Operationalizing FAccT : A Case Study at the Swedish Tax Agency

Abstract: Fairness, accountability and transparency (FAccT) in machine learning is an interdisciplinary area that concerns the design, development, deployment and maintenance of ethical AI and ML. Examples of research challenges in the field are detecting biased models, accountability issues that arise with systems that make decisions without human intervention or oversight, and the blackbox issues where decisions made by an AI system are untraceable. Whereas previous research within the FAccT domain typically only uses one perspective to investigate and research ethical AI, this paper takes the opposite approach of considering all three perspectives and uses them together to conduct a holistic case study. The aim of this paper is to provide tangible insights into how organizations can work with ethical AI and ML. The empirical evidence is gathered from the advanced data analytics (ADA) team at the Swedish Tax Agency in the form of interviews and quantitative data from a model developed by the team. Most notably, the quantitative and qualitative results show that: the data set used to train the model is biased, and there are risks with the current modus operandi due to (1) disagreeing views on accountability and (2) differences in literacy and understanding of ML and AI. Furthermore, this paper also features examples of how newly proposed frameworks such as SMACTR (a large scale AI systems audit framework), data sheets and model cards can be used by ADA in the development process to address these issues, and the potential benefits and caveats of the frameworks themselves. We also showcase how theoretical models such as Larssons 7 nuances of transparency and Bovens accountability framework can be applied in a practical setting and provide supporting evidence that shows their respective applicability. Finally, the implications of taking a collective approach to FAccT, the importance of ethics and transparency, and comparisons of different used frameworks are discussed.