par Bontempi, Gianluca 
Référence Lecture notes in computer science, 15508 LNCS, page (333-343)
Publication Publié, 2025-07-01
Référence Lecture notes in computer science, 15508 LNCS, page (333-343)
Publication Publié, 2025-07-01
Article révisé par les pairs
| Résumé : | Intelligent agents rely on AI/ML functionalities to predict the consequences of possible actions and optimise the policy. However, the research community’s effort in addressing prediction accuracy has been so intense (and successful) that it created the illusion that the more accurate the learner’s prediction (or classification), the better the final decision would have been. Now, such an assumption is valid only if the (human or artificial) decision maker has complete knowledge of the utility of the possible actions. This paper argues that the AI/ML community has taken so far a too unbalanced approach by devoting excessive attention to the estimation of the state (or target) probability to the detriment of accurate and reliable estimations of the utility. In particular, few studies have examined the impact of a wrong utility assessment on the expected utility of the decision strategy. This situation is creating a substantial gap between the expectations and the effective impact of AI solutions, as witnessed by recent criticisms and emphasised by the regulatory legislative efforts. This paper aims to study this gap by quantifying the sensitivity of the expected utility to the utility uncertainty and comparing it to the one due to probability estimation. Theoretical and simulated results show that an inaccurate utility assessment may be as (and sometimes more) harmful than a poor probability estimation. The final recommendation to the community is to shift from a pure accuracy-driven (or obsessed) approach to a more utility-aware methodology. |
