Applying Reflexivity to Artificial Intelligence for Researching Marginalized Communities and Real-World Problems

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dc.contributor.authorAguilar, Nathan
dc.contributor.authorLandau, Aviv
dc.contributor.authorMathiyazhagan, Siva
dc.contributor.authorAuyeung, Alex
dc.contributor.authorDillard, Sarah
dc.contributor.authorPatton, Desmond
dc.date.accessioned2022-12-27T18:55:02Z
dc.date.available2022-12-27T18:55:02Z
dc.date.issued2023-01-03
dc.description.abstractDespite 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.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.090
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.other0e99dc86-a5d9-4cd5-8825-9656618c92dd
dc.identifier.urihttps://hdl.handle.net/10125/102719
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAccountability, Evaluation, and Obscurity of AI Algorithms
dc.subjectaccountability
dc.subjectartificial intelligence
dc.subjectfairness
dc.subjectprivacy
dc.subjectreflexivity
dc.subjectsocial work
dc.titleApplying Reflexivity to Artificial Intelligence for Researching Marginalized Communities and Real-World Problems
dc.type.dcmitext
prism.startingpage712

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Accountability, Evaluation, and Obscurity of AI Algorithms