Artificial intelligence based tools are being developed for decision support in healthcare, however, they are frequently found to lack the required functionality to achieve the clinical goals for which they were built. This results in wasted time, money and resources for hospitals attempting to implement and operate such tools. To determine how functionality issues can be resolved prior to tool implementation, it is necessary to understand why such tools are being designed and then built. Our research focuses on clinical decision support tools with functionality issues arising from target variable invalidity. In this paper, we analyze published articles which present clinical decision support tool designs related to clinical goals. These tools use machine learning models trained on electronic health record data. We find that design decisions driven by data availability can introduce construct invalidity in clinical decision support tool designs, leading to an inability of the tool to address the clinical goal. We observe that alternative goals to the main clinical goal are used to justify continued development. We show that functional limitations of the tool related to the clinical goal can be obscured by imprecise terminology in the model’s stated functionality. Finally, we highlight the need for reconsidered approaches to dataset creation, defining success criteria, and the reporting and transparency of research outcomes as they relate to clinical goals.

A sociotechnical systems lens on AI is often used to bring attention to the human factors and societal impacts that are often neglected through technical abstraction. However, abstraction is also a general principle of sociotechnical systems, where functional objectives (e.g. fair hiring decisions) are operationalised into low-level implementations (e.g. fair algorithms, recourse, legal basis). The trouble with abstraction arises when critical contextual factors are erroneously neglected, leading to an impoverished representation of the problem space. De-contextualisation can render the resulting solutions problematic when they are re-contextualised back into the site of use, where misabstractions may produce safety hazards, harms, moral wrongs, and context frictions. Despite growing recognition that context matters for how sociotechnical systems operate in practice, the normative implications of abstraction are still understudied. In this paper, we propose misabstraction as an analytic framework for thinking about the perils and challenges of sociotechnical abstraction. We use the framework to analyse the requirements specification outlined in the procurement tender of a recommender system for public employment services and show how misabstractions cascade through the sociotechnical stack, producing ripple effects that implicate hidden and neglected contextual factors across multiple frames (e.g. institutional, organisational, operational, and algorithmic). Misabstraction can help policymakers, system designers, critical scholars, and civil society alike to attend to the political conditions that shape design, and their implications for understanding and addressing systemic risk in sociotechnical AI systems.