Despite the growing prevalence of human-AI decision making, the human-AI team’s decision performance often remains suboptimal, partially due to insufficient examination of humans’ own reasoning. In this paper, we explore designing AI systems that directly analyze humans’ decision rationales and encourage critical reflection of their own decisions. We introduce the AI-Assisted Critical Thinking (AACT) framework, which leverages a domain-specific AI model’s counterfactual analysis of human decision to help decision-makers identify potential flaws in their decision argument and support the correction of them. Through a case study on house price prediction, we find that AACT outperforms traditional AI-based decision-support in reducing over-reliance on AI, though also triggering higher cognitive load. Subgroup analysis reveals AACT can be particularly beneficial for some decision-makers such as those very familiar with AI technologies. We conclude by discussing the practical implications of our findings, use cases and design choices of AACT, and considerations for using AI to facilitate critical thinking.

Visual content shared on social media often includes sensitive elements that can threaten personal privacy. While privacy protection tools--some of which are powered by the state-of-the-art generative AI (Gen-AI) technologies--have been increasingly developed to address such visual privacy concerns by identifying sensitive elements in visual content and suggesting or applying modifications to process the visual content, the success of these tools depends on how well they meet users' nuanced needs and preferences. In this study, we conducted semi-structured interviews with 18 individuals who have either experienced or caused privacy violations in shared visual content in the past to gather first-hand perspectives on stakeholders' privacy concerns, their preferences for how to address these concerns, and their attitude toward the use of generative AI for privacy protection. Our findings highlight that sensitive elements are often not limited to direct identifiers but include contextual combinations and external information that can lead to unintended inferences. Decisions about whether and what to modify are shaped by concerns about privacy effectiveness, content value, content meaning, and emotional or social relevance, while choices around how to modify are influenced by recognition difficulty, visual content integrity, contextual consistency, atmosphere, and usability of modification methods. Participants saw Gen-AI as a promising tool for lowering editing barriers and enhancing creative control but also raised concerns about data usage, manipulation, and transparency. Importantly, we identify tensions between uploaders and depicted individuals, emphasizing the need for shared consent mechanisms and user-centered design in privacy protection. We conclude by discussing design implications for context-aware, flexible, and ethically responsible privacy tools.