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Privacy is a key principle for developing ethical AI technologies, but how does including AI technologies in products and services change privacy risks? We constructed a taxonomy of AI privacy risks by an- alyzing 321 documented AI privacy incidents. We codifed how the unique capabilities and requirements of AI technologies described in those incidents generated new privacy risks, exacerbated known ones, or otherwise did not meaningfully alter the risk. We present 12 high-level privacy risks that AI technologies either newly created (e.g., exposure risks from deepfake pornography) or exacerbated (e.g., surveillance risks from collecting training data). One upshot of our work is that incorporating AI technologies into a product can alter the privacy risks it entails. Yet, current approaches to privacy-preserving AI/ML (e.g., federated learning, diferential pri- vacy, checklists) only address a subset of the privacy risks arising from the capabilities and data requirements of AI.
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“I Don’t Know If We’re Doing Good. I Don’t Know If We’re Doing Bad”: Investigating How Practitioners Scope, Motivate, and Conduct Privacy Work When Developing AI Products
How do practitioners who develop consumer AI products scope, motivate, and conduct privacy work? Respecting pri- vacy is a key principle for developing ethical, human-centered AI systems, but we cannot hope to better support practitioners without answers to that question. We interviewed 35 industry AI practitioners to bridge that gap. We found that practitioners viewed privacy as actions taken against pre-defined intrusions that can be exacerbated by the capabilities and requirements of AI, but few were aware of AI-specific privacy intrusions documented in prior literature. We found that their privacy work was rigidly defined and situated, guided by compliance with privacy regulations and policies, and generally demoti- vated beyond meeting minimum requirements. Finally, we found that the methods, tools, and resources they used in their privacy work generally did not help address the unique pri- vacy risks introduced or exacerbated by their use of AI in their products. Collectively, these findings reveal the need and opportunity to create tools, resources, and support structures to improve practitioners’ awareness of AI-specific privacy risks, motivations to do AI privacy work, and ability to ad- dress privacy harms introduced or exacerbated by their use of AI in consumer products.
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- Award ID(s):
- 2316768
- PAR ID:
- 10543400
- Publisher / Repository:
- USENIX Security
- Date Published:
- ISBN:
- 978-1-939133-44-1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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There is a critical need for community engagement in the process of adopting artificial intelligence (AI) technologies in public health. Public health practitioners and researchers have historically innovated in areas like vaccination and sanitation but have been slower in adopting emerging technologies such as generative AI. However, with increasingly complex funding, programming, and research requirements, the field now faces a pivotal moment to enhance its agility and responsiveness to evolving health challenges. Participatory methods and community engagement are key components of many current public health programs and research. The field of public health is well positioned to ensure community engagement is part of AI technologies applied to population health issues. Without such engagement, the adoption of these technologies in public health may exclude significant portions of the population, particularly those with the fewest resources, with the potential to exacerbate health inequities. Risks to privacy and perpetuation of bias are more likely to be avoided if AI technologies in public health are designed with knowledge of community engagement, existing health disparities, and strategies for improving equity. This viewpoint proposes a multifaceted approach to ensure safer and more effective integration of AI in public health with the following call to action: (1) include the basics of AI technology in public health training and professional development; (2) use a community engagement approach to co-design AI technologies in public health; and (3) introduce governance and best practice mechanisms that can guide the use of AI in public health to prevent or mitigate potential harms. These actions will support the application of AI to varied public health domains through a framework for more transparent, responsive, and equitable use of this evolving technology, augmenting the work of public health practitioners and researchers to improve health outcomes while minimizing risks and unintended consequences.more » « less
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