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౦ژେֶʛഅ৔ઇ೫ ਓؒத৺ͷ ҙࢥܾఆࢧԉ"* ೥౓ਓ޻஌ೳֶձશࠃେձ +4"* νϡʔτϦΞϧ 

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ਓ "*ʹΑΔҙࢥܾఆͷਫ਼౓޲্ʹ͸ "*΁ͷཔΓํ͕ॏཁ "*͕൑அΛఏࣔ ਓ͕ؒϨϏϡʔ ਓ͕ؒ࠷ऴ൑அ "*͕ޡ౴ˠݟಀͯ͠࠾༻😖 "*͕ਖ਼౴ˠޡ্ͬͯॻ͖😖

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ຊνϡʔτϦΞϧͷߏ੒ ͸͡Ίʹ  ͍ͭ"*ʹཔΓɺ͍ͭୀ͚Δ͔  આ໌ͱ֬৴౓͸ɺཔΓํΛͲ͏ม͑Δ͔  "*ॿݴΛ࠶ߟͷ͖͔͚ͬʹ͢Δ  --.ΛͲ͏͍ٙɺͲ͏໾ׂ͚ͮΔ͔  ෳ਺ͷॿݴ͸ɺࢹ఺ʹ΋ѹྗʹ΋ͳΔ 3

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͍ͭ"*ʹཔΓɺ͍ͭୀ͚Δ͔

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ਓ "*ͷҙࢥܾఆ͸ࣗಈతʹ͸ྑ͘ͳΒͳ͍ ͍ͭ"*ʹཔΓɺ͍ͭୀ͚Δ͔ ˔ ࿦จɾ࣮ݧΛର৅ʹͨ͠ϝλ෼ੳɿҙࢥܾఆਫ਼౓͸ɺ ˙ ਓ "*͸ɺਓؒ୯ಠΑΓ͸ྑ͍͜ͱ͕ଟ͍ ˙ ਓ "*͕ɺਓؒ୯ಠɾ"*୯ಠͷྑ͍ํΛ௒͑Δͱ͸ݶΒͳ͍ 5 Vaccaro et al. When combinations of humans and AI are useful. Nature Human Behaviour 2024. "*͕ਓؒΑΓߴਫ਼౓ͳ৔໘Ͱɺ "*ͷ൑அΛ࠾༻Ͱ͖͍ͯͳ͍

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ਓ "*Ͱਫ਼౓্͕͕ͬͯ΋ద੾ʹཔΕ͍ͯΔͱ͸ݶΒͳ͍ ͍ͭ"*ʹཔΓɺ͍ͭୀ͚Δ͔ ˔ ࠶൜༧ଌͱϩʔϯ৹ࠪΛ୊ࡐʹɺ"*൑அͷఏࣔํ๏Λม࣮͑ͯݧ ˙ ਓ "*͸ਓؒ୯ಠΑΓ΋ߴਫ਼౓ʢ"*୯ಠΑΓ͸௿ਫ਼౓ʣ ˙ ͕ͩʮࣗ෼΍"*͕ͲΕ͘Β͍౰͍ͨͬͯΔ͔ʯ ʮ͍ͭ"*ʹཔΔ΂͖͔ʯΛࢀՃऀ͸೺ѲͰ͖͍ͯͳ͔ͬͨ 6 Green and Chen. The principles and limits of algorithm-in-the-loop decision making. CSCW 2019. ਓؒ୯ಠ "*୯ಠ "* ਓؒ "* ਓؒʢ"*༧ଌΛॳظೖྗʹઃఆʣ "* ਓؒ ʢਓؒͷ༧ଌͷޙʹ"*ఏࣔʣ "* ਓؒʢઆ໌෇͖ʣ "* ਓؒʢճ౴ޙʹਖ਼ղఏࣔʣ ༗ҙʹਖ਼ʢෛʣͷؔ܎ɺ༗ҙͳؔ܎ͳ͠ ࣗݾ൑அ΁ͷ ֬৴౓ͱ ࣮ࡍͷਖ਼౴཰ͷؔ܎ "*ੑೳ΁ͷ ओ؍తධՁͱ "*ਖ਼౴཰ͷؔ܎ "*͕ਖ਼͍࣌͠ʹ "*൑அΛ࠾༻Ͱ͖Δ͔

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จ຺৘ใʹجͮ͘൑அ͸"*΁ͷա৴ΛऑΊ͏Δ ͍ͭ"*ʹཔΓɺ͍ͭୀ͚Δ͔ 7 De-Arteaga et al. A case for humans-in-the-loop: Decisions in the presence of erroneous algorithmic scores. CHI 2020. "*͕༧ଌͨ͠ϦεΫείΞΛ ୲౰ऀʹఏࣔ ௐࠪෆཁPS ཁௐࠪΛ൑அ "*είΞ͕Ҏ্ͷ৔߹ ʮௐࠪෆཁʯ൑அʹ͸ ্࢘ͷڐՄ͕ඞཁ ˔ ࣇಐٮ଴ɾωάϨΫτ௨ใͷεΫϦʔχϯά"*ͷޡ࡞ಈΛར༻͠ɺ "*ʹର͢Δઐ໳Ոʢࣇಐ෱ࢱ୲౰ऀʣͷৼΔ෣͍Λ෼ੳ ˔ ߴϦεΫͱޡදࣔɿ"*ʹैΘͣʮௐࠪෆཁʯͱ൑அ͢Δ܏޲ɺ ௿ϦεΫͱޡදࣔɿඞཁͳέʔεͰʮཁௐࠪʯͱਖ਼͘͠൑அ ˙ ઐ໳Ո͸"*൑அҎ֎ͷจ຺৘ใ ʢ௨ใ಺༰΍ߦ੓ه࿥ʣΛ ༻͍ͯ൑அ͍ͯͨ͠

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ࣦഊ৚݅Λֶͼ΍͍͢"*ͷํ͕ਓ "*ʹ͸ద͍ͯ͠Δ ͍ͭ"*ʹཔΓɺ͍ͭୀ͚Δ͔ ˔ ਓؒʹ"*ͷࣦഊ৚݅Λֶ͹ͤΔήʔϜΛઃܭ ˔ ෆྑ඼൑ఆ͕୊ࡐɻ੡඼ͷಛ௃͕༩͑ΒΕɺ ࢀՃऀ͸ҎԼͷ͍ͣΕ͔Λબ୒ ˙ "*ʹ೚ͤΔɿਖ਼౴ͳΒใुɾޡ౴ͳΒଛࣦ ˙ ࣗ෼Ͱ൑அɿใु΋ଛࣦ΋ͳ͠ ˔ ϥ΢ϯυ͕ਐΉͱਓؒ͸"*ͷࣦഊ৚݅Λֶͼɺ "*͕ਖ਼౴ͷ࣌ʹ೚ͤɺޡ౴ͷ࣌ʹୀ͚ΔΑ͏ʹͳͬͨ ˔ ಉਫ਼౓ͳΒɺ୯७ɾ௿࣍ݩɾҰ؏ͨ͠൑அͷ"*΄Ͳ ਓ "*ͷྦྷੵใु͕޲্ 8 Bansal et al. Beyond accuracy: The role of mental models in human-AI team performance. HCOMP 2019.

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આ໌͕͋Ε͹ద੾ͳཔΓํΛֶ΂Δͷ͔ʁ ͍ͭ"*ʹཔΓɺ͍ͭୀ͚Δ͔ ˔ ༿ը૾෼ྨλεΫͰɺਓ͕"*൑அΛ࠾༻͢Δ͔Λෳ਺ͷ"*આ໌Ͱൺֱ ˔ આ໌͕͋Δͱɺ"*͕ਖ਼౴ͷ࣌ʹ࠾༻͠ɺޡ౴ͷ࣌ʹڋ൱͢Δ܏޲૿ ˙ ը૾આ໌͕ޮՌେɻղऍ͠΍͍ۙ͢๣ࣄྫ͕ɺݸผࣄྫͰ"*࠾༻ ͷ൑அΛॿ͚ͨՄೳੑ͕͋Δ 9 Yang et al. How does visual explanation foster appropriate trust in machine learning? IUI 2020. ۙ๣ࣄྫΛը૾Ͱ දݱ͢Δઆ໌ ۙ๣ࣄྫΛಛ௃ϕΫτϧͰ දݱ͢Δઆ໌ "*൑அ ܇࿅σʔλதͷۙ๣ࣄྫ

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0WFSSFMJBODFͱVOEFSSFMJBODF ͍ͭ"*ʹཔΓɺ͍ͭୀ͚Δ͔ 10 "*ͷ൑அ ਓͷ࠷ऴ൑அ ˓ ˓ ˓ ʷ ʷ ˓ ʷ ʷ ˓ਖ਼౴ɺʷޡ౴ 6OEFSSFMJBODF "*͕ਖ਼౴ͷ࣌ɺ"*൑அΛڋ൱͢Δׂ߹ 0WFSSFMJBODF "*͕ޡ౴ͷ࣌ɺ"*൑அΛ࠾༻͢Δׂ߹

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"QQSPQSJBUFSFMJBODF 3"*3 343 ͍ͭ"*ʹཔΓɺ͍ͭୀ͚Δ͔ 11 Schemmer et al. Appropriate reliance on AI advice: Conceptualization and the effect of explanations. IUI 2023. ਓͷॳظ൑அ "*ͷ൑அ ਓͷ࠷ऴ൑அ ʷ ˓ ˓ ʷ ˓ ʷ ʷ ʷ ˓ ʷ ʷ ʷ ਓͷॳظ൑அ "*ͷ൑அ ਓͷ࠷ऴ൑அ ˓ ˓ ˓ ˓ ˓ ʷ ˓ ʷ ˓ ˓ ʷ ʷ 3"*3 3FMBUJWF"*SFMJBODF  ਓؒͷॳظ൑அ͕ޡ౴Ͱɺ"*͕ਖ਼౴ͷͱ͖ɺ"*൑அΛ࠾༻͢Δׂ߹

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"QQSPQSJBUFSFMJBODF 3"*3 343 ͍ͭ"*ʹཔΓɺ͍ͭୀ͚Δ͔ 12 Schemmer et al. Appropriate reliance on AI advice: Conceptualization and the effect of explanations. IUI 2023. ਓͷॳظ൑அ "*ͷ൑அ ਓͷ࠷ऴ൑அ ʷ ˓ ˓ ʷ ˓ ʷ ʷ ʷ ˓ ʷ ʷ ʷ ਓͷॳظ൑அ "*ͷ൑அ ਓͷ࠷ऴ൑அ ˓ ˓ ˓ ˓ ˓ ʷ ˓ ʷ ˓ ˓ ʷ ʷ 343 3FMBUJWFTFMGSFMJBODF  ਓؒͷॳظ൑அ͕ਖ਼౴Ͱɺ"*͕ޡ౴ͷͱ͖ɺ"*൑அΛڋ൱͢Δׂ߹

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આ໌͸ɺਖ਼͍͠"*ʹཔΔߦಈ͚ͩΛଅ͢ ͍ͭ"*ʹཔΓɺ͍ͭୀ͚Δ͔ ˔ ِϗςϧϨϏϡʔͷݕग़λεΫͰઆ໌ͷӨڹΛݕূ ˙ -*.&ܕͷ୯ޠϋΠϥΠτઆ໌ ˔ આ໌͸3"*3Λ༗ҙʹߴΊΔ͕343͸มԽ͠ͳ͍ ˙ ʮઆ໌͕ɺޡͬͨ"*Λڋ൱͢ΔͷΛॿ͚Δʯͱ͸ݴ͑ͳ͍ ˔ "*΁ͷओ؍త৴པ͕ߴ͍ͱɺ3"*3͕ߴ·Δ͕343͸Լ͕Δ ˙ ओ؍త৴པ͸ਖ਼͍͠"*ͷ ࠾༻Λॿ͚Δ͕ ޡͬͨ"*΁ͷա৴΋ ট͖͏Δ 13 Schemmer et al. Appropriate reliance on AI advice: Conceptualization and the effect of explanations. IUI 2023.

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આ໌ͱ֬৴౓͸ɺཔΓํΛͲ͏ม͑Δ͔

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Ϟσϧͷղऍੑ͸ɺద੾ͳґଘΛଅ͢ͱ͸ݶΒͳ͍ આ໌ͱ֬৴౓͸ɺཔΓํΛͲ͏ม͑Δ͔ ˔ ΞύʔτՁ֨༧ଌͰͷ࣮ݧɻઆ໌ͱͯ͠ઢܗճؼϞσϧͷ ܎਺ΛݟͤΔ৔߹ $MFBS ͱݟͤͳ͍৔߹ #MBDLCPY Λൺֱ ˔ ม਺$MFBS৚݅ ˙ Ϟσϧ༧ଌΛ༧૝͢ΔλεΫ͸ߴਫ਼౓Ͱղ͚ͨ ˙ ਓ "*ͷਫ਼౓͸վળ͠ͳ͔ͬͨɻ"*͕ଥ౰ͳ࣌ʹ࠾༻͢Δߦಈʹ͸ ܨ͕Βͳ͔ͬͨ ˙ ֎Ε஋Ͱ͸ ޡͬͨϞσϧ༧ଌΛ ୀ͚ʹ͔ͬͨ͘ 15 Poursabzi-Sangdeh et al. Manipulating and measuring model interpretability. CHI 2021. $MFBS৚݅ɿ܎਺͕ݟ͑Δ #MBDLCPY৚݅ɿ܎਺͕ݟ͑ͳ͍

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આ໌͸ݕূࡐྉ͔ʁઆಘ͔ʁ આ໌ͱ֬৴౓͸ɺཔΓํΛͲ͏ม͑Δ͔ ˔ "*ͱਓ͕ؒಉ౳ͷਖ਼౴཰ͷλεΫʢײ৘෼ྨɺ࿦ཧਪ࿦ʣͰ࣮ݧ ˔ "*ͷ֬৴౓ͱઆ໌Λఏࣔ͢Δͱ ਓ "*ͷਖ਼౴཰͸ਓؒ୯ಠɾ"*୯ಠΛ্ճͬͨ ˙ "*ਖ਼౴࣌͸ਓ "*ͷਖ਼౴཰্͕͕͕ͬͨ"*ޡ౴࣌͸Լ͕ͬͨ ˙ આ໌͕ɺݕূͰ͸ͳ͘໡໨త௥ैΛଅͯ͠͠·ͬͨڪΕ 16 Bansal et al. Does the whole exceed its parts? The effect of AI explanations on complementary team performance. CHI 2021. ༧ଌࠜڌΛϋΠϥΠτ͢Δઆ໌

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આ໌͕ޮ͘ͷ͸ɺਓ͕ؒ"*ͷޡΓΛ௿࿑ྗͰݕূͰ͖Δ࣌ આ໌ͱ֬৴౓͸ɺཔΓํΛͲ͏ม͑Δ͔ ˔ ໎࿏ͷग़ޱΛ୒Ͱ౴͑ΔλεΫɻܦ࿏આ໌Λछྨ༻ҙ ˔ ೉қ౓͕ߴ͍໎࿏Ͱ͸ )JHIUMJHIUઆ໌ɿઆ໌ແ͠ΑΓ΋PWFSSFMJBODF͕վળ 8SJUUFOઆ໌ɿɹઆ໌ແ͠ʹରͯ͠༗ҙͳվળͳ͠ ˙ ݕূ͠΍͍͢આ໌͸PWFSSFMJBODFͷվળʹ໾ཱͭ ˔ ਖ਼౴࣌ͷϘʔφεֹΛ্͛ΔͱɺPWFSSFMJBODF͕͞Βʹվળ 17 Vasconcelos et al. Explanations can reduce overreliance on AI systems during decision-making. CSCW 2023. )JHIMJHIUઆ໌ 8SJUUFOઆ໌ "*͸࣌ʑนΛ͢Γൈ͚Δɻ IJHIMJHIUઆ໌ͩͱؾ͖ͮ΍͍͢

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ࣗݾա৴͸"*Λ࢖Θͳ͍ޡΓΛੜΉ આ໌ͱ֬৴౓͸ɺཔΓํΛͲ͏ม͑Δ͔ ˔ ࿦ཧਪ࿦λεΫΛ୊ࡐʹࣗݾೳྗ΁ͷࣗ৴͕ "*൑அͷ࠾༻ʹ༩͑ΔӨڹΛௐࠪ ˔ ࣗݾೳྗΛաେධՁͨ͠ਓ͸ɺ"*ͷਖ਼౴࣌Ͱ ΋"*Λ࠾༻ͤͣɺ࠷ऴ൑அͷਖ਼౴཰͕௿͔ͬͨ ˔ ੒੷΍"*ޡ౴ΛݟͤΔνϡʔτϦΞϧΛಋೖ ˙ ࣗݾೳྗ͕աେධՁͷਓɿBQQSPQSJBUF SFMJBODF͕վળ͢Δ܏޲ ˙ ࣗݾೳྗ͕աখධՁͷਓɿBQQSPQSJBUF SFMJBODF͕༗ҙʹѱԽɻ"*΁ͷʢޡͬͨʣ ༏ӽײͱ"*ճආ܏޲͕ੜͨ͡ 18 He et al. Knowing about knowing: An illusion of human competence can hinder appropriate reliance on AI systems. CHI 2023.

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ࣗ෼ͱ"*ͷͲͪΒΛٙ͏΂͖͔ આ໌ͱ֬৴౓͸ɺཔΓํΛͲ͏ม͑Δ͔ ˔ "*ͷ֬৴౓ʹՃ͑ͯࣗ਎ͷਖ਼౴ՄೳੑΛ༻͍པΓํͷௐ੔Λࢧԉ ˙ ࢀՃऀ͝ͱͷॳظ൑அσʔλΛ༻͍ͯਖ਼౴ՄೳੑΛਪఆ ˔ "*ͷ֬৴౓͸ߴ͍͕"*ޡ౴ͷέʔεͰ ਓ "*ͷਖ਼౴཰͕༗ҙʹ޲্ ˔ "*֬৴౓ࣗ਎ͷਖ਼౴Մೳੑͷ࣌ʹ "*൑அΛӅ͢ํ๏͸ 0WFSSFMJBODFվળ܏޲͕͋ͬͨ ˙ "*ͷ֬৴౓͸௿͍͕"*ਖ਼౴ͷέʔεͰ ਓ "*ͷਖ਼౴཰͕༗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 ਖ਼౴Մೳੑ "*ͷ ֬৴౓

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"*ॿݴΛ࠶ߟͷ͖͔͚ͬʹ͢Δ

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"*Λ͙͢ݟͤͳ͍ຎࡲ͸ɺա৴ΛऑΊ͏Δ "*ॿݴΛ࠶ߟͷ͖͔͚ͬʹ͢Δ ˔ ৯ࣄը૾౳͔Β୸ਫԽ෺࡟ݮʹޮ͘৯ࡐΛબͿλεΫ͕୊ࡐ ˔ "*൑அ΍આ໌Λ࠷ॳ͔ΒఏࣔͤͣʮຎࡲʯΛ༩͑Δํ๏Λ࣮૷ɿ ϘλϯΛԡͨ͠Βఏࣔɺॳظ൑அͷޙʹఏࣔɺඵޙʹఏࣔ ˔ ຎࡲ͋Γ͸"*΁ͷPWFSSFMJBODF Λ཈͑੒੷͕༗ҙʹվળ ˔ Ұํɺຎࡲ͋Γ͸ɺ ओ؍త৴པɾ޷·͠͞ΛߴΊΔ ઃܭͱ͸ݶΒͣɺೝ஌తෛ୲Λ ൐͏ 21 Bucinca et al. To trust or to think: Cognitive forcing functions can reduce overreliance on AI in AI-assisted decision- making. CSCW 2021.

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࠶ߟհೖ͸ɺݱ৔ͷޮ཰ੑͱিಥ͢Δ "*ॿݴΛ࠶ߟͷ͖͔͚ͬʹ͢Δ ˔ ౶೘ප໢ບ঱ͷεΫϦʔχϯά"*͕ҟৗͳ͠ͱ༧ଌͨ͠ ৔߹ɺ؟Պҩͷ֬ೝ͕࣌ؒ୹͘ͳΔόΠΞε͕൑໌ ˔ όΠΞεରࡦʹਓͷॳظ൑அΛಋೖ ˙ ॳظ൑அˠ"*ˠ࠷ऴ൑அɿ "*ʹΑΔޮ཰ԽΛ્֐͢Δͱෆධ ˙ ॳظ൑அ͕ҟৗͳ͠ˠ"*͕൓ൃ͢Δ৔߹ʹਓʹ࠶֬ೝ Λґཔɿ޷ධ͕ͩʮ"*ʹ؂ಜ͞Ε͍ͯΔʯͱෆշʹײ ͡Δਓ΋͍ͨ ˔ ߴ͍࡞ۀྔ͕ٻΊΒΕΔຊ୊ࡐͰ͸όΠΞεͷةݥੑ͸ ೝࣝ͞Ε͍͕ͯͨɺͦΕΑΓ΋ޮ཰ੑ͕ॏࢹ͞Ε͍ͯͨ 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

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આ໌Λ͋͑ͯ࡟Δͱɺ"*΁ͷ௥ै͸Լ͕Δ "*ॿݴΛ࠶ߟͷ͖͔͚ͬʹ͢Δ ˔ ਓʹߟ͑ͤ͞ΔͨΊʹʮ"*આ໌Λ෦෼తʹݟͤΔʯख๏ΛධՁ ˙ ୊ࡐɿ࠷୹ܦ࿏ίετͷճ౴ɺจষதͷޡΓ਺ͷճ౴ ˔ ෦෼తͳઆ໌͸׬શͳઆ໌ͱൺֱͯ͠ɺ"*ਖ਼౴࣌΋ޡ౴࣌΋ "*ͷ࠾༻཰Λ༗ҙʹԼ͛ͨ ˙ PWFSSFMJBODF͸཈͑ΒΕ͕ͨVOEFSSFMJBODF͕ੜͨ͡ 23 DeJong et al. Cognitive forcing for better decision-making: Reducing overreliance on AI systems through partial explanations. CSCW 2025. ෦෼తͳઆ໌ ׬શͳઆ໌ ෦෼తͳઆ໌ ׬શͳઆ໌

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આ໌Λ໰͍ʹม͑ΔͱɺओுΛݕূ͠΍͘͢ͳΔ "*ॿݴΛ࠶ߟͷ͖͔͚ͬʹ͢Δ ˔ ʮ"*આ໌จΛ໰͍ʹม͑Δʯํ๏ʢ࣭໰ܕʣΛఏҊ ˙ ୊ࡐɿ࿦૪తτϐοΫͷจষΛಡΈɺ࿦ཧతଥ౰ੑΛ൑அ ˔ ࣭໰ܕʹΑΓɺਖ਼౴཰͕༗ҙʹ޲্ͨ͠ ˙ આ໌ܕ͸ʮ͜ͷ৘ใͰे෼ʯͱײͤ͡͞΍͘͢ɺ ࣭໰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

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ࣗ෼ͱ"*ͷࠜڌࠩ෼ΛৼΓฦΒͤΔ "*ॿݴΛ࠶ߟͷ͖͔͚ͬʹ͢Δ ˔ ॳظ൑அͷޙʹ"*આ໌Λݟ ͤΔʮߏ଄తͳৼΓฦΓʯ ˙ ࣗ෼ͱ"*ͷҧ͍Λ֬ೝ ˠ"*൑அɾઆ໌Ͱଥ౰ͳ ఺ɾෆࣗવͳ఺Λߟ͑Δ ˠॳظ൑அΛৼΓฦΓɺ ҡ࣋͢Δ͔"*࠾༻͢Δ͔ ΛܾΊͯཧ༝Λॻ͘ ˔ ਖ਼౴཰͕༗ҙʹ޲্ɺ PWFSSFMJBODF͕཈͑ΒΕͨ 25 Li et al. Guided reflection in AI-assisted decision-making: Effects on AI overreliance and decision accuracy. CHI 2026. ᶃ"*൑அͱઆ໌ΛݟΔ ᶄ"*൑அΛϨϏϡʔ ᶅॳظ൑அͷৼΓฦΓ ᶆཧ༝Λॻ͍ͯ࠷ऴ൑அ

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ਓͱ"*ͷෆҰகΛٞ࿦͠ɺ"*ͷ൑அج४Λௐ੔͢Δ "*ॿݴΛ࠶ߟͷ͖͔͚ͬʹ͢Δ ˔ ਓͱ"*͕֤ʑͷ൑அͱ൑அج४Λݟͯٞ࿦͢Δʢ--.Λ׆༻ʣˠਓͷ ओுͷڧ͞౳ʹج͖ͮ"*͕൑அج४Λௐ੔ˠਓʹఏࣔ͠ਓ͕࠷ऴ൑அ ˔ ӃࢼͷεΫϦʔχϯάΛ୊ࡐʹ࣮ݧɻ"*આ໌͚ͩΛݟͤΔ৔߹ͱൺֱ ͯ͠ਖ਼౴཰͕༗ҙʹ޲্ɻ0WFSSFMJBODFΛݮΒ͢͜ͱ͕Ͱ͖ͨ ˙ ҰํͰ"*ʹର͢Δओ؍త৴པײɺຬ଍౓͸௿Լͨ͠ 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"*͕൑அج४Λௐ੔͠൑அΛߋ৽ ᶆਓ͕࠷ऴ൑அ

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"*͸ਓؒͷ൑அج४Λݟ௚͢ڭࡐʹ΋ͳΔ "*ॿݴΛ࠶ߟͷ͖͔͚ͬʹ͢Δ ˔ ਓؒ୯ಠͰެฏͳ൑அ͕Ͱ͖ΔΑ͏ʹɺެฏϞσϧͱࣗ෼Λ໛฿ͨ͠ ʢෆެฏͳʣϞσϧͷઆ໌Λڭࡐͱͯ͠ఏࣔ ˔ ଟ͘ͷࢀՃऀͰ൑அͷެฏੑ ͕޲্ɺʮಛఆͷ৘ใ ͚ͩͰ൑அ͍ͯͨ͠ʯ౳ͷ ࠶ߟ΍ؾ͖ͮΛ༩͑ɺ ൑அج४ͷௐ੔Λଅͨ͠ 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

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--.ΛͲ͏͍ٙɺͲ͏໾ׂ͚ͮΔ͔

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--.ݕࡧ͸଎͍͕ɺޡ౴ʹؾ͖ͮʹ͍͘ --.ΛͲ͏͍ٙɺͲ͏໾ׂ͚ͮΔ͔ ˔ ैདྷܕݕࡧPS--.࣭໰Ԡ౴Λ׆༻ͯ͠ 467ͷੵࡌޮ཰Λൺֱ͢ΔλεΫ ˔ --.࣭໰Ԡ౴͸ޮ཰త͕ͩ--.͕ؒҧ͑Δ ೉λεΫͰਖ਼౴཰͕௿Լ ˙ --.͸ޡ৘ใΛग़͢৔໘͕͕͋ͬͨɺ ࢀՃऀͷओ؍త৴པ͸ैདྷܕͱಉ౳ ˙ ମݧશମ͸ैདྷܕݕࡧΑΓߴධՁ ˔ --.ͷτʔΫϯੜ੒֬཰ͷߴ௿ͷ৭෇͚Ͱ ೉λεΫͷਖ਼౴཰͕վળ 29 Spatharioti et al. Effects of LLM-based search on decision making: Speed, accuracy, and overreliance. CHI 2025. --.࣭໰Ԡ౴ ैདྷܕݕࡧ

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--.આ໌͸ɺݕূΑΓ΋ೲಘΛଅ͢ --.ΛͲ͏͍ٙɺͲ͏໾ׂ͚ͮΔ͔ ˔ ٬؍తࣄ࣮ʹؔ͢Δ୒໰୊Ͱͷ࣮ݧɻ --.ਖ਼ޡઆ໌༗ແιʔε༗ແͰൺֱ ˔ આ໌͕͋Δͱਖ਼ޡʹΑΒͣSFMJBODFΛߴΊͨ ˔ ιʔε͕͋ΔͱPWFSSFMJBODFΛԼ͛Δ܏޲ ˔ --.ޡ౴࣌ͷ࠷ऴਖ਼౴཰͸ ιʔεͷΈઆ໌ ιʔε ˙ આ໌͕ઌʹʮೲಘʯΛଅͨ͠Մೳੑ ˔ આ໌͕͋Δͱਖ਼ޡʹΑΒͣʮࠜڌ෇͚͕े෼ʯ ʮ൑அʹ໾ཱͭʯͱ͍͏ҹ৅Λ༩͑ͨ 30 Kim et al. Fostering appropriate reliance on large language models: The role of explanations, sources, and inconsistencies. CHI 2025. આ໌ ιʔε

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--.BHFOUͷߦಈϩά͸ɺਖ਼֬ੑͷอূʹ͸ͳΒͳ͍ --.ΛͲ͏͍ٙɺͲ͏໾ׂ͚ͮΔ͔ ˔ --.BHFOU׆༻ͷఆੑ෼ੳɻ ʮߴྸͷ਌଒ͷ݈߁Λࢧԉ͢ΔͨΊʹ ৘ใΛௐ΂ΔʯλεΫͰ࣮ݧɻࢀՃऀ ͸ςοΫاۀۈ຿ɺෳ਺஍Ҭɻ ˔ ࢀՃऀ͸ɺιʔεͷ਺΍छྨɺߦಈϩ άɺճ౴ͷ௕͞ɾจମ͕--.BHFOUʹ ର͢Δ֬৴౓ͷख͕͔Γͩͱޠͬͨ ˙ ͜ΕΒ͸࣮ࡍͷਖ਼֬ੑΛอূͤͣɺ BHFOU͕ޡ౴Ͱ΋ࢀՃऀ͕BHFOUग़ྗ ΁ͷߴ͍֬৴౓Λࣔ͢৔໘͕͋ͬͨ 31 Brachman et al. Building appropriate mental models: What users know and want to know about an agentic AI chatbot. IUI 2025. Ϣʔβͷ࣭໰ʹର͠ɺ ࣗ෼ͰݕࡧߦಈΛܾΊͯԠ౴͢Δ --.BHFOU

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--.ͷจମ͸಺༰ͱ͸ผʹ൑அΛಈ͔͢ --.ΛͲ͏͍ٙɺͲ͏໾ׂ͚ͮΔ͔ ˔ "*ॿݴͷτʔϯ͕ਓͷҙࢥܾఆʹ༩͑ΔӨڹΛௐࠪɻ ୊ࡐ͸өըਪનɺҙݟܗ੒ɺ࠶൜༧ଌɻ ˙ --.Λ༻͍ͯॿݴͷτʔϯΛ'PSNBM DBTVBM SPNBOUJD౳ʹม׵ ˔ ϢʔβଐੑผͷޮՌͷҧ͍͕ݟΒΕͨɻ ˙ ߴྸͳਓ΄Ͳ'PSNBMͳ τʔϯʹ௥ै͠΍͍͢ɻ ࠶൜༧ଌͰ͸ࣗ෼ͷ ࠷ऴ൑அ΁ͷ֬৴౓΋ ڧ·ͬͨ 32 Okoso et al. Do expressions change decisions? Exploring the impact of AI's explanation tone on decision-making. CHI 2025. ॿݴͷ಺༰͸ಉ͡Ͱ τʔϯ͚ͩม׵

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--.͸ཧ༝͚ͮΛ޿͛Δ૬खʹ΋ͳΔ --.ΛͲ͏͍ٙɺͲ͏໾ׂ͚ͮΔ͔ ˔ ౤ࢿϙʔτϑΥϦΦ࡞੒ࢧԉ"*Ͱ࣮ݧɻࢀՃऀ͸౤ࢿʹे෼ৄ͍͠ɻ ˙ 3FDPNNFOE"*౤ࢿ঎඼Λ௚઀ਪન ˙ &YUFOE"*ࢀՃऀ͸౤ࢿํ਑ͱཧ༝ΛࣗવݴޠͰॻ͘ɻ"*͕Ϩ Ϗϡʔ͠ɺࢀՃऀ͸ͦΕΛ౿·͑ͯ࠷ऴܾఆ ˔ ྆ํͷ"*Ͱɺ౤ࢿઌ͕Ұ෦ͷࠃ΍ۀछʹภΓʹ͘͘ͳͬͨɻಛʹ &YUFOE"*Ͱ͸ɺ౤ࢿઌͷࠃɾ஍Ҭ͕ΑΓ޿͕Δ܏޲͕͋ͬͨ ˔ ݁Ռ΁ͷຬ଍౓͸&YUFOE"*ͷํ͕ߴ͔ͬͨɻ 3FDPNNFOE"*͸৽͍͠ํ޲ੑΛ༩͑΍͘͢ɺೝ஌ෛՙ΋খ͔ͬͨ͞ 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.

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ूஂҙࢥܾఆʹհೖ͢Δ൓࿦"* --.ΛͲ͏͍ٙɺͲ͏໾ׂ͚ͮΔ͔ ˔ "*൑அΛ౿·͑ͯूஂͰٞ࿦͢Δ ৔໘Ͱ͸ɺଞऀ΁ͷಉௐ΍ ରཱճආͰ"*ͷޡΓΛݟಀ͢ڪΕ ˔ "*൑அ΍ଟ਺೿ҙݟʹ൓࿦͢Δ ٞ࿦հೖCPUl%FWJM`TBEWPDBUFz ΛఏҊ ˔ ࠶൜༧ଌΛ୊ࡐʹ࣮ݧɻٞ࿦հೖʹ ΑΓਫ਼౓͕޲্ɻಛʹ"*൑அ΁ͷ ൓࿦͕༗ޮɻࢀՃऀ͕ࠜڌͷ໌֬Խ΍ଞऀʹൃݴΛଅ༷͢ࢠ΋͋ͬͨɻ ˙ ͨͩ͠νʔϜϫʔΫ΍ࣗ෼ͨͪͷਖ਼͠͞΁ͷओ؍ධՁ͸ѱԽ 34 Chiang et al. Enhancing AI-assisted group decision making through LLM-powered devil's advocate. IUI 2024. 🔴☠ "*൑அ΍ଟ਺೿ҙݟʹ Ϙοτ͕൓࿦

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௚઀઀৮Λ୅ସ͢Δ"*୅หऀ --.ΛͲ͏͍ٙɺͲ͏໾ׂ͚ͮΔ͔ ˔ ҟͳΔഎܠͷूஂؒʢྫɿࠃ಺ֶੜWTཹֶੜʣͷٞ࿦͸ෆ҆Λ൐͏ ˔ ଟࢹ఺औಘΛଅͨ͢Ί಺ूஂٞ࿦ʹհೖ͢Δ֎ूஂͷ୅หऀ"*ΛఏҊ ˙ ֎ूஂͷΈͷٞ࿦ϩά౳Λ༻͍ͯ୅หऀ"*Λ࣮૷ ˔ ʮࠃࡍྈͰͷτϥϒϧճආͷͨΊͷ׆ಈܭըࡦఆʯʹ͍ͭͯ ࠃ಺ֶੜʢ಺ूஂʣ͕ཹֶੜʢ֎ूஂʣͷ୅หऀ"*ͱٞ࿦͢Δ࣮ݧ ˔ কདྷతͳ௚઀઀৮΁ͷෆ͕҆ ௿ݮ͞ΕΔ܏޲͕ݟΒΕͨ ˔ ·ͨɺࢀՃऀࣗ਎͕֎ूஂࢹ఺Λ औΓೖΕΔέʔεͱ୅หऀ"*ʹ ֎ूஂࢹ఺ΛҕͶΔέʔε͕͋ͬͨ 35 Hata et al. GroupEnvoy: A Conversational Agent Speaking for the Outgroup to Foster Intergroup Relations. CUI 2026 (accepted). +4"*ൃද <݄೔ ໦ ɺ04> ֎ूஂͷٞ࿦ϩά౳Λ༻͍ ୅หऀ"*Λ࣮૷ ಺ूஂ"*ͷٞ࿦ʹ୅หऀ"*͕ࢀՃ

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ෳ਺ͷॿݴ͸ɺࢹ఺ʹ΋ѹྗʹ΋ͳΔ

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4FDPOEPQJOJPO͸"*௥ैΛݮΒ͕͢ਫ਼౓͸্͛ͳ͍ ෳ਺ͷॿݴ͸ɺࢹ఺ʹ΋ѹྗʹ΋ͳΔ ˔ "*൑அʹՃ͑ͯଞऀPSଞ"*ͷ൑அ TFDPOEPQJOJPO Λఏࣔ͢Δ৔߹ ΛөըϨϏϡʔͷײ৘ۃੑ൑ఆΛ୊ࡐ࣮ͯ͠ݧ ˔ ଞऀ൑அͷఏࣔ͸ɺ"*΁ͷSFMJBODFΛ༗ҙʹԼ͛ͨɻ ಛʹ"*ͱଞऀ͕ରཱ͢Δͱɺେ͖͘Լ͕ͬͨ ˙ "*͕ਖ਼͍࣌͠΋"*ʹ௥ै͠ͳ͍ ͨΊਖ਼౴཰͸վળ͠ͳ͔ͬͨ ˙ ॴཁ࣌ؒ͸૿Ճͨ͠ ˔ ଞऀ൑அΛʮଞ"*ͷ൑அʯͱఏࣔ ͯ͠΋ɺޮՌ͸มΘΒͳ͔ͬͨ 37 Lu et al. Does more advice help? The effects of second opinions in AI-assisted decision making. CSCW 2024. "*൑அ ଞऀͷ൑அ

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ಉ͡ਫ਼౓Ͱ΋ਓؒͷॿݴ͕ॏࢹ͞Ε΍͍͢ ෳ਺ͷॿݴ͸ɺࢹ఺ʹ΋ѹྗʹ΋ͳΔ ˔ ਓҎ্ͷਓؒBOEPS"*͕ॿݴ͢Δ ৔߹ΛՍۭͷҩྍ਍அͰ࣮ݧ ˔ ਓؒͱ"*͕ಉ౳ͷਫ਼౓ͳΒ ਓ͕ؒ঱ঢ়৘ใΛࢧ࣋ͨ࣌͠ͷํ ͕ɺ"*͕ࢧ࣋ͨ࣌͠ΑΓ൑அʹ൓ө ͞Ε΍͔ͬͨ͢ ˔ ॿݴऀʹΑͬͯਫ਼౓͕ҟͳΔͱ͖͸ ਓ͔ؒ"*͔ͷӨڹ͸খ͍͞ 38 Zhong et al. Drivers and influence of social conformity on decision making in human-AI teams. Scientific Reports 2026. ॿݴऀͱͦͷਫ਼౓ ॿݴऀͷ൑அ

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"*ͷ਺ΑΓ߹ҙ౓ͷํ͕௥ैʹӨڹ ෳ਺ͷॿݴ͸ɺࢹ఺ʹ΋ѹྗʹ΋ͳΔ ˔ ෳ਺"*͕ॿݴ͢Δ৔໘Ͱ"*ͷ਺  ͱ"*಺߹ҙ౓ͷӨڹΛௐࠪɻ ೥ऩ༧ଌɺ࠶൜༧ଌɺσʔτ༧ଌ͕୊ࡐɻ--.ੜ੒ͷ"*આ໌΋෇༩ɻ ˔ Ұ෦λεΫͰ"*͕"*ΑΓ΋༗ҙʹਖ਼౴཰ΛߴΊͨ ˙ "*Ͱ͸͜ͷޮՌ͸ͳ͔ͬͨ ˔ "*਺ͷ૿Ճ͸"*ଟ਺ҙݟ΁ͷ 4XJUDI཰ʹ͸Өڹ͠ͳ͔ͬͨɺ "*߹ҙ౓͕ߴ͍΄Ͳ"*ଟ਺ҙݟ ΁ͷ4XJUDI཰্͕͕ͬͨ ˙ ࢀՃऀ͔Β͸ʮ"*ಉ͕࢜ҙݟ ෆҰகͩͱࠞཚͨ͠ʯͱ੠͕͋Γɺೝ஌ෛՙΛ΋ͨΒ͢ڪΕ 39 Tsuchiya et al. More isn't always better: Balancing decision accuracy and conformity pressures in multi-AI advice. CHI 2026. +4"*ൃද <݄೔ ໦ ɺ(4>

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ෳ਺ΤʔδΣϯτʹΑΔଟࢹ఺୳ࡧ ෳ਺ͷॿݴ͸ɺࢹ఺ʹ΋ѹྗʹ΋ͳΔ ˔ ෳ਺"*Λଟ਺ܾͰ͸ͳ͘ଟࢹ఺୳ࡧʹ࢖͏γεςϜl$IPJDF.BUFTz ˙ ʮझຯ༻ͷ৽͍͠ΧϝϥΛ୳͍ͯ͠Δʯͷ࣭໰ʹରͯ͠ʮը࣭ॏ ࢹʯʮॳ৺ऀ޲͚ʯʮܰྔॏࢹʯ౳ͷΤʔδΣϯτ͕༻ҙ͞ΕΔ ˙ ΤʔδΣϯτ܈ͱϢʔβͷ ର࿩Ͱग़ͯ͘Δ0QUJPOT΍ $SJUFSJBΛཤྺͱͯ͠ه࿥ ˙ Ϣʔβ͸ཤྺ͔Βॏཁͳ΋ͷΛ 1SFGFSFODFTQBDFͱͯ͠ه࿥ɺ ࠷ऴ൑அ ˔ ࣗಈԽෳ਺ΤʔδΣϯτΑΓ΋ ޿͍୳ࡧ͕ՄೳͱߴධՁ 40 Park et al. ChoiceMates: Supporting unfamiliar online decision-making with multi-agent conversational interactions. IUI 2026. ෳ਺"*͕ҟͳΔࢹ఺Λ୲͏ ཤྺ͔Βॏཁͳ΋ͷΛ Ϣʔβ͕நग़ ෳ਺"*ͱάϧʔϓνϟοτ

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·ͱΊ

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5BLFIPNFNFTTBHFT ·ͱΊ ˔ "*ͷਫ਼౓͕ߴͯ͘΋ਓ "*ҙࢥܾఆͷਫ਼౓͕ߴ͘ͳΔͱ͸ݶΒͳ͍ ˔ આ໌΍֬৴౓ΛʮೲಘʯͰ͸ͳ͘ʮݕূʯʹ࢖͑ΔΑ͏ʹ͢΂͖ ˔ --.΋͞ΒͳΔʮೲಘʯΛੜΉڪΕ͕͋Δɻ ख़ߟΛࢧԉ͢ΔͨΊͷ࢖͍ํ͕ٻΊΒΕΔ 42