How do Practitioners Define and Design for Privacy When Developing Consumer AI Technologies?
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Yang, Stephanie
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Abstract
As artificial intelligence (AI) is swiftly advancing many facets of technology, it is more pertinent than ever to consider how these systems are designed for end user privacy. Prior research reveals that there are areas for AI practitioners to improve privacy considerations when designing AI systems, but lack clarity in how these changes could be implemented. We looked to reveal exactly where the gap between research and practice is for AI practitioners and privacy. By conducting semi-structured interviews, we revealed that this gap often stems from a lack of a centralized privacy definition. While practitioners may have felt personally motivated to uphold privacy, the lack of privacy education and compliance-centered rigidity made it difficult to do so. Moving forward, we believe that informed, up-to-date policy could be a good path to ensure privacy is upheld in all AI products.
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Undergraduate Research Option Thesis