Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks

Fig. 1. We identify 12 privacy risks that the unique capabilities and/or requirements of AI can entail. For example, the capabilities of AI create new risks (purple) of identification, distortion, physiognomy, and unwanted disclosure; the data requirements of AI can exacerbate risks (light blue) of surveillance, exclusion, secondary use, and data breaches owing to insecurity.

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 analyzing 321 documented AI privacy incidents. We codified 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, differential privacy, checklists) only address a subset of the privacy risks arising from the capabilities and data requirements of AI.

CCS Concepts: • Security and privacy → Human and societal aspects of security and privacy; • Human-centered computing → Human computer interaction (HCI).