Explainable Artificial Intelligence for Enhancing Trust in Autonomous Systems
DOI:
https://doi.org/10.64235/kvy1v726Keywords:
Explainable artificial intelligence, autonomous systems, human-AI trust, reliance calibration, interpretabilityAbstract
Autonomous systems—ranging from self-driving vehicles to robotic surgical assistants and automated financial decision
engines—are increasingly entrusted with consequential, safety-critical decisions. Yet their reliance on opaque, high-dimensional
machine learning models has produced a persistent “trust gap” between system capability and human willingness to rely on
these systems. This study investigates whether Explainable Artificial Intelligence (XAI) techniques can measurably enhance
user trust, calibrate reliance, and improve decision quality in interactions with autonomous systems. Using a mixed-methods
experimental design, 312 participants interacted with a simulated autonomous driving and decision-support platform under
six explanation conditions (a black-box baseline and five XAI methods: SHAP, LIME, Grad-CAM, counterfactual explanations,
and integrated gradients). Quantitative trust was measured via a validated 7-point Likert instrument, behavioral reliance was
logged, and qualitative interviews probed the mechanisms underlying trust formation. Results indicated that all XAI conditions
produced significantly higher trust than the black-box baseline (mean increase of 2.3 to 2.9 points, p < .001), with counterfactual
explanations yielding the highest trust (M = 6.02, SD = 0.31). Trust scaled monotonically with explanation fidelity, and appropriate
(calibrated) reliance—rather than raw trust—was the strongest predictor of decision accuracy. Qualitative analysis revealed
that perceived transparency, actionability, and consistency drove trust formation. We conclude that XAI is a necessary but not
sufficient condition for trustworthy autonomy: explanation quality, not mere presence, determines whether trust is appropriately
calibrated. Implications for human-centered design, regulation, and future research are discussed.
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