In-context Inverse Optimality for Fair Digital Twins: A Preference-based approach
This work addresses fairness alignment in autonomous decision-making systems like Digital Twins for socio-technical applications, representing an incremental improvement by integrating human preferences into optimization.
This paper tackles the gap between mathematically optimal decisions of Digital Twins and human fairness expectations by developing a preference-driven framework that learns fairness objectives from human pairwise preferences, demonstrated on a COVID-19 hospital resource allocation scenario with results showing alignment with human-perceived fairness while maintaining computational efficiency.
Digital Twins (DTs) are increasingly used as autonomous decision-makers in complex socio-technical systems. Their mathematically optimal decisions often diverge from human expectations, exposing a persistent gap between algorithmic and bounded human rationality. This work addresses this gap by proposing a framework that operationalizes fairness as a learnable objective within optimization-based Digital Twins. We introduce a preference-driven learning pipeline that infers latent fairness objectives directly from human pairwise preferences over feasible decisions. A novel Siamese neural network is developed to generate convex quadratic cost functions conditioned on contextual information. The resulting surrogate objectives align optimization outcomes with human-perceived fairness while maintaining computational efficiency. The approach is demonstrated on a COVID-19 hospital resource allocation scenario. This study provides an actionable path toward embedding human-centered fairness in the design of autonomous decision-making systems.