity to Artificial Intelligence for Researching Marginalized Communities and Real-World Problems
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Date
2023-01-03
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712
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Abstract
Despite advances in artificial intelligence (AI), ethical principles have been overlooked, harming marginalized communities. These flaws are due to a lack of critical insight into the complex positionality of the researcher, power dynamics between scholars and the communities being studied, and the structural impact on real-world problems when AI systems appear to be accurate but ethically fail. Reflexivity is a process that yields a better understanding of community-specific nuances, areas requiring local expertise, and the potential consequences of scholastic interventions for real-world problems (i.e., social, environmental, or socioeconomic). The paper builds on the five stages of social work reflexivity that can be applied to AI researchers and provided questions that can be asked in order to increase privacy, accountability, and fairness. We discuss the effective implementation of reflexivity in research, detail the stages of social work reflexivity and highlight key questions for AI researchers to ask throughout the research process.
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Accountability, Evaluation, and Obscurity of AI Algorithms, accountability, artificial intelligence, fairness, privacy, reflexivity, social work
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10
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Proceedings of the 56th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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