算法歧视:研究其类型和监管措施,重点关注美国的法律实践
Xukang Wang1, Ying Cheng Wu2, Xueliang Ji3
1Sage IT Consulting Group, Shanghai, China.
Frontiers in artificial intelligence
|June 5, 2024
概括
算法决策系统可以延续偏见. 本研究分析了算法歧视的类型,并提出了人工智能部署中公平性和问责制的解决方案.
科学领域:
- 计算机科学 计算机科学
- 法律 法律 法律 法律
- 社会学 社会学 社会学
背景情况:
- 算法决策系统在刑事司法和就业等关键领域普遍存在.
- 这些系统风险放大社会偏见和歧视,尽管它们的效率.
- 了解和减轻算法偏差对于公平的技术部署至关重要.
研究的目的:
- 对算法歧视类型进行深入分析.
- 探索对算法偏差的挑战和潜在解决方案.
- 检查管理算法公平性的法律和监管框架.
主要方法:
- 系统的文献审查.
- 法律文件的分析.
- 在不同地区和部门进行比较的案例研究.
主要成果:
- 确定了五种类型的算法偏差:代理偏差,特征选择偏差,代理歧视,差异性影响和定向广告.
- 检查了美国的法律框架,包括原则性法规,预防性控制和责任.
- 提供了欧盟,加拿大,澳大利亚和亚洲算法公平性的比较概述.
结论:
- 通过风险评估和招聘算法的案例研究来证明现实世界的影响.
- 建议跨学科的研究,积极的政策,公众意识和监测.
- 强调需要适应性方法,以确保对社会负责任的人工智能部署.
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