Bridging Predictions and Interventions: An Integrated Framework for Automated Decision-Systems
For researchers and practitioners designing ADS in high-stakes domains (e.g., criminal justice, healthcare), this paper reframes the problem from prediction to intervention, but the argument is conceptual without empirical validation.
The paper argues that improving predictive accuracy alone is insufficient for effective automated decision systems (ADS) in social settings, as predictions alter workflows and decision processes. It proposes shifting from a prediction-centric to an intervention-oriented framework to better anticipate real-world consequences.
Automated decision systems (ADS) leverage predictions about individual future outcomes to inform consequential decision-making in organizational settings. Across various settings - including criminal pretrial release, clinical triage, student support, and more - it is often assumed that improved predictive accuracy is the priority consideration in determining better downstream outcomes upon the deployment of ADS. In practice, real-world case studies reveal that this is far from the case: introducing individual predictions into decision-making modifies organizational workflows, assessment, and decision-making processes in ways that require a complete re-consideration of our approach to the design, evaluation, and deployment of ADS. As a result, this Perspective develops an integrated framework for studying ADS in social systems, shifting current priorities from a purely prediction-based paradigm towards an intervention-oriented view that accounts for real-world conditions. Our aim is to improve our understanding of ADS and more meaningfully anticipate its downstream societal and organizational consequences.