(Unfair) Norms in Fairness Research: A Meta-Analysis
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909266663702528 |
|---|---|
| author | Chien, Jennifer Bergman, A. Stevie McKee, Kevin R. Tomasev, Nenad Prabhakaran, Vinodkumar Qadri, Rida Marchal, Nahema Isaac, William |
| author_facet | Chien, Jennifer Bergman, A. Stevie McKee, Kevin R. Tomasev, Nenad Prabhakaran, Vinodkumar Qadri, Rida Marchal, Nahema Isaac, William |
| contents | Algorithmic fairness has emerged as a critical concern in artificial intelligence (AI) research. However, the development of fair AI systems is not an objective process. Fairness is an inherently subjective concept, shaped by the values, experiences, and identities of those involved in research and development. To better understand the norms and values embedded in current fairness research, we conduct a meta-analysis of algorithmic fairness papers from two leading conferences on AI fairness and ethics, AIES and FAccT, covering a final sample of 139 papers over the period from 2018 to 2022. Our investigation reveals two concerning trends: first, a US-centric perspective dominates throughout fairness research; and second, fairness studies exhibit a widespread reliance on binary codifications of human identity (e.g., "Black/White", "male/female"). These findings highlight how current research often overlooks the complexities of identity and lived experiences, ultimately failing to represent diverse global contexts when defining algorithmic bias and fairness. We discuss the limitations of these research design choices and offer recommendations for fostering more inclusive and representative approaches to fairness in AI systems, urging a paradigm shift that embraces nuanced, global understandings of human identity and values. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16895 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | (Unfair) Norms in Fairness Research: A Meta-Analysis Chien, Jennifer Bergman, A. Stevie McKee, Kevin R. Tomasev, Nenad Prabhakaran, Vinodkumar Qadri, Rida Marchal, Nahema Isaac, William Computers and Society Artificial Intelligence Algorithmic fairness has emerged as a critical concern in artificial intelligence (AI) research. However, the development of fair AI systems is not an objective process. Fairness is an inherently subjective concept, shaped by the values, experiences, and identities of those involved in research and development. To better understand the norms and values embedded in current fairness research, we conduct a meta-analysis of algorithmic fairness papers from two leading conferences on AI fairness and ethics, AIES and FAccT, covering a final sample of 139 papers over the period from 2018 to 2022. Our investigation reveals two concerning trends: first, a US-centric perspective dominates throughout fairness research; and second, fairness studies exhibit a widespread reliance on binary codifications of human identity (e.g., "Black/White", "male/female"). These findings highlight how current research often overlooks the complexities of identity and lived experiences, ultimately failing to represent diverse global contexts when defining algorithmic bias and fairness. We discuss the limitations of these research design choices and offer recommendations for fostering more inclusive and representative approaches to fairness in AI systems, urging a paradigm shift that embraces nuanced, global understandings of human identity and values. |
| title | (Unfair) Norms in Fairness Research: A Meta-Analysis |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2407.16895 |