Inioluwa Deborah Raji
Orcid: 0000-0002-9510-3015Affiliations:
- Mozilla Foundation, USA
According to our database1,
Inioluwa Deborah Raji
authored at least 23 papers
between 2019 and 2024.
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Bibliography
2024
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling.
CoRR, 2024
2023
Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem.
CoRR, 2023
Actionable Auditing Revisited: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products.
Commun. ACM, 2023
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, 2023
2022
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022
Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022
Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022
From Algorithmic Audits to Actual Accountability: Overcoming Practical Roadblocks on the Path to Meaningful Audit Interventions for AI Governance.
Proceedings of the AIES '22: AAAI/ACM Conference on AI, Ethics, and Society, Oxford, United Kingdom, May 19, 2022
2021
Data and its (dis)contents: A survey of dataset development and use in machine learning research.
Patterns, 2021
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021
Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021
2020
Patterns, 2020
The Discomfort of Death Counts: Mourning through the Distorted Lens of Reported COVID-19 Death Data.
Patterns, 2020
Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing.
Proceedings of the FAT* '20: Conference on Fairness, 2020
Proceedings of the AIES '20: AAAI/ACM Conference on AI, 2020
2019
ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles.
CoRR, 2019
Proceedings of the Conference on Fairness, Accountability, and Transparency, 2019
Actionable Auditing: Investigating the Impact of Publicly Naming Biased Performance Results of Commercial AI Products.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019