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.

Welcome to episode 4 of New voices in AI. In this episode Maria De-Arteaga shares her work and journey into algorithmic fairness and human algorithm collaboration. You can find out more on Maria's website and follow her on Twitter, @mariadearteaga. Daly: Hello and welcome to New Voices in AI the series from Ai hub where we celebrate the voices of Masters and PhD students, early career researchers and those with a new perspective on AI. I am Joe Daly, engagement manager for AI hub and this week I am talking to Maria De-Arteaga about some of her research.