Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring
本文探讨AI招聘中数据、设计和决策三方面如何导致排斥与包容，指出需关注组织匹配、分类和交叉性，并建议加强人机互动与算法审计研究。
Abstract Despite frequent claims that increased use of artificial intelligence (AI) in hiring will reduce the human bias that has long plagued recruitment and selection, AI may equally replicate and amplify such bias and embed it in technology. This article explores exclusion and inclusion in AI‐supported hiring, focusing on three interrelated areas: data, design and decisions. It is suggested that in terms of data, organisational fit, categorisations and intersectionality require consideration in relation to exclusion. As various stakeholders collaborate to create AI, it is essential to explore which groups are dominant and how subjective assessments are encoded in technology. Although AI‐supported hiring should enhance recruitment decisions, evidence is lacking on how humans and machines interact in decision‐making, and how algorithms can be audited and regulated effectively for inclusion. This article recommends areas for interrogation through further research, and contributes to understanding how algorithmic inclusion can be achieved in AI‐supported hiring.