Jacob Tjaden

Jacob Tjaden

Machine learning models are often criticized as opaque from a lack of transparency in their decision-making process. This study examines how policy design impacts the quality of explanations in ML models. We conducted a classroom experiment with 124 participants and analyzed the effects of policy length and purpose on developer compliance with policy requirements. Our results indicate that while policy length affects engagement with some requirements, policy purpose has no effect, and explanation quality is generally poor. These findings highlight the challenge of effective policy development and the importance of addressing diverse stakeholder perspectives within explanations.

Zahra Abba Omar, Nadia Nahar, Jacob Tjaden et al.

Modern machine learning produces models that are impossible for users or developers to fully understand -- raising concerns about trust, oversight, safety, and human dignity when they are integrated into software products. Transparency and explainability methods aim to provide some help in understanding models, but it remains challenging for developers to design explanations that are understandable to target users and effective for their purpose. Emerging guidelines and regulations set goals but may not provide effective actionable guidance to developers. In a large-scale experiment with 124 participants, we explored how developers approach providing end-user explanations, including what challenges they face, and to what extent specific policies can guide their actions. We investigated whether and how specific forms of policy guidance help developers design explanations and provide evidence for policy compliance for an ML-powered screening tool for diabetic retinopathy. Participants across the board struggled to produce quality explanations and comply with the provided policies. Contrary to our expectations, we found that the nature and specificity of policy guidance had little effect. We posit that participant noncompliance is in part due to a failure to imagine and anticipate the needs of non-technical stakeholders. Drawing on cognitive process theory and the sociological imagination to contextualize participants' failure, we recommend educational interventions.