Concisely Explaining the Doubt: Minimum-Size Abductive Explanations for Linear Models with a Reject Option
Trustworthiness in artificial intelligence depends not only on what a model decides, but also on how it handles and explains cases in which a reliable decision cannot be made. In critical domains such as healthcare and finance, a reject option allows the model to abstain when evidence is insufficient, making it essential to explain why an instance is rejected in order to support informed human intervention. In these settings, explanations must not only be interpretable, but also faithful to the underlying model and computationally efficient enough to support real-time decision making. Abductive explanations guarantee fidelity, but their exact computation is known to be NP-hard for many cl...
| Authors | Gleilson Pedro Fernandes, Thiago Alves Rocha |
|---|---|
| Primary category | cs.LG |
| Published | 14 March 2026 |
| Last updated at source | 14 March 2026 |
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