"*͕ɺਓؒ୯ಠɾ"*୯ಠͷྑ͍ํΛ͑ΔͱݶΒͳ͍ 5 Vaccaro et al. When combinations of humans and AI are useful. Nature Human Behaviour 2024. "*͕ਓؒΑΓߴਫ਼ͳ໘Ͱɺ "*ͷஅΛ࠾༻Ͱ͖͍ͯͳ͍
˙ ࣗͰஅɿใुଛࣦͳ͠ ˔ ϥϯυ͕ਐΉͱਓؒ"*ͷࣦഊ݅Λֶͼɺ "*͕ਖ਼ͷ࣌ʹͤɺޡͷ࣌ʹୀ͚ΔΑ͏ʹͳͬͨ ˔ ಉਫ਼ͳΒɺ୯७ɾ࣍ݩɾҰ؏ͨ͠அͷ"*΄Ͳ ਓ "*ͷྦྷੵใु্͕ 8 Bansal et al. Beyond accuracy: The role of mental models in human-AI team performance. HCOMP 2019.
˔ "*ͷओ؍త৴པ͕ߴ͍ͱɺ3"*3͕ߴ·Δ͕343Լ͕Δ ˙ ओ؍త৴པਖ਼͍͠"*ͷ ࠾༻Λॿ͚Δ͕ ޡͬͨ"*ͷա৴ ট͖͏Δ 13 Schemmer et al. Appropriate reliance on AI advice: Conceptualization and the effect of explanations. IUI 2023.
"*ͷਖ਼্͕͕͕ͬͨ"*ޡ࣌Լ͕ͬͨ ˙ આ໌͕ɺݕূͰͳ͘తैΛଅͯ͠͠·ͬͨڪΕ 16 Bansal et al. Does the whole exceed its parts? The effect of AI explanations on complementary team performance. CHI 2021. ༧ଌࠜڌΛϋΠϥΠτ͢Δઆ໌
˔ ਖ਼࣌ͷϘʔφεֹΛ্͛ΔͱɺPWFSSFMJBODF͕͞Βʹվળ 17 Vasconcelos et al. Explanations can reduce overreliance on AI systems during decision-making. CSCW 2023. )JHIMJHIUઆ໌ 8SJUUFOઆ໌ "*࣌ʑนΛ͢Γൈ͚Δɻ IJHIMJHIUઆ໌ͩͱؾ͖͍ͮ͢
˙ ࣗݾೳྗ͕աେධՁͷਓɿBQQSPQSJBUF SFMJBODF͕վળ͢Δ ˙ ࣗݾೳྗ͕աখධՁͷਓɿBQQSPQSJBUF SFMJBODF͕༗ҙʹѱԽɻ"*ͷʢޡͬͨʣ ༏ӽײͱ"*ճආ͕ੜͨ͡ 18 He et al. Knowing about knowing: An illusion of human competence can hinder appropriate reliance on AI systems. CHI 2023.
˔ "*֬৴ ࣗͷਖ਼Մೳੑͷ࣌ʹ "*அΛӅ͢ํ๏ 0WFSSFMJBODFվળ͕͋ͬͨ ˙ "*ͷ֬৴͍͕"*ਖ਼ͷέʔεͰ ਓ "*ͷਖ਼͕༗ҙʹѱԽ 19 Ma et al. Who should I trust: AI or myself? Leveraging human and AI correctness likelihood to promote appropriate trust in AI-assisted decision-making. CHI 2023. ࣗͷ ਖ਼Մೳੑ "*ͷ ֬৴
˔ Ұํɺຎࡲ͋Γɺ ओ؍త৴པɾ·͠͞ΛߴΊΔ ઃܭͱݶΒͣɺೝతෛ୲Λ ͏ 21 Bucinca et al. To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision- making. CSCW 2021.
˙ ॳظஅ͕ҟৗͳ͠ˠ"*͕ൃ͢Δ߹ʹਓʹ࠶֬ೝ Λґཔɿධ͕ͩʮ"*ʹಜ͞Ε͍ͯΔʯͱෆշʹײ ͡Δਓ͍ͨ ˔ ߴ͍࡞ۀྔ͕ٻΊΒΕΔຊࡐͰόΠΞεͷةݥੑ ೝࣝ͞Ε͍͕ͯͨɺͦΕΑΓޮੑ͕ॏࢹ͞Ε͍ͯͨ 22 Bach et al. “If I had all the time in the world ” : Ophthalmologists' perceptions of anchoring bias mitigation in clinical AI support. CHI 2023. ɿ ҟৗͳ͠ ԫɿ গྔͷ පมݕग़ ɿ ΑΓଟ͘ͷ පมݕग़
PWFSSFMJBODF͑ΒΕ͕ͨVOEFSSFMJBODF͕ੜͨ͡ 23 DeJong et al. Cognitive forcing for better decision-making: Reducing overreliance on AI systems through partial explanations. CSCW 2025. ෦తͳઆ໌ શͳઆ໌ ෦తͳઆ໌ શͳઆ໌
࣭ܕ΄Ͳ࠶ݕ౼Λଅ͞ͳ͔ͬͨՄೳੑ͕͋Δ 24 Danry et al. Don ’ t just tell me, ask me: AI systems that intelligently frame explanations as questions improve human logical discernment accuracy over causal AI explanations. CHI 2023. ࣭ܕ આ໌ܕ
ҡ࣋͢Δ͔"*࠾༻͢Δ͔ ΛܾΊͯཧ༝Λॻ͘ ˔ ਖ਼͕༗ҙʹ্ɺ PWFSSFMJBODF͕͑ΒΕͨ 25 Li et al. Guided reflection in AI-assisted decision-making: Effects on AI overreliance and decision accuracy. CHI 2026. ᶃ"*அͱઆ໌ΛݟΔ ᶄ"*அΛϨϏϡʔ ᶅॳظஅͷৼΓฦΓ ᶆཧ༝Λॻ͍ͯ࠷ऴஅ
26 Ma et al. Towards human-AI deliberation: Design and evaluation of LLM-empowered deliberative AI for AI-assisted decision-making. CHI 2025. ᶃਓͱ"*͕அͱઆ໌Λఏࣔ ᶄਓͱ"*͕ٞ ᶅ"*͕அج४Λௐ͠அΛߋ৽ ᶆਓ͕࠷ऴஅ
அج४ͷௐΛଅͨ͠ 27 Yang et al. Fair machine guidance to enhance fair decision making in biased people. CHI 2024. Example of an appropriate response. You predicted that the person below would have a LOW INCOME. To be fair, you should have predicted a HIGH INCOME. Age: 50, Gender: Male Race: Asian Workclass: Self-employed Education: Professional school #years of education: 15 Marital status: Married Relationship: Husband Occupation: Professional specialty Working time: 50h/week Native country: Philippines Your criteria vs. fair criteria. Age: 50, Gender: Male Race: Asian Workclass: Self-employed Education: Professional school #years of education: 15 Marital status: Married Relationship: Husband Occupation: Professional specialty Working time: 50h/week Native country: Philippines Age: 50, Gender: Male Race: Asian Workclass: Self-employed Education: Professional school #years of education: 15 Marital status: Married Relationship: Husband Occupation: Professional specialty Working time: 50h/week Native country: Philippines Your criteria Fair criteria HIGH INCOME LOW INCOME The left column of the figure shows your decision criteria, as estimated from your answers using AI. You tend to predict a high income when the information is blue (or when the value of blue information is high). You tend to predict low income when the information is red (or when the value of red information is high). The right column of the figure shows fair decision criteria, as estimated by Fair AI. Your decision will be fairer if you follow these criteria. To be fair, you should predict a high income when the information is blue (or when the value of blue information is high). To be fair, you should predict a low income when the information is red (or when the value of red information is high). B "Fair AI," which simulates what your judgment would look like if it were fair. ࢀՃऀΛ฿ͨ͠ ෆެฏϞσϧͷઆ໌ ࢀՃऀ͕ެฏͳ߹Λ฿ͨ͠ ެฏϞσϧͷઆ໌
ࢀՃऀͷओ؍త৴པैདྷܕͱಉ ˙ ମݧશମैདྷܕݕࡧΑΓߴධՁ ˔ --.ͷτʔΫϯੜ֬ͷߴͷ৭͚Ͱ λεΫͷਖ਼͕վળ 29 Spatharioti et al. Effects of LLM-based search on decision making: Speed, accuracy, and overreliance. CHI 2025. --.࣭Ԡ ैདྷܕݕࡧ
--.ޡ࣌ͷ࠷ऴਖ਼ ιʔεͷΈ આ໌ ιʔε ˙ આ໌͕ઌʹʮೲಘʯΛଅͨ͠Մೳੑ ˔ આ໌͕͋Δͱਖ਼ޡʹΑΒͣʮࠜڌ͚͕ेʯ ʮஅʹཱͭʯͱ͍͏ҹΛ༩͑ͨ 30 Kim et al. Fostering appropriate reliance on large language models: The role of explanations, sources, and inconsistencies. CHI 2025. આ໌ ιʔε
ର͢Δ֬৴ͷख͕͔Γͩͱޠͬͨ ˙ ͜ΕΒ࣮ࡍͷਖ਼֬ੑΛอূͤͣɺ BHFOU͕ޡͰࢀՃऀ͕BHFOUग़ྗ ͷߴ͍֬৴Λࣔ͢໘͕͋ͬͨ 31 Brachman et al. Building appropriate mental models: What users know and want to know about an agentic AI chatbot. IUI 2025. Ϣʔβͷ࣭ʹର͠ɺ ࣗͰݕࡧߦಈΛܾΊͯԠ͢Δ --.BHFOU
ϢʔβଐੑผͷޮՌͷҧ͍͕ݟΒΕͨɻ ˙ ߴྸͳਓ΄Ͳ'PSNBMͳ τʔϯʹै͍͢͠ɻ ࠶൜༧ଌͰࣗͷ ࠷ऴஅͷ֬৴ ڧ·ͬͨ 32 Okoso et al. Do expressions change decisions? Exploring the impact of AI's explanation tone on decision-making. CHI 2025. ॿݴͷ༰ಉ͡Ͱ τʔϯ͚ͩม
྆ํͷ"*Ͱɺࢿઌ͕Ұ෦ͷࠃۀछʹภΓʹ͘͘ͳͬͨɻಛʹ &YUFOE"*ͰɺࢿઌͷࠃɾҬ͕ΑΓ͕Δ͕͋ͬͨ ˔ ݁Ռͷຬ&YUFOE"*ͷํ͕ߴ͔ͬͨɻ 3FDPNNFOE"*৽͍͠ํੑΛ༩͑͘͢ɺೝෛՙখ͔ͬͨ͞ 33 Reicherts et al. AI, help me think-but for myself: Assisting people in complex decision-making by providing different kinds of cognitive support. CHI 2025.
ࠃֶੜʢूஂʣ͕ཹֶੜʢ֎ूஂʣͷหऀ"*ͱٞ͢Δ࣮ݧ ˔ কདྷతͳ৮ͷෆ͕҆ ݮ͞ΕΔ͕ݟΒΕͨ ˔ ·ͨɺࢀՃऀ͕ࣗ֎ूஂࢹΛ औΓೖΕΔέʔεͱหऀ"*ʹ ֎ूஂࢹΛҕͶΔέʔε͕͋ͬͨ 35 Hata et al. GroupEnvoy: A Conversational Agent Speaking for the Outgroup to Foster Intergroup Relations. CUI 2026 (accepted). +4"*ൃද <݄ ɺ04> ֎ूஂͷٞϩάΛ༻͍ หऀ"*Λ࣮ ूஂ"*ͷٞʹหऀ"*͕ࢀՃ
˙ "*͕ਖ਼͍࣌͠"*ʹै͠ͳ͍ ͨΊਖ਼վળ͠ͳ͔ͬͨ ˙ ॴཁ࣌ؒ૿Ճͨ͠ ˔ ଞऀஅΛʮଞ"*ͷஅʯͱఏࣔ ͯ͠ɺޮՌมΘΒͳ͔ͬͨ 37 Lu et al. Does more advice help? The effects of second opinions in AI-assisted decision making. CSCW 2024. "*அ ଞऀͷஅ
˔ ॿݴऀʹΑͬͯਫ਼͕ҟͳΔͱ͖ ਓ͔ؒ"*͔ͷӨڹখ͍͞ 38 Zhong et al. Drivers and influence of social conformity on decision making in human-AI teams. Scientific Reports 2026. ॿݴऀͱͦͷਫ਼ ॿݴऀͷஅ
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