AI consumer markets are characterized by severe buyer-supplier market asymmetries. Complex AI systems can appear highly accurate while making costly errors or embedding hidden defects. While there have been regulatory efforts surrounding different forms of disclosure, large information gaps remain. This paper provides the first experimental evidence on the important role of information asymmetries and disclosure designs in shaping user adoption of AI systems. We systematically vary the density of low-quality AI systems and the depth of disclosure requirements in a simulated AI product market to gauge how people react to the risk of accidentally relying on a low-quality AI system. Then, we compare participants’ choices to a rational Bayesian model, analyzing the degree to which partial information disclosure can improve AI adoption. Our results underscore the deleterious effects of information asymmetries on AI adoption, but also highlight the potential of partial disclosure designs to improve the overall efficiency of human decision-making.

Handcrafted radiomic descriptors are widely used in biomedical image analysis, but classic radiomics pipelines often suffer from high feature redundancy and an underdeveloped, weakly principled feature-selection practice, which together can impair generalization and limit model interpretability. To address this, we introduce ReSHAPe (Redundancy-Reduced SHAP-based Evaluation), a two-stage, model-aware feature selection pipeline that makes SHAP-driven selection practical for radiomics. ReSHAPe first performs redundancy pruning by removing highly correlated features using Spearman rank correlation, retaining within each correlated group the descriptor with the lower absolute skewness. It then applies SHAP-based global importance to rank the remaining features and iteratively select a compact subset; an ensemble variant aggregates SHAP rankings across multiple classifiers to promote consensus and interoperability. We evaluate ReSHAPe on three MedMNIST v2 subsets (BreastMNIST, PneumoniaMNIST, BloodMNIST) using 285 handcrafted features and five well-known classifiers (SVM, Decision Tree, Random Forest, Extra Trees, XGBoost), comparing against univariate filters (ANOVA F-test, mutual information), SHAP-only selection, correlation-based filtering, and full-feature baselines. Across datasets, ReSHAPe preserves performance while drastically reducing dimensionality; on radiomic tasks, it is consistently competitive with SHAP-only selection f-measure weighted values differences typically lower than 0.03, and it remains effective in the non-radiomic multiclass setting (maximum decrease of f-measure weighted value lower than 0.04). Finally, the correlation pre-filtering stage markedly reduces SHAP overhead, which would otherwise require 200 additional model training/evaluation steps when applied directly to the full feature space.

Abstract Artificial Intelligence (AI) systems are increasingly used for decision-making across domains, raising debates over the information and explanations they should provide. Most research on Explainable AI (XAI) has focused on feature-based explanations, with less attention on alternative styles. Personality traits like the Need for Cognition (NFC) can also lead to different decision-making outcomes among low and high NFC individuals. We investigated how presenting AI information (prediction, confidence, and accuracy) and different explanation styles (example-based, feature-based, rule-based, and counterfactual) affect accuracy, reliance on AI, and cognitive load in a loan application scenario. We also examined low and high NFC individuals’ differences in prioritizing XAI interface elements (loan attributes, AI information, and explanations), accuracy, and cognitive load. Our findings show that high AI confidence significantly increases reliance on AI while reducing cognitive load. Feature-based explanations did not enhance accuracy compared to other conditions. Although counterfactual explanations were less understandable, they enhanced overall accuracy, increasing reliance on AI and reducing cognitive load when AI predictions were correct. Both low and high NFC individuals prioritized explanations after loan attributes, leaving AI information as the least important. However, we found no significant differences between low and high NFC groups in accuracy or cognitive load, raising questions about the role of this specific personality trait in AI-assisted decision-making. These findings underscore the importance of user-centric personalization in XAI interfaces, where explanation styles are tailored to users’ personality traits, cognitive characteristics, and task context, with support adapted to each individual to optimize human–AI collaboration.

Previous research on eXplainable Artificial Intelligence (XAI) in AI-assisted decision-making has shown mixed results in increasing users' accuracy while mitigating overreliance on AI. A promising yet underexplored strategy consists of providing AI assistance on-demand through explicit interaction. Preliminary results show that users with high Need for Cognition (NFC) benefit more from such a paradigm, though the effects predicted by similar cognitive measures require further investigation. In addition, hybrid approaches consisting of descriptive statistics on the training data (global data-centric) with model-centric explanations have shown the potential to mitigate overreliance while improving accuracy for experts and lay users in the health domain. However, the impact of this approach in other fields is still unknown.This paper investigates the effects of four on-demand explanation types - local model-centric, global data-centric, local/global model-centric, and hybrid - on users' accuracy and overreliance. We also assess how variations in Need for Cognition (NFC), Epistemic Curiosity (EC), and Curiosity and Exploration Inventory-II (CEI-II) impact these metrics and explore correlations among these traits.Our findings indicate no significant differences among on-demand explanations to improve accuracy or mitigate overreliance. The same holds for low and high NFC, EC, and CEI-II individuals, although we found moderate positive correlations among these psychometrics. Post-hoc analysis revealed that personality traits and the on-demand intervention influenced other decision-making behaviors more than the type of explanation provided. Users who requested on-demand assistance exhibited lower confidence, suggesting that seeking data or AI support may undermine self-confidence. Interestingly, individuals with higher NFC and CEI-II scores showed greater confidence, and those scoring higher on CEI-II requested AI assistance less frequently.We contribute to expanding the knowledge about XAI-assisted decision-making by providing practical guidelines for designing AI systems that account for individual cognitive traits and user confidence, helping to improve their effectiveness in decision-making tasks.

A common criteria for Explainable AI (XAI) is to support users in establishing appropriate trust in the AI – rejecting advice when it is incorrect, and accepting advice when it is correct. Previous findings suggest that explanations can cause an over-reliance on AI (overly accepting advice). Explanations that evoke appropriate trust are even more challenging for decision-making tasks that are difficult for humans and AI. For this reason, we study decision-making by non-experts in the high-uncertainty domain of stock trading. We compare the effectiveness of three different explanation styles (influenced by inductive, abductive, and deductive reasoning) and the role of AI confidence in terms of a) the users’ reliance on the XAI interface elements (charts with indicators, AI prediction, explanation), b) the correctness of the decision (task performance), and c) the agreement with the AI’s prediction. In contrast to previous work, we look at interactions between different aspects of decision-making, including AI correctness, and the combined effects of AI confidence and explanations styles. Our results show that specific explanation styles (abductive and deductive) improve the user’s task performance in the case of high AI confidence compared to inductive explanations. In other words, these styles of explanations were able to invoke correct decisions (for both positive and negative decisions) when the system was certain. In such a condition, the agreement between the user’s decision and the AI prediction confirms this finding, highlighting a significant agreement increase when the AI is correct. This suggests that both explanation styles are suitable for evoking appropriate trust in a confident AI.

Existing eXplainable Artificial Intelligence (XAI) techniques support people in interpreting AI advice. However, although previous work evaluates the users’ understanding of explanations, factors influencing the decision support are largely overlooked in the literature. This article addresses this gap by studying the impact of user uncertainty , AI correctness , and the interaction between AI uncertainty and explanation logic-styles for classification tasks. We conducted two separate studies: one requesting participants to recognize handwritten digits and one to classify the sentiment of reviews. To assess the decision making, we analyzed the task performance, agreement with the AI suggestion, and the user’s reliance on the XAI interface elements. Participants make their decision relying on three pieces of information in the XAI interface (image or text instance, AI prediction, and explanation). Participants were shown one explanation style (between-participants design) according to three styles of logical reasoning (inductive, deductive, and abductive). This allowed us to study how different levels of AI uncertainty influence the effectiveness of different explanation styles. The results show that user uncertainty and AI correctness on predictions significantly affected users’ classification decisions considering the analyzed metrics. In both domains (images and text), users relied mainly on the instance to decide. Users were usually overconfident about their choices, and this evidence was more pronounced for text. Furthermore, the inductive style explanations led to overreliance on the AI advice in both domains—it was the most persuasive, even when the AI was incorrect. The abductive and deductive styles have complex effects depending on the domain and the AI uncertainty levels.