Towards Stable Preferences for Stakeholder-aligned Machine Learning
This addresses the challenge of aligning machine learning with stakeholder values in organ transplantation, though it appears incremental as it builds on existing preference learning methods.
The researchers tackled the problem of kidney allocation by developing a method to learn individual and group-level preferences from survey data, aiming to incorporate stakeholder values into the process to enhance ethical and equitable practices.
In response to the pressing challenge of kidney allocation, characterized by growing demands for organs, this research sets out to develop a data-driven solution to this problem, which also incorporates stakeholder values. The primary objective of this study is to create a method for learning both individual and group-level preferences pertaining to kidney allocations. Drawing upon data from the 'Pairwise Kidney Patient Online Survey.' Leveraging two distinct datasets and evaluating across three levels - Individual, Group and Stability - we employ machine learning classifiers assessed through several metrics. The Individual level model predicts individual participant preferences, the Group level model aggregates preferences across participants, and the Stability level model, an extension of the Group level, evaluates the stability of these preferences over time. By incorporating stakeholder preferences into the kidney allocation process, we aspire to advance the ethical dimensions of organ transplantation, contributing to more transparent and equitable practices while promoting the integration of moral values into algorithmic decision-making.