Facial Recognition Technology A Survey of Policy and Implementation Issues

By Lucas D. Introna, Helen Nissenbaum. 2009

93 citations recorded by the source corpus.

Subfields: Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety, Robustness & Interpretability

1. Hanna M. Wallach
2. Jennifer Wortman Vaughan
3. Meredith Ringel Morris
4. Michael K. Reiter
5. Ece Kamar
6. Anhong Guo
7. Solon Barocas
8. Mahmood Sharif

Citations in the corpus

• Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs (2021, background): “This ensures that the resulting dataset reflects the system’s intended use cases and deployment contexts, as well as capturing some of the sociotechnical dynamics and their effects on system performance [43, 68].”
• Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition (2016, background): “This threshold strikes a good balance between usability and security [18]; by using it, false acceptance (of zero-effort im-4 postors) never occurs, while true acceptance remains high.”
• Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition (2018, background): “Finally, to maintain a high level of security, it is important to minimize the DNN’s false positives [27].”