Algorithmic accountability

Abstract

Advancements in technology have led to the widespread adoption of machine learning (ML) algorithms in almost all areas of society (eg, shaping customer experiences through recommendations, and supporting organizational activities through automating tasks). Despite their potential benefits (eg, personalizing experiences, enhancing productivity, improving decision-making) various examples have shown that systems based on such algorithms can lead to negative consequences. For instance, use cases in healthcare (Obermeyer et al. 2019) or finance (Blattner and Nelson 2021), have demonstrated how ML systems can undermine fairness and discriminate against minorities by generating statistical or reproducing societal biases (Mitchell et al. 2021). Other examples in marketing have illustrated how individuals’ privacy can be compromised by inferring intimidating knowledge about individuals and using it for targeted advertising (Hill 2012; Mattu and Hill 2022). With the development from simple rule-based to complex probabilistic algorithms, the potential for harmful effects gets even more pressing, as their operation is increasingly opaque and more and more automated. While it has been already widely investigated how such issues can be addressed (eg, Liu et al. 2022; Mehrabi et al. 2021), many organizations still fail to mitigate the often unintended, negative outcomes of the ML systems they are developing, providing, and using. Thus, academics (eg, Novelli et al. 2023; Wieringa 2020) and policymakers (eg, Mökander et al. 2022; Smuha 2021) have put an increasing emphasis on the topic of algorithmic accountability to ensure the ethical development and use of such systems (Donia 2022).Although algorithmic accountability is mentioned as an important principle in almost all guidelines and regulatory documents for the ethical development of ML systems, there is still considerable uncertainty on what constitutes it and how it can be accomplished (Jobin et al. 2019). This is due to the fact, that algorithmic accountability is an ambiguous concept that deals with many different questions (Bovens 2010; Poechhacker and Kacianka 2021; Wieringa 2020). Many of these questions arise from the existence of different accountability types. To shed light on these accountability types and to bring together the different questions around algorithmic accountability, our article aims to introduce the topic to the BISE community. Future work on algorithmic accountability promises to build the foundation for organizations and developers to effectively implement and manage accountability measures and to inform policy-makers on how to appropriately regulate ML systems. Furthermore, it could provide insights into how individuals deal with accountability-related concerns and how it affects their interaction with ML systems.