问题化人工智能在招聘和组织不平等中的作用:一个多学科的审查
Karen D Hughes1, Alla Konnikov2, Nicole Denier3
1University of Alberta, Canada, karen.hughes@ualberta.ca.
概括
人工智能 (AI) 在招聘方面可能通过隐藏偏见来加剧组织不平等. 需要进一步的研究和多利益相关者责任来规范算法招聘并确保公平.
科学领域:
- 综合计算,数据科学,管理,社会科学和法律的多学科研究.
- 专注于人工智能 (AI) 的社会和组织影响.
背景情况:
- 人工智能 (AI) 越来越多地用于招聘和招聘.
- 倡导者强调了人工智能在提高招聘精度,效率,多样性和适应性方面的潜力.
- 批评者对AI可能带来偏见,不公平和侵犯隐私的可能性表示担忧.
研究的目的:
- 批判性地检查人工智能在招聘中的影响,以解决组织不平等问题.
- 通过综合各种学术观点,推进关于人工智能在招聘中的作用的辩论.
- 为理解和解决人工智能驱动的雇佣不平等问题提出一个框架.
主要方法:
- 混合审查方法结合了范围和问题化审查方法.
- 从各种学术领域的多学科奖学金的分析.
- 应用阿克尔 (2006) 的"不平等制度"框架.
主要成果:
- 在概念化围绕人工智能在招聘方面的关键问题时确定了不对称性.
- 突出了人工智能隐藏在招聘过程中的现有不平等现象的巨大潜力.
- 注意到有关对算法招聘的监管的持续质疑.
结论:
- 提出了"算法介导的不平等制度"的概念来描述AI的作用.
- 强调了人工智能通过算法隐形性和感知合法性来隐藏和重现不平等的能力.
- 呼吁建立一个跨学科的"知识链"和多利益相关者"责任链"来规范和应用人工智能.
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