# Should I trust an AI's advice about how to spend my day? > Trust it as far as it has earned, and no further. Across six experiments, people followed advice more readily when they believed it came from an algorithm rather than a person (Logg et al., 2019), yet watching an automated aid make mistakes made them distrust even a reliable one (Dzindolet et al., 2003). - Page: https://tre-up.com/research/should-i-trust-an-ai-with-advice-about-my-day - Topic: [Trust and uncertainty](https://tre-up.com/research/topics/trust-and-uncertainty) - Published 2026-10-07 · Updated 2026-10-17 · 6 sources - Written by Pursue Lab, Tre-Up's research process (https://tre-up.com/research/method) - Cite as: Pursue Lab, Tre-Up (2026). Should I trust an AI's advice about how to spend my day? https://tre-up.com/research/should-i-trust-an-ai-with-advice-about-my-day ## Too much and too little Research on automation names two opposite errors: relying on a system too much (misuse) and ignoring it, often after false alarms (disuse) [1]. Because people can't fully understand complex systems, trust is what decides how much they rely on them, so the goal is trust that fits the system's real ability [2]. Across six experiments, ordinary people followed advice more when they thought it came from an algorithm rather than a person [3]. In other experiments, people expected an automated aid to beat them, then distrusted even a reliable aid after watching it make mistakes, unless they were told why it might err [4]. ## Keeping your own judgment in charge Designs that make people think before they see the AI's answer reduced over-reliance more than explanations did, though people liked them least [5]. Explanations help when a result disappoints, but too much explanation erodes trust again [6]. ## Where the evidence stops Much of this research comes from aviation, industry and estimation tasks, not from personal decisions about your own day. The direction is consistent; the size of each effect in daily life is not known. ## Something to try today Before you accept an assistant's suggestion for your day, say your own answer first, even in one word. Then compare. It is the cheapest version of the design that reduced over-reliance. ## How Tre-Up approaches this Pursue sometimes asks one question before it suggests anything, so your judgment comes first. When it is unsure, it says why in one plain sentence. Tre-Up is an iOS app in development. Pursue is the AI inside it. This note describes research and design intent, not results from the app. Nothing here is medical advice. ## Sources Summaries are written in our own words from each source's abstract, public record or official text. Follow the link to read the original. 1. Parasuraman R, Riley V (1997). Humans and Automation: Use, Misuse, Disuse, Abuse. Human Factors 39(2):230–253. https://doi.org/10.1518/001872097778543886 - Institutions: Catholic University of America (United States); Honeywell Technology Center (United States) - Our summary: https://tre-up.com/research/sources/parasuraman-riley-1997-use-misuse 2. Lee JD, See KA (2004). Trust in Automation: Designing for Appropriate Reliance. Human Factors 46(1):50–80. https://doi.org/10.1518/hfes.46.1.50_30392 - Institutions: University of Iowa (United States) - Our summary: https://tre-up.com/research/sources/lee-see-2004-trust-in-automation 3. Logg JM, Minson JA, Moore DA (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes 151:90–103. https://doi.org/10.1016/j.obhdp.2018.12.005 - Institutions: Harvard University (United States); University of California, Berkeley (United States) - Our summary: https://tre-up.com/research/sources/logg-2019-algorithm-appreciation 4. Dzindolet MT, Peterson SA, Pomranky RA, Pierce LG, Beck HP (2003). The role of trust in automation reliance. International Journal of Human-Computer Studies 58(6):697–718. https://doi.org/10.1016/S1071-5819(03)00038-7 - Institutions: Affiliation not yet confirmed - Our summary: https://tre-up.com/research/sources/dzindolet-2003-trust-reliance 5. Buçinca Z, Malaya MB, Gajos KZ (2021). To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making. Proceedings of the ACM on Human-Computer Interaction 5(CSCW1). https://doi.org/10.1145/3449287 - Institutions: Harvard University (United States) - Our summary: https://tre-up.com/research/sources/bucinca-2021-cognitive-forcing 6. Kizilcec RF (2016). How Much Information? Effects of Transparency on Trust in an Algorithmic Interface. Proceedings of CHI 2016, 2390–2395. https://doi.org/10.1145/2858036.2858402 - Institutions: Stanford University (United States) - Our summary: https://tre-up.com/research/sources/kizilcec-2016-transparency-trust ## Related notes - [How should an AI say it isn't sure?](https://tre-up.com/research/how-should-an-ai-say-it-isnt-sure.md) - [Why do I push back when an app tells me what to do?](https://tre-up.com/research/why-do-i-push-back-when-an-app-tells-me-what-to-do.md) - [How can an app help me if it doesn't know me yet?](https://tre-up.com/research/how-can-an-app-help-me-if-it-doesnt-know-me-yet.md) --- © 2026 Tre-Up. 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