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Mustafa Yalçıner

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Artificial Intelligence cs.CY Data Structures and Algorithms Human-Computer Interaction Machine Learning

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preprint2026arXiv

Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users

Trust calibration -- aligning user trust judgment with model capability -- is crucial for safe deployment of explainable AI (XAI), yet is often evaluated via global trust ratings detached from objective performance evidence. We present a preregistered, incentivized between-subject online study (N=418 representative UK sample) on explainable skin-lesion classification that disentangles expectation-setting from experienced performance. Participants completed 15 case evaluations using a fixed XAI panel (malignancy score, reliability score, and saliency map). We systematically manipulated five experimental onboarding conditions varying example-based information and limitation disclosures with five stimulus packages naturally varying observed prediction quality. Calibration was operationalized as the deviation between trust-related judgments (TAIS and case-wise ratings) and objective performance benchmarks for the encountered cases, analysed with hierarchical mixed-effects models. Only limitation disclosure for case-wise measures reliably impacts trust calibration, and short-term experience did not yield progressive calibration. Further, the experienced package of stimuli explained substantially more variance than the experimental manipulation. However, participants were hard-pressed to differentiate between case-wise perceived trust, trustworthiness, and accuracy estimation. We discuss implications for designing limitation communication and for measuring and analysing calibration metrics in XAI evaluations. All study materials and data of this study are publicly available for replication and further academic use.

preprint2022arXiv

Improved Approximation Algorithms for Individually Fair Clustering

We consider the $k$ -clustering problem with $\ell_p$ -norm cost, which includes $k$ -median, $k$ -means and $k$ -center, under an individual notion of fairness proposed by Jung et al. [2020]: given a set of points $P$ of size $n$ , a set of $k$ centers induces a fair clustering if every point in $P$ has a center among its $n/k$ closest neighbors. Mahabadi and Vakilian [2020] presented a $(p^{O(p)},7)$ -bicriteria approximation for fair clustering with $\ell_p$ -norm cost: every point finds a center within distance at most $7$ times its distance to its $(n/k)$ -th closest neighbor and the $\ell_p$ -norm cost of the solution is at most $p^{O(p)}$ times the cost of an optimal fair solution. In this work, for any $\varepsilon>0$ , we present an improved $(16^p +\varepsilon,3)$ -bicriteria for this problem. Moreover, for $p=1$ ( $k$ -median) and $p=\infty$ ( $k$ -center), we present improved cost-approximation factors $7.081+\varepsilon$ and $3+\varepsilon$ respectively. To achieve our guarantees, we extend the framework of [Charikar et al., 2002, Swamy, 2016] and devise a $16^p$ -approximation algorithm for the facility location with $\ell_p$ -norm cost under matroid constraint which might be of an independent interest. Besides, our approach suggests a reduction from our individually fair clustering to a clustering with a group fairness requirement proposed by Kleindessner et al. [2019], which is essentially the median matroid problem [Krishnaswamy et al., 2011].

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