arXiv:2606.18867v1 Announce Type: new Abstract: When algorithmic predictors inform resource allocation in high-stakes domains such as healthcare, these predictors must account for strategic manipulation of input features. The typical solution is to redesign the predictor itself to explicitly account for strategic interactions. In practice, however, decision makers are often constrained to adjusting coarser levers within existing prediction pipelines. For example, healthcare organizations often select which features to exclude based on perceived manipulability, while using standard regularizati
The increasing deployment of algorithmic predictors in critical domains necessitates robust methods for strategic feature selection to mitigate manipulation.
This research addresses a fundamental vulnerability in AI systems, particularly those used in high-stakes environments, by proposing practical solutions for feature selection under strategic manipulation.
The focus shifts from solely redesigning predictors to incorporating coarser adjustments within existing AI pipelines, which allows for more practical implementation in current organizational structures.
- · AI ethicists
- · Healthcare organizations
- · Machine learning researchers
- · AI governance frameworks
- · Malicious actors
- · Ineffective AI models
- · Organizations using un-manipulation-proof AI
Algorithmic predictors become more robust against feature manipulation, leading to more reliable and fair outcomes in critical applications.
Increased trust in AI systems could accelerate their adoption in sensitive sectors, but also highlight new attack vectors.
The development of 'manipulation-aware' AI design principles could become a standard requirement for high-stakes algorithmic deployments, leading to new certification bodies.
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Read at arXiv cs.LG