The extensive adoption of business analytics (BA) has brought financial gains
and increased efficiencies. However, these advances have simultaneously drawn
attention to rising legal and ethical challenges when BA inform decisions with
fairness implications. As a response to these concerns, the emerging study of
algorithmic fairness deals with algorithmic outputs that may result in
disparate outcomes or other forms of injustices for subgroups of the
population, especially those who have been historically marginalized. Fairness
is relevant on the basis of legal compliance, social responsibility, and
utility; if not adequately and systematically addressed, unfair BA systems may
lead to societal harms and may also threaten an organization's own survival,
its competitiveness, and overall performance. This paper offers a
forward-looking, BA-focused review of algorithmic fairness. We first review the
state-of-the-art research on sources and measures of bias, as well as bias
mitigation algorithms. We then provide a detailed discussion of the
utility-fairness relationship, emphasizing that the frequent assumption of a
trade-off between these two constructs is often mistaken or short-sighted.
Finally, we chart a path forward by identifying opportunities for business
scholars to address impactful, open challenges that are key to the effective
and responsible deployment of BA.

该论文提供了针对算法公平性的业务分析的前瞻性评论，首先审查了偏差的来源和度量标准以及偏差缓解算法的最新研究进展，然后详细讨论了效用公平关系，并最终确定了业务学者可以解决和解决的关键挑战，这些挑战对 BA 的有效和负责任的部署至关重要。