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2024 EqualAI Summit proceedings
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: RAND Corporation
Conference proceedings from a summit focused on AI equity and fairness, published by RAND; relevant for governance and policy dimensions of AI safety, particularly around bias, discrimination, and equitable deployment frameworks.
Metadata
Importance: 38/100conference paperreference
Summary
Proceedings from the 2024 EqualAI Summit, hosted or published by RAND, focusing on equitable and responsible AI development. The summit likely addresses bias mitigation, fairness, and governance frameworks for AI systems. Content covers policy recommendations and practitioner perspectives on reducing discriminatory outcomes in AI deployment.
Key Points
- •Examines equity and fairness challenges in AI systems across various sectors and deployment contexts
- •Brings together policymakers, researchers, and practitioners to address algorithmic bias and discrimination
- •Likely includes governance frameworks and best practices for responsible AI development
- •RAND publication adds credibility as a policy research institution focused on evidence-based recommendations
- •Addresses intersection of AI safety, civil rights, and equitable outcomes in automated decision-making
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI Policy Effectiveness | Analysis | 64.0 |
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Steps Toward AI Governance: Insights and Recommendations from the 2024 EqualAI Summit | RAND
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The Wayback Machine - https://web.archive.org/web/20260331141936/https://www.rand.org/pubs/conf_proceedings/CFA3799-1.html
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CF-A3799-1
Steps Toward AI Governance
Insights and Recommendations from the 2024 EqualAI Summit
Douglas Yeung, Tina Huang, Benjamin Boudreaux, Prateek Puri, Jonathan W. Welburn, Anita Chandra, Miriam Vogel
Expert InsightsPublished Feb 20, 2025
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EqualAI's 2024 artificial intelligence (AI) summit, cosponsored by RAND, was convened in Washington, D.C., to facilitate dialogue among corporate stakeholders from multiple industries, functions, and roles about AI development, acquisition, and integration. The purpose of the summit was to identify and align on common practices, discuss challenges, and share lessons learned in establishing and evaluating metrics in AI governance.
These conference proceedings describe key insights derived from summit discussions about best practices, metrics, and tools for evaluating the standards and performance of AI systems. The authors highlight two the
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