Integrating Fairness in the Software Design Process: An Interview Study With HCI and ML Experts

Abstract

The term Fairness is used in the field of Machine Learning to refer to a suite of evaluation techniques, measures, and process adjustments focused on the reduction of bias in statistical models. With many different potential definitions and methodologies for implementing fairness, some contradictory in goal or incompatible in approach, there are challenges in the selection of the most appropriate definition(s) to meet the ethical expectations society or end users may have for a model. This is further confounded by the potentially complex engineering challenges in applying the selected definition. The goal of designing and developing a fair system requires interdisciplinary collaboration between user experience researchers, focused on the socio-technical challenges of application usage and adoption, and data scientists and machine learning software developers, directly involved in the technical engineering and optimisation of such models. How these two groups interact and how their areas of specialisation can support and influence the other when developing fair AI is unknown. This leaves open questions about how these stakeholders view fairness, how it can be operationalised in their current work, and what are the obstacles/opportunities for improving the treatment of fairness in practice. In order to answer these questions, 18 subject matter experts from the fields of human-computer interaction (HCI) or Machine Learning (ML) were interviewed about their experience with fairness, their perception of current fairness adoption, and the challenges they would anticipate in a set of example scenarios. This work identifies some of the current challenges to developing fair artificial intelligence (AI). In dealing with these challenges, we propose approaches that include expanding the responsibilities of HCI and ML specialists, and identifying areas where joint responsibility is required.