Artificial intelligence (AI) is being used in audit processes. However, auditors may ignore algorithmic advice, a condition known as algorithm aversion. This study examines the association between the perceived dependability and transparency (explainability) of standard artificial intelligence systems and their effects on algorithm aversion and trust in AI outputs; and the association between trust and auditors’ desire to rely professionally. The questionnaire was issued to 140 external and internal auditors and consisted of 25 items, measuring five constructs on a 5-point Likert scale. All constructs showed good internal consistency (Cronbach's α = 0.848–0.882). Reliability and transparency were negatively related to algorithm aversion (β = −0.29 and −0.30) and positively related to trust (β = 0.27 and 0.31), while aversion was negatively related to trust (β = −0.28). Trust had the strongest standardized coefficient in the professional-reliance-intention model (β = 0.40). The models explained 24.7%, 45.6%, and 38.6% of the variance in aversion, trust, and reliance intentions, respectively. The percentile bootstrap analysis (5,000 resamples) revealed favorable indirect associations between reliability and transparency with reliance intention through trust. The confirmatory factor analysis (CFA) indicated a good fit (χ2(265) = 283.74, p = 0.205; CFI = 0.99; TLI = 0.99; RMSEA = 0.02). Composite reliability was ≥ 0.85, average variance extracted was ≥ 0.53, indicating a good convergent validity. The Fornell–Larcker criterion confirmed the discriminant validity. Analyses of diagnostics suggested sample adequacy (KMO = 0.90), no multicollinearity (maximum VIF = 1.84), and a Harman single factor of 37.3% for an initial assessment of common-method bias. All 8 hypotheses were empirically supported. Our results show that trust mediates the relationship between dependable and explainable AI design and auditors’ intention to rely on AI outputs, but the cross-sectional design does not allow for causal conclusion.