AI systems are increasingly being positioned to assist people in decision-making. However, recent empirical studies show critical concerns that people over-rely on AI advice without analytically engaging with it. While HCI research explores how people rely on AI advice, we argue that it largely overlooks an important aspect: replicating realistic decision-making scenarios. Human-AI interaction factors influence people’s reliance on AI advice. To understand human-AI interaction factors and their interplay, we conducted an analytical review of recent studies in human-AI reliance literature. We analyzed the decision-making tasks in research and their validity in application-grounded contexts. Our findings show that user engagement is a precious commodity for relying on AI advice; however, it comes at a cost. We also discuss factors contributing to “appropriate reliance”, existing research gaps, and recommendations for intervention design for human-AI reliance. Our work contributes to the critical body of research on building appropriate reliance on AI advice.

Alternative text (alt text) or image descriptions for scientific figures make them accessible to screen reader users.Yet, generating high-quality alt text remains a significant challenge, particularly for people lacking subject-matter expertise.Emerging AI tools, such as generative AI, can help people understand contexts where they lack subject expertise.This work explores how university Disability Services Office (DSO) professionals write alt text for scientific figures without subject expertise.We conducted a user study with 12 DSO professionals who authored alt text for scientific figures, first without and then using generative AI.We assessed the participants' processes for alt text generation and their confidence in the resulting output.We also conducted semi-structured interviews to understand DSO professionals' experiences with the alt text generation.Our findings reveal that AI assistance improved participants' confidence and efficiency, but introduced new challenges for interaction and trust.Participants expressed caution about relying on AI-generated descriptions without sufficient domain knowledge and editing.These insights highlight the need for AI-augmented tools that better scaffold the alt text writing process for accessibility professionals.

Users often over-rely on AI-assisted decisions without analytically engaging with them, even in practical domains.In this work, we explore persuading users to analytically engage with AI assistance to reduce their over-reliance using a complex business case of customer classification.We explore the effect of persuasive cognitive engagement through explanations and communicating system uncertainty to examine the behavior of participants having diverse expertise.We leverage their feedback and objective behavior to understand their perception of the AI performance.Our findings show a contrast in participants' subjective and objective behavior, indicating inappropriate reliance on AI assistance with the perception of system performance.However, we observe the positives of interactive cognitive engagement and identify further directions to get deeper insights into expert domains with personalized AI assistance and behavioral persuasion.