What is the point of fairness?

By Cynthia L. Bennett, Os Keyes. 2019

78 citations recorded by the source corpus.

Work integrating conversations around AI and Disability is vital and valued, particularly when done through a lens of fairness. Yet at the same time, analysing the ethical implications of AI for disabled people solely through the lens of a singular idea of "fairness" risks reinforcing existing power dynamics, either through reinforcing the position of existing medical gatekeepers, or promoting tools and techniques that benefit otherwise-privileged disabled people while harming those who are rendered outliers in multiple ways. In this paper we present two case studies from within computer vision - a subdiscipline of AI focused on training algorithms that can "see" - of technologies putatively intended to help disabled people but, through failures to consider structural injustices in their design, are likely to result in harms not addressed by a "fairness" framing of ethics. Drawing on disability studies and critical data science, we call on researchers into AI ethics and disability to move beyond simplistic notions of fairness, and towards notions of justice.

Subfields: Cognitive Neuroscience, Safety Research, Philosophy

How researchers see this paper

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

In Cognitive Neuroscience

1. L. Elisa Celis
2. Meredith Ringel Morris
3. Hal Daumé Iii
4. Hanna M. Wallach
5. Su Lin Blodgett
6. Vijay Keswani

In Safety Research

1. Hanna M. Wallach
2. Su Lin Blodgett
3. L. Elisa Celis
4. Hal Daumé Iii
5. Ece Kamar
6. Solon Barocas
7. Vijay Keswani
8. Adrian Weller

In Philosophy

1. L. Elisa Celis
2. Hal Daumé Iii
3. Hanna M. Wallach
4. Solon Barocas
5. Vijay Keswani

Citations in the corpus

• Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “whether they should be built at all, and (b) keeping development and deployment decisions in the hands of technologists (Bennett and Keyes, 2019; Cifor et al., 2019; Green, 2019; Katell et al., 2020).”
• Designing an Online Infrastructure for Collecting AI Data From People With Disabilities (2021, background): “These calls-to-action encouraged inspecting whether AI applications are trained on datasets that represented PWD, whether they had reasonable accuracy when used by PWD, and what sorts of societal biases they can incur against PWD and other vulnerable groups [6, 17, 24, 28, 52, 57].”
• Implicit Diversity in Image Summarization (2019, background): “Uninformed application of any technology that aims to ensure fairness can inadvertently cause more harm than good [9, 54, 89].”