Explaining AI Decisions to Mitigate Cognitive Biases in

Human-AI Collaboration

Miswan Gumanti1,* Citra Dewi2

1 Institut Bakti Nusantara, Lampung, Indonesia

2 Universitas Lampung, Indonesia

Emails: mgumanti0205@gmail.com · citra.dewi@eng.unila.ac.id

Received: January 24, 2026 Revised: February 27, 2026 Accepted: March 28, 2026 ⋆ Corresponding author

ABSTRACT

Human-AI collaboration can improve decision quality only when users know when to rely on an AI recommendation

and when to resist it. Explanations are often proposed as a remedy, but explanation content can also intensify

automation bias or reinforce a user’s initial belief. This paper presents a cognitive explanation-selection model

for mitigating over-reliance and under-reliance in AI-assisted decision tasks. The study compares no explanation,

feature-based, contrastive, example-driven, and hybrid explanations across simulated novice, intermediate, and

expert decision makers using a public medical decision dataset as the task substrate. The analysis focuses on

reliance behaviour rather than on model accuracy alone. The proposed model estimates when the user is likely to

accept a wrong recommendation, reject a correct recommendation, or accept advice simply because it confirms

an initial judgment. The results indicate that contrastive and hybrid explanations are more effective for reducing

automation bias, while example-driven explanations preserve trust for lower-expertise users. The paper concludes

with a transparent interface loop for high-stakes environments in which explanation style is selected according to

user expertise, AI confidence, and human-AI agreement.

Keywords: Human-AI collaboration Explainable AI Automation bias Confirmation bias Appropriate reliance

1. INTRODUCTION

AI-assisted decision-making is now common in domains

where human judgment remains legally, ethically, or operationally

central. Medical monitoring, risk triage, air traffic

supervision, financial screening, and emergency response all

involve situations in which an AI model may recommend

an action while a human operator retains responsibility for

the final decision. In these settings, better model accuracy

is not sufficient. A highly accurate model can still harm performance

when users accept its incorrect recommendations

too readily, ignore its correct recommendations, or treat its

explanations as rhetorical persuasion rather than as evidence

to be inspected.

This paper addresses that problem by studying explanation

formats as cognitive interventions. The central question is not

simply whether explanations increase trust, but whether they

improve reliance calibration. Appropriate reliance requires

two complementary behaviours: accepting correct AI advice

when it improves the user’s decision, and rejecting incorrect

AI advice when the user’s own judgment is better. This

distinction is critical because many explanation interfaces

raise acceptance of AI advice in general. When acceptance

rises for both correct and incorrect advice, the interface may

create automation bias rather than reliable collaboration.

A second difficulty is confirmation bias. Users rarely encounter

AI recommendations as blank slates. They bring

initial beliefs, expertise, previous cases, and domain expectations

into the decision process. When an AI recommendation

agrees with the user’s first impression, the recommendation

may be accepted with little scrutiny. When it disagrees, it

may be dismissed defensively. Explanation formats therefore

need to do more than justify the AI output; they need to cre-