User acceptance of AI-driven decisions depends on explanation type, with sensitivity-analysis and certification explanations increasing acceptance¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? The review reports, citing Shulner-Tal et al., that people were more accepting of AI decisions when explanations emphasized sensitivity analysis or certification. → Papaneophytou 2025

Evidence¶

Papaneophytou 2025¶

Papaneophytou, C.; Nicolaou, S.A. (2025). Promoting Critical Thinking in Biological Sciences in the Era of Artificial Intelligence: The Role of Higher Education. Trends High. Educ. 2025, 4, 24. https://doi.org/10.3390/higheredu4020024

q2 · i? · review · r2

Narrative review of perceived legitimacy of algorithmic decisions, reporting the Shulner-Tal et al. [50] finding on explanation types. The review frames this as supporting explainable AI (XAI) in educational and scientific contexts.

"Their study found that the type of explanation matters: people were more accepting of AI decisions when explanations emphasized sensitivity analysis (how changes in input data affect output) or certification (expert endorsement or validation)."

Discussion¶

Related Claims¶

• Teachers report domain-driven explanations are more influential for building trust than data-driven explanations — related
• Domain-driven explanations significantly increase teachers' understandability, trust, and acceptance compared to data-driven explanations — related
• Explainability's effect on trust in AI-enabled systems is mixed: it can increase trust and reliance but also raises cognitive load, slows performance, and sometimes does not improve trust — related
• Explanatory feedback alone raises both perceived fairness and decision acceptance, and the indirect effect of agent on acceptance is significant only when explanations are provided — a broader claim this one bears on
• Supporting user interaction and control in the indicator implementation process positively affects transparency, trust, satisfaction, and acceptance in LA — related