AIシステム開発のライフサイクルに人間の認知バイアスが与える影響

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1. "*γεςϜ։ൃͷϥΠϑαΠΫϧʹ ਓؒͷೝ஌όΠΞε͕༩͑ΔӨڹ ʙ"*Λͭ͘Δɾ͔ͭ͏ਓؒΛ஌Δʙ ஜ೾େֶҩֶҩྍܥ ߨࢣ ߳઒ཨಸ 4QFDJBM
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2. / 86  Take Home Message 00 ਺஋ʹͨ͠ॠؒʹҰਓา͖ͯ͠͠·͏΋ͷ͸ੈʹଟ͋͘Δɻ ͋͋ݴ͑͹ɺ͜͏ݴ͏ɻ
3. / 86  AIʹΑΔਓؒͷҙࢥܾఆͷࢧԉ 02 AI͸ਓؒͷ஌ੑΛɾɾɾ ௒ӽ͢Δ ࠶ݱ͢Δ ࢧԉɾ֦ு͢Δಓ۩ ൚༻ਓ޻஌ೳ ௒஌ೳ γϯΪϡϥϦςΟ "*ΞϥΠϝϯτ ҙࢥܾఆࢧԉ ߹ҙܗ੒ࢧԉ
4. / 86  AIʹΑΔਓؒͷҙࢥܾఆͷࢧԉ 02 AI͸ਓؒͷ஌ੑΛɾɾɾ ௒ӽ͢Δ ࠶ݱ͢Δ ࢧԉɾ֦ு͢Δಓ۩ uࣾձ͕ɺҙࢥܾఆऀͱͯ͠ͷ ੹೚ΛਓؒʹٻΊΔ෼໺ └ҩྍ਍அɺࡋ൑ɺ اۀਓࣄɺߦ੓ͳͲ uਓ͕ؒAIͷग़ྗʹج͍ͮͯ ೳಈతʹҙࢥܾఆ͢Δ෼໺ └޿ࠂɺαʔϏεఏڙͳͲ ൚༻ਓ޻஌ೳ ௒஌ೳ γϯΪϡϥϦςΟ "*ΞϥΠϝϯτ ҙࢥܾఆࢧԉ ߹ҙܗ੒ࢧԉ
5. / 86  AIʹΑΔਓؒͷҙࢥܾఆͷࢧԉ 02 [1] ྩ࿨̐೥౓਍ྍใुվఆʹ͍ͭͯʢ௨஌ʣ, อൃ 0304 ୈ 1 ߸, 2022 [2] Brennan, T., Dieterich, W., & Ehret, B. (2009). Evaluating the predictive validity of the COMPAS risk and needs assessment system. Criminal Justice and behavior, 36(1), 21-40. [3] https://www.darpa.mil/program/explainable-artificial-intelligence [4] https://www.robustintelligence.com/jp-whitepaper/ai- governance-whitepaper-1 [5] https://www.digiarc.aist.go.jp/publication/aiqm/ [13] Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., ... & McCulloch, P. (2022). Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nature medicine, 28(5), 924-933. [14] https://pubs.rsna.org/page/ai/blog/2022/09/ryai_editorsblog0928 [6] https://www.soumu.go.jp/menu_news/s-news/01tsushin06_02000277.html [7] https://www8.cao.go.jp/cstp/aigensoku.pdf [8] https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines- trustworthy-ai [9] https://standards.ieee.org/wp-content/uploads/import/documents/other/ead_v2.pdf [10] https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449 ࿨༁ɿ https://www.soumu.go.jp/main_content/000642217.pdf, 2019 [11] https://unesdoc.unesco.org/ark:/48223/pf0000385082 [12] https://www.un.org/en/ai-advisory-body ظ଴ͷߴ·Γɾ࣮Ԡ༻ͷ޿͕Γ ݒ೦΁ͷରԠ u ෯޿͍ର৅ ◇ ҩྍ ը૾਍அ"* ʙຊ๜อݥద༻ <> ◇ ࢘๏ $0.1"4
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6. / 86  AI։ൃϥΠϑαΠΫϧͷ֤ՕॴͰੜ͡ΔόΠΞε 02 Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Equity and access in algorithms, mechanisms, and optimization (pp. 1-9). ֶशɾਪ࿦ ධՁ Ϟσϧ train test ڭࢣ͋Γ σʔληοτ
7. / 86  AI։ൃϥΠϑαΠΫϧͷ֤ՕॴͰੜ͡ΔόΠΞε 02 Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Equity and access in algorithms, mechanisms, and optimization (pp. 1-9). ਆͷΈͧ஌Δ ਅͷੈք σʔλੜ੒ α ϯ ϓ Ϧ ϯ ά ͜ͷ࢟΋ ਆͷΈͧ஌Δ ܭଌ ٬ ମ Խ σʔληοτ લ ॲ ཧ ࣗಈతॲཧ ֶशɾਪ࿦ ධՁ Ϟσϧ train test ڭࢣ͋Γ σʔληοτ train test ڭࢣ͋Γ σʔληοτ
8. / 86  AI։ൃϥΠϑαΠΫϧͷ֤ՕॴͰੜ͡ΔόΠΞε 02 Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Equity and access in algorithms, mechanisms, and optimization (pp. 1-9). ਆͷΈͧ஌Δ ਅͷੈք σʔλੜ੒ α ϯ ϓ Ϧ ϯ ά ͜ͷ࢟΋ ਆͷΈͧ஌Δ ܭଌ ٬ ମ Խ σʔληοτ લ ॲ ཧ ࣗಈతॲཧ ֶशɾਪ࿦ ޙ ॲ ཧ γ ε ς Ϝ Խ ධՁ Ϟσϧ train test ڭࢣ͋Γ σʔληοτ train test ڭࢣ͋Γ σʔληοτ
9. / 86   AI։ൃϥΠϑαΠΫϧͷ֤ՕॴͰੜ͡ΔόΠΞε 02 Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Equity and access in algorithms, mechanisms, and optimization (pp. 1-9). ਆͷΈͧ஌Δ ਅͷੈք σʔλੜ੒ α ϯ ϓ Ϧ ϯ ά ͜ͷ࢟΋ ਆͷΈͧ஌Δ ܭଌ ٬ ମ Խ σʔληοτ લ ॲ ཧ ࣗಈతॲཧ ֶशɾਪ࿦ ޙ ॲ ཧ γ ε ς Ϝ Խ ධՁ Ϟσϧ train test ڭࢣ͋Γ σʔληοτ train test ڭࢣ͋Γ σʔληοτ
10. / 86 
      AI։ൃϥΠϑαΠΫϧͷ֤ՕॴͰੜ͡ΔόΠΞε 02 Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Equity and access in algorithms, mechanisms, and optimization (pp. 1-9). ਆͷΈͧ஌Δ ਅͷੈք σʔλੜ੒ α ϯ ϓ Ϧ ϯ ά ͜ͷ࢟΋ ਆͷΈͧ஌Δ ܭଌ ٬ ମ Խ σʔληοτ લ ॲ ཧ ࣗಈతॲཧ ֶशɾਪ࿦ ޙ ॲ ཧ γ ε ς Ϝ Խ ධՁ Ϟσϧ train test ڭࢣ͋Γ σʔληοτ train test ڭࢣ͋Γ σʔληοτ
11. / 86  AI։ൃϥΠϑαΠΫϧͷ֤ՕॴͰੜ͡ΔόΠΞε 02 Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Equity and access in algorithms, mechanisms, and optimization (pp. 1-9). ਆͷΈͧ஌Δ ਅͷੈք σʔλੜ੒ α ϯ ϓ Ϧ ϯ ά ͜ͷ࢟΋ ਆͷΈͧ஌Δ ܭଌ ٬ ମ Խ σʔληοτ લ ॲ ཧ ࣗಈతॲཧ ֶशɾਪ࿦ ޙ ॲ ཧ γ ε ς Ϝ Խ ධՁ Ϟσϧ train test ڭࢣ͋Γ σʔληοτ train test ڭࢣ͋Γ σʔληοτ ྺ࢙తͳภΓ ɾஉঁͰҟͳΔ ऩೖσʔλ͕஝ੵ ୅දͷภΓ ɾ೥ྸ΍ਓछͷภΓ ܭଌͷภΓ ɾܭଌޡࠩ ɾσʔλͷඪ४Խෆ଍ ධՁͷภΓ ɾෆద੾ͳධՁࢦඪ ɾϕϯνϚʔΫσʔλ͕ ฼ूஂΛ୅ද͍ͯ͠ͳ͍ ࣮૷ͷภΓ ɾ࠶൜༧ଌϞσϧΛܐظܾఆ γεςϜʹ૊ΈࠐΉ ɾར༻ऀͷࣗ཯ੑΛଛͳ͏ઃܭ
12. / 86  AI։ൃϥΠϑαΠΫϧͱਓؒͷೝ஌όΠΞε 02 Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Equity and access in algorithms, mechanisms, and optimization (pp. 1-9). ਆͷΈͧ஌Δ ਅͷੈք σʔλੜ੒ α ϯ ϓ Ϧ ϯ ά ͜ͷ࢟΋ ਆͷΈͧ஌Δ ܭଌ ٬ ମ Խ σʔληοτ લ ॲ ཧ ࣗಈతॲཧ ֶशɾਪ࿦ ޙ ॲ ཧ γ ε ς Ϝ Խ ධՁ Ϟσϧ train test ڭࢣ͋Γ σʔληοτ train test ڭࢣ͋Γ σʔληοτ ਓؒʹΑΔภΓ ೝ஌όΠΞε ਓؒʹΑΔภΓ ೝ஌όΠΞε ਓؒʹΑΔภΓ ೝ஌όΠΞε ྺ࢙తͳภΓ ɾஉঁͰҟͳΔ ऩೖσʔλ͕஝ੵ ୅දͷภΓ ɾ೥ྸ΍ਓछͷภΓ ܭଌͷภΓ ɾܭଌޡࠩ ɾσʔλͷඪ४Խෆ଍ ධՁͷภΓ ɾෆద੾ͳධՁࢦඪ ɾϕϯνϚʔΫσʔλ͕ ฼ूஂΛ୅ද͍ͯ͠ͳ͍ ࣮૷ͷภΓ ɾ࠶൜༧ଌϞσϧΛܐظܾఆ γεςϜʹ૊ΈࠐΉ ɾར༻ऀͷࣗ཯ੑΛଛͳ͏ઃܭ
13. / 86  AI։ൃϥΠϑαΠΫϧͱਓؒͷೝ஌όΠΞε 02 ਆͷΈͧ஌Δ ਅͷੈք Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Equity and access in algorithms, mechanisms, and optimization (pp. 1-9). σʔλੜ੒ α ϯ ϓ Ϧ ϯ ά ͜ͷ࢟΋ ਆͷΈͧ஌Δ ܭଌ ٬ ମ Խ σʔληοτ લ ॲ ཧ ࣗಈతॲཧ ֶशɾਪ࿦ ޙ ॲ ཧ γ ε ς Ϝ Խ ධՁ Ϟσϧ train test ڭࢣ͋Γ σʔληοτ train test ڭࢣ͋Γ σʔληοτ ྺ࢙తͳภΓ ɾஉঁͰҟͳΔ ऩೖσʔλ͕஝ੵ ୅දͷภΓ ɾ೥ྸ΍ਓछͷภΓ ܭଌͷภΓ ɾܭଌޡࠩ ɾσʔλͷඪ४Խෆ଍ ධՁͷภΓ ɾෆద੾ͳධՁࢦඪ ɾϕϯνϚʔΫσʔλ͕ ฼ूஂΛ୅ද͍ͯ͠ͳ͍ ਓؒͷೝ஌ͷϑΟϧλʔΛհ͞ͳ͍ͱ֎ࡏԽͰ͖ͳ͍஌ ओ؍త൑அɺଟ͘ͷ࣬ױͷ਍அɺͳͲ ࣮૷ͷภΓ ɾ࠶൜༧ଌϞσϧΛܐظܾఆ γεςϜʹ૊ΈࠐΉ ɾར༻ऀͷࣗ཯ੑΛଛͳ͏ઃܭ ਓؒʹΑΔภΓ ೝ஌όΠΞε ਓؒʹΑΔภΓ ೝ஌όΠΞε ਓؒʹΑΔภΓ ೝ஌όΠΞε
14. / 86  ࠓ೔ͷ͓͸ͳ͠ 02 AI։ൃϥΠϑαΠΫϧʹ͓͍ͯɺਓؒͷೝ஌όΠΞε͕༩͑ΔӨڹΛ֓؍͢Δ u ਓؒͷҙࢥܾఆ͸ภΔɺͱ͍͏ࣄ࣮Λ஌Δ u ࠓ͋ΔղܾࡦͱɺࠓޙඞཁͳղܾࡦΛߟ͑Δ └पล෼໺ʢHCIͳͲʣ΍ਓؒཧղʢೝ஌ՊֶͳͲʣͷ؍఺΋ ࠓ೔ͷ໨త Schwartz, R., Vassilev, A., Greene, K., Perine, L., Burt, A., & Hall, P. (2022). Towards a standard for identifying and managing bias in artificial intelligence. NIST special publication, 1270(10.6028). ਓؒͷߴ࣍ೝ஌ಛੑʹىҼ͢Δ AIͷόΠΞε͸ݟա͝͞Ε͖ͯͨ
15. / 86  ࠓ೔ͷ͓͸ͳ͠ 02 AI։ൃϥΠϑαΠΫϧʹ͓͍ͯɺਓؒͷೝ஌όΠΞε͕༩͑ΔӨڹΛ֓؍͢Δ u ਓؒͷҙࢥܾఆ͸ภΔɺͱ͍͏ࣄ࣮Λ஌Δ u ࠓ͋ΔղܾࡦͱɺࠓޙඞཁͳղܾࡦΛߟ͑Δ └पล෼໺ʢHCIͳͲʣ΍ਓؒཧղʢೝ஌ՊֶͳͲʣͷ؍఺΋ ࠓ೔ͷ໨త Birhane, A., Kalluri, P., Card, D., Agnew, W., Dotan, R., & Bao, M. (2022, June). The values encoded in machine learning research. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 173-184). NeurIPSͱICML 2008/9೥→2018/9೥ هࡌ͕૿߲͑ͨ໨ͷهࡌ͕͋Δ࿦จ਺ ਓ͔ؒΒֶͿɿ10%͘Β͍ ϢʔβʔΠϯλʔϑΣʔεɿ5%ҎԼ ࣗ཯ੑɺਖ਼ٛɺݸਓͷଚॏɿθϩ ͍ΘΏΔτοϓΧϯϑΝʹ͸͋·Γग़ͳ͍࿩୊
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17. AI։ൃͷ্ྲྀաఔ
18. / 86  Take Home Message 1 10 ਺஋ʹͨ͠ॠؒʹҰਓา͖ͯ͠͠·͏΋ͷ͸ੈʹଟ͋͘Δɻ ηΦυΞɾMɾϙʔλʔஶ ౻֞༟ࢠ༁ ʮ਺஋ͱ٬؍ੑʯ p.299
19. / 86  ͸͡Ίʹ 10  ਆͷΈͧ஌Δ ਅͷੈք σʔλੜ੒ α ϯ ϓ Ϧ ϯ ά ͜ͷ࢟΋ ਆͷΈͧ஌Δ ܭଌ ٬ ମ Խ σʔληοτ લ ॲ ཧ ࣗಈతॲཧ ֶशɾਪ࿦ ޙ ॲ ཧ γ ε ς Ϝ Խ ධՁ Ϟσϧ train test ڭࢣ͋Γ σʔληοτ train test ڭࢣ͋Γ σʔληοτ
20. / 86  ͸͡Ίʹ 10  ਆͷΈͧ஌Δ ਅͷੈք σʔλੜ੒ α ϯ ϓ Ϧ ϯ ά ͜ͷ࢟΋ ਆͷΈͧ஌Δ ܭଌ ٬ ମ Խ σʔληοτ લ ॲ ཧ ࣗಈతॲཧ train test ڭࢣ͋Γ σʔληοτ ໨࣍  σʔλ࡞੒ͱೝ஌όΠΞε Ø ݸਓͰͷҙࢥܾఆ Ø ूஂతҙࢥܾఆ Ø σʔλԽʹΑΔ৘ใͷܽམ 
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21. / 86  ໨࣍ 10  σʔλ࡞੒ͱೝ஌όΠΞε Ø ݸਓͰͷҙࢥܾఆ Ø ूஂతҙࢥܾఆ Ø σʔλԽʹΑΔ৘ใͷܽམ Ø ҉໧తͳ஌Λѻ͍͍ͨ 
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22. / 86  ࡞੒͞ΕΔσʔλͷภΓ1 11 *;NE0@'-7 C3?-( FJIDGL:38'' 9 :38>@/- %"&"! ;6 =1+A<40)+A<40) ؾ෼ͷམͪࠐΈ͕ ͋Γ·͔͢ʁ 0(ͳ͍)ʙ100(͋Δ) Ͱճ౴͍ͯͩ͘͠͞ ؾ෼ͷམͪࠐΈ͕ ͋Γ·͔͢ʁ 0(ͳ͍)ʙ100(͋Δ) Ͱճ౴͍ͯͩ͘͠͞ ؾ෼ͷམͪࠐΈ͕ ͋Γ·͔͢ʁ 0(ͳ͍)ʙ100(͋Δ) Ͱճ౴͍ͯͩ͘͠͞ 0 100 0 100 50
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24. / 86  ࡞੒͞ΕΔσʔλͷภΓ2 12 #$72A5,0@
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25. / 86  ࡞੒͞ΕΔσʔλͷภΓ2 12 
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26. / 86  ࡞੒͞ΕΔσʔλͷภΓ2 12 
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     ※εϥΠυʹهࡌ ͨ͠ϥϕϧ͸ྫͰ͢ #$72A5,0@
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    https://www.toyo.co.jp/medica l/casestudy/detail/id=5525 https://github.com/ieee8023/c ovid-chestxray-dataset ※εϥΠυʹهࡌ ͨ͠ϥϕϧ͸ྫͰ͢ #$72A5,0@
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28. / 86  ࡞੒͞ΕΔσʔλͷภΓ2 12 
    
    https://www.toyo.co.jp/medica l/casestudy/detail/id=5525 https://github.com/ieee8023/c ovid-chestxray-dataset 
    
    
      ※εϥΠυʹهࡌ ͨ͠ϥϕϧ͸ྫͰ͢ B.5
29. / 86  ࡞੒͞ΕΔσʔλͷภΓ2 12 
      
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      ※εϥΠυʹهࡌ ͨ͠ϥϕϧ͸ྫͰ͢ B.5
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31. / 86  ࡞੒͞ΕΔσʔλͷภΓ2 12 ϓϥΠϛϯάʢQSJNJOHʣ
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    > [1] Meyer, D. E., & Schvaneveldt, R. W. (1971). Facilitation in recognizing pairs of words: evidence of a dependence between retrieval operations. Journal of experimental psychology, 90(2), 227. [2] Tversky, A., & Kahneman, D., (1974). Judgement under uncertainty: Heuristics and biases. Science, 185, 1124-1131. [3] Koehler, D. J., & Harvey, N. (Eds.). (2008). Blackwell handbook of judgment and decision making. John Wiley & Sons., p.99 [3] Valdez, A. C., Ziefle, M., & Sedlmair, M. (2017). Priming and anchoring effects in visualization. IEEE transactions on visualization and computer graphics, 24(1), 584-594.
32. / 86  ࡞੒͞ΕΔσʔλͷภΓ2 12 ϓϥΠϛϯάʢQSJNJOHʣ
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    > [1] Meyer, D. E., & Schvaneveldt, R. W. (1971). Facilitation in recognizing pairs of words: evidence of a dependence between retrieval operations. Journal of experimental psychology, 90(2), 227. [2] Tversky, A., & Kahneman, D., (1974). Judgement under uncertainty: Heuristics and biases. Science, 185, 1124-1131. [3] Koehler, D. J., & Harvey, N. (Eds.). (2008). Blackwell handbook of judgment and decision making. John Wiley & Sons., p.99 [3] Valdez, A. C., Ziefle, M., & Sedlmair, M. (2017). Priming and anchoring effects in visualization. IEEE transactions on visualization and computer graphics, 24(1), 584-594. [4]
33. / 86  ࡞੒͞ΕΔσʔλͷภΓ3 13 ·ͱΊ A B A A
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35. / 86  ࡞੒͞ΕΔσʔλͷภΓ3 13 ܦ࿏ґଘੑ
    ಉௐ ूஂ ͳͲ u ҙݟू໿ ◇ ܦ࿏ґଘੑʢpath dependenceʣʢΞϩʔͷෆՄೳੑఆཧʣ[1] ◇ ಉௐʢconformityʣ[2] ݸਓͷ൑அ͕ಉ੮͢Δଞऀͷ൑அʹӨڹΛड͚Δ ◇ ूஂۃੑԽʢ੒ۃԽʣʢgroup polarizationʣ[3] ूஂʹΑΔٞ࿦‎ҙݟ͕ۃ୺ʢաܹ/৻ॏʣʹͳΔ [1] Arrow, Kenneth J. (1950). "A Difficulty in the Concept of Social Welfare" . Journal of Political Economy. 58 (4): 328–346. [2] Asch, S. E. (1951). Effects of group pressure upon the modification and distortion of judgments. [3] Moscovici, S., & Zavalloni, M. (1969). The group as a polarizer of attitudes. Journal of personality and social psychology, 12(2), 125. [4] ޿ాΒɺ৺ཧֶ͕ඳ͘ϦεΫͷੈքɺܚጯٛक़େֶग़൛ձɺp.179 [5] ന઒ల೭, & খࣲ. (2018). Պֶٕज़༧ଌௐࠪख๏ʹؔ͢Δ਺ཧత෼ੳ σϧϑΝΠௐࠪٴͼϦΞϧλΠϜɾσϧϑΝΠ๏ʹؔ͢ΔΤʔδΣϯτγϛϡϨʔγϣϯ. ݚڀ ٕज़ ܭը, 33(2), 170-183. u σϧϑΝΠؔ࿈ख๏ ख๏ʹґଘͯ͠ɺ࠷ऴతʹू໿͞ΕΔҙݟ͕ҟͳΔ [5] ҙݟू໿ͷ ܾఆϧʔϧ ܾఆ݁Ռʹ͍ͭͯͷ֤Ϟσϧͷ༧ଌ஋ ༗ࡑ ແࡑ ະܾ ൺྫܕ .220 .780 .000 2/3ଟ਺ܾܕ .001 .899 .100 ຬ৔Ұகܕ .000 1.000 .000 [4]
36. / 86  σʔλԽʹΑΔ৘ใͷܽམ1 ա౓ͳ୯७Խ 14  & *$#' %(")! A@H?5D6J> 9711J>9,;2; 2 5>@ BA2=B C@AE 8@.
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40. / 86  ղܾʹ޲͚ͨࢼΈ 16 ٕ ज़ ։ ൃ u φοδ
    
    
    ҙࢥܾఆΛಛఆͷํ޲ʹม͑Δબ୒ΞʔΩςΫνϟͷཁૉ <> u ϒʔετ
    
    
    ೝ஌తٕྔΛߴΊΔ·ͨ͸ ֫ಘ͢Δ͜ͱͰ߹ཧతͳҙࢥܾఆʹಋ͘<> u ख़ߟΛଅ͢ <> ਓ ؒ ʹ հ ೖ AI UI u ೝ஌όΠΞεͷӨڹΛܰݮͨ͠ϥϕϧΛ༧ଌ͢Δ਺ཧϞσϧ [3, 4] u ೝ஌όΠΞεܰݮͷͨΊͷΫϥ΢υιʔγϯάͰͷνΣοΫϦετ [11] u ϊΠζͷ͋Δσʔλʹରͯ͠ؤ݈ͳػցֶशϞσϧ [5] u গ਺σʔλʹରͯ͠ؤ݈ͳػցֶशϞσϧ few-shot learning, semi/weakly-supervised, domain adaptation, self-supervised, … u Human-in-the-Loop ػցֶश [6] u VASͷઃܭͷఏҊ [1] ໨੝Γºɺ஋͕ݟ͑ΔಈతεϥΠμʔ ̋ɺ ଳঢ়ϥϕϧ2ͭͷεϥΠμʔ ̋ u ௥Ճઃ໰΍৘ใఏࣔͰରॲ [2] [1] Matejka, J., Glueck, M., Grossman, T., & Fitzmaurice, G. (2016, May). The effect of visual appearance on the performance of continuous sliders and visual analogue scales. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 5421-5432). [2] Hube, C., Fetahu, B., & Gadiraju, U. (2019, May). Understanding and mitigating worker biases in the crowdsourced collection of subjective judgments. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-12). [3] Zhuang, H., Parameswaran, A., Roth, D., & Han, J. (2015, August). Debiasing crowdsourced batches. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1593-1602). [4] Gemalmaz, M. A., & Yin, M. (2021). Accounting for Confirmation Bias in Crowdsourced Label Aggregation. In IJCAI (pp. 1729-1735). [5] Song, H., Kim, M., Park, D., Shin, Y., & Lee, J. G. (2022). Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems [6] .Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2023). Human-in-the-loop machine learning: A state of the art. Artificial Intelligence Review, 56(4), 3005-3054. .[11] Draws, T., Rieger, A., Inel, O., Gadiraju, U., & Tintarev, N. (2021, October). A checklist to combat cognitive biases in crowdsourcing. In Proceedings of the AAAI conference on human computation and crowdsourcing (Vol. 9, pp. 48-59). [7] Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth and happiness. Simon & Schuster [8] Hertwig, R., & Grüne-Yanoff, T. (2017). Nudging and boosting: Steering or empowering good decisions. Perspectives on Psychological Science, 12 (6), 973–986. [9] O’Sullivan, E. D., & Schofield, S. J. (2019). A cognitive forcing tool to mitigate cognitive bias–a randomised control trial. BMC medical education, 19, 1-8.[10] Kameda, T., Toyokawa, W., & Tindale, R. S. (2022). Information aggregation and collective intelligence beyond the wisdom of crowds. Nature Reviews Psychology, 1(6), 345-357 ू ஂ ݸ ਓ u ूஂతҙࢥܾఆʹΑΓਖ਼֬ੑ͕޲্͢Δ৚݅ͷ໢ཏతݕ౼ <>
41. / 86  ղܾʹ޲͚ͨࢼΈ 16 ٕ ज़ ։ ൃ u φοδ
    
    
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46. / 86  ͸͡Ίʹ 20
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    > [1] Vasey, B., Ursprung, S., Beddoe, B., Taylor, E. H., Marlow, N., Bilbro, N., ... & McCulloch, P. (2021). Association of clinician diagnostic performance with machine learning–based decision support systems: a systematic review. JAMA network open, 4(3), e211276-e211276. [2] Freeman, K., Geppert, J., Stinton, C., Todkill, D., Johnson, S., Clarke, A., & Taylor-Phillips, S. (2021). Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. bmj, 374.
47. / 86  ͸͡Ίʹ 20
     ֶशɾਪ࿦ ޙ ॲ ཧ γ ε ς Ϝ Խ ධՁ Ϟσϧ train test ڭࢣ͋Γ σʔληοτ [1] Lai, V., Chen, C., Smith-Renner, A., Liao, Q. V., & Tan, C. (2023, June). Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies. In Pr of the 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 1369-1385). [1] ಓಙత൑அ΍૑଄తͳҙࢥܾఆ ʢJF
    ౴͕͑ఆ·Βͳ͍λεΫʣ ࠓ೔͸࿩͞ͳ͍͜ͱ ※ લఏɿAIͷੑೳ͸ۃΊͯߴ͍ "*͕༧ଌ·ͨ͸ࣝผͨ݁͠ՌΛɺਓؒ͸ͲͷΑ͏ʹ࢖ͬͯҙࢥܾఆ͢Δͷ͔ʁ
48. / 86  ໨࣍ 20  ਓؒ͸"*Λద੾ʹ࢖͑ͳ͍ Ø "*ͷաখධՁͱաେධՁ 
    ղܾͷج൫ͱͳΓ͏Δݚڀ Ø "*ٕज़։ൃܥ Ø આ໌ՄೳੑɾղऍՄೳੑ Ø ৴པɺઆಘ Ø ػցֶशΞϧΰϦζϜͷ։ൃ Ø ਓؒͷҙࢥܾఆաఔΛཧղ͢Δݚڀ
49. / 86  AI΁ͷաখධՁɾաେධՁ 21 աখධՁ "*Λॿݴͱͯ͠ར༻  ࣾձ৺ཧֶ +VEHF
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    <> advice taking [1] Yaniv, I., & Kleinberger, E. (2000). Advice taking in decision making: Egocentric discounting and reputation formation. Organizational behavior and human decision processes, 83(2), 260-281 [2] Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: people erroneously avoid algorithms after seeing them err. Journal of experimental psychology: General, 144(1), 114. [3] S. Shyam Sundar and Jinyoung Kim. 2019. Machine Heuristic: When We Trust Computers More than Humans with Our Personal Information. CHI2019, p.1-9 [4] Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human factors, 39(2), 230-253. [5] Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human factors, 52(3), 381-410. [6] Skitka, L. J., Mosier, K. L., & Burdick, M. (1999). Does automation bias decision-making?. International Journal of Human-Computer Studies, 51(5), 991-1006. [7] Tschopp, M. (2020). PAS-The perfect automation schema. https://www.scip.ch/en/?labs.20200507
50. / 86  AI΁ͷաখධՁɾաେධՁ 21 Chen, C., Feng, S., Sharma, A., & Tan, C. (2022). Machine Explanations and Human Understanding. Transactions on Machine Learning Research. ਅͷڥք ਓ͕΋ͱ΋ͱ ͍࣋ͬͯͨڥք ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ AIͷڥք ʔ ʔ ʴ
51. / 86  AI΁ͷաখධՁɾաେධՁ 21 Chen, C., Feng, S., Sharma, A., & Tan, C. (2022). Machine Explanations and Human Understanding. Transactions on Machine Learning Research. ਅͷڥք ਓ͕΋ͱ΋ͱ ͍࣋ͬͯͨڥք ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ AIͷڥք ʔ ʔ ʴ
52. / 86  AI΁ͷաখධՁɾաେධՁ 21 Chen, C., Feng, S., Sharma, A., & Tan, C. (2022). Machine Explanations and Human Understanding. Transactions on Machine Learning Research. ਅͷڥք ਓ͕΋ͱ΋ͱ ͍࣋ͬͯͨڥք ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ AIͷڥք ʔ ʔ ʴ
53. / 86  AI΁ͷաখධՁɾաେධՁ 21 Chen, C., Feng, S., Sharma, A., & Tan, C. (2022). Machine Explanations and Human Understanding. Transactions on Machine Learning Research. ਅͷڥք ਓ͕΋ͱ΋ͱ ͍࣋ͬͯͨڥք ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ AIͷڥք ʔ ʔ ʴ ʔ
54. / 86  AI΁ͷաখධՁɾաେධՁ 21 Chen, C., Feng, S., Sharma, A., & Tan, C. (2022). Machine Explanations and Human Understanding. Transactions on Machine Learning Research. ਅͷڥք ਓ͕΋ͱ΋ͱ ͍࣋ͬͯͨڥք ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ AIͷڥք ʔ ʔ ʴ ͜ͷ͋ͨΓ͸AIͷݴ͏௨Γʹͨ͠ํ͕͍͍ʢAIͷաখධՁ͕໰୊ʣ
55. / 86  AI΁ͷաখධՁɾաେධՁ 21 Chen, C., Feng, S., Sharma, A., & Tan, C. (2022). Machine Explanations and Human Understanding. Transactions on Machine Learning Research. ਅͷڥք ਓ͕΋ͱ΋ͱ ͍࣋ͬͯͨڥք ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ AIͷڥք ʔ ʔ ʴ ͜ͷ͋ͨΓ͸ࣗ෼ͷߟ͑Λ৴ͨ͡ํ͕͍͍ʢAIͷաେධՁ͕໰୊ʣ ʔ
56. / 86  AI΁ͷաখධՁɾաେධՁ 21 Chen, C., Feng, S., Sharma, A., & Tan, C. (2022). Machine Explanations and Human Understanding. Transactions on Machine Learning Research. ਅͷڥք ਓ͕΋ͱ΋ͱ ͍࣋ͬͯͨڥք ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ AIͷڥք ʔ ʔ ʴ ʔ ্3ͭͷઢͷ͢΂ͯΛਓ͕ؒ ೺ѲͰ͖ͳ͍ͱ ਓؒ͸AIΛద੾ʹ࢖͑ͳ͍   
57. / 86  AI΁ͷաখධՁɾաେධՁ 21 աখධՁ "*Λॿݴͱͯ͠ར༻  ࣾձ৺ཧֶ +VEHF
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    <> advice taking [1] Yaniv, I., & Kleinberger, E. (2000). Advice taking in decision making: Egocentric discounting and reputation formation. Organizational behavior and human decision processes, 83(2), 260-281 [2] Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: people erroneously avoid algorithms after seeing them err. Journal of experimental psychology: General, 144(1), 114. [3] S. Shyam Sundar and Jinyoung Kim. 2019. Machine Heuristic: When We Trust Computers More than Humans with Our Personal Information. CHI2019, p.1-9 [4] Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human factors, 39(2), 230-253. [5] Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human factors, 52(3), 381-410. [6] Skitka, L. J., Mosier, K. L., & Burdick, M. (1999). Does automation bias decision-making?. International Journal of Human-Computer Studies, 51(5), 991-1006. [7] Tschopp, M. (2020). PAS-The perfect automation schema. https://www.scip.ch/en/?labs.20200507
58. / 86  AI΁ͷաখධՁɾաେධՁ 21 աখධՁ ΞϧΰϦζϜݏѱʢBMHPSJUIN
    BWFSTJPOʣ "*΋ؒҧ͑Δ͜ͱΛ஌Δͱɺͦͷ"*ͷग़ྗΑΓ΋ࣗ෼ͷ൑அΛબ୒͢Δ <> ୠɿʮਓ͕ؒࣗ෼ΑΓ΋ਫ਼౓ͷߴ͍ΞϧΰϦζϜͷग़ྗΛ࢖Θͳ͍ʯ ͷҙຯͰ࢖ΘΕΔ͜ͱ͕ଟ͍ 0 100 200 300 400 500 600 700 800 900 1000 2015 2016 2017 2018 2019 2020 2021 2022 2023 GOOGLE SCHOLAR هࣄ਺ 2023/11/11ݱࡏ ↔ algorithm appreciation ਓؒ͸ΞϧΰϦζϜͷग़ྗΛ࠾༻͢Δ [2] [1] Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: people erroneously avoid algorithms after seeing them err. Journal of experimental psychology: General, 144(1), 114. [2] Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90-103. ࣮ݧϓϩτίϧ [1]
59. / 86  AI΁ͷաখධՁɾաେධՁ 21 աখධՁ ࣗݾத৺తॿݴׂҾʢFHPDFOUSJD
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    EJTDPVOUJOHʣ ࣗઆʹ౎߹Α͘ɺଞऀ͔ΒͷॿݴΛׂΓҾ͘ <> ΠΪϦε͕ࠃͱͯ͠ਪ঑͍ͯ͠Δ ਍அࢧԉAIͷॿݴΛҩࢣ͕աখධՁ [2] ౶೘පͷൃ঱֬཰ ※ਅͷਖ਼ղ:70% ྫ ਍ ࡯ "*ͷࢧԉ ˞༧ଌɾࣝผਫ਼౓͕ۃΊͯߴ͍લఏ ࠷ऴతͳҙࢥܾఆ ױऀ9 ҩऀ" 50% 70% ༧ଌޡࠩ  35% ҩऀ" 70% [1] Yaniv, I., & Kleinberger, E. (2000). Advice taking in decision making: Egocentric discounting and reputation formation. Organizational behavior and human decision processes, 83(2), 260-281. [2] Pálfi, B., Arora, K., & Kostopoulou, O. (2022). Algorithm-based advice taking and clinical judgement: impact of advice distance and algorithm information. Cognitive research: principles and implications, 7(1), 70. [3] Bailey, P. E., Leon, T., Ebner, N. C., Moustafa, A. A., & Weidemann, G. (2022). A meta-analysis of the weight of advice in decision-making. Current Psychology, 1-26. ॿݴऀʹΑΒͣ ؤ݈ʹ࠶ݱ [3] ˞
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60. / 86  AI΁ͷաখධՁɾաେධՁ 21 աେධՁ ࣗಈԽόΠΞεʢBVUPNBUJPO
    CJBTʣ ࣗಈԽγεςϜʹա౓ʹґଘ͢Δ܏޲ <> [1] Skitka, L. J., Mosier, K. L., & Burdick, M. (1999). Does automation bias decision-making?. International Journal of Human-Computer Studies, 51(5), 991-1006. [2] Alon-Barkat, S., & Busuioc, M. (2023). Human–AI interactions in public sector decision making:“automation bias” and “selective adherence” to algorithmic advice. Journal of Public Administration Research and Theory, 33(1), 153-169. [3] Kupfer, C., Prassl, R., Fleiß, J., Malin, C., Thalmann, S., & Kubicek, B. (2023). Check the box! How to deal with automation bias in AI-based personnel selection. Frontiers in Psychology, 14, 1118723. [4] Lyell, D., & Coiera, E. (2017). Automation bias and verification complexity: a systematic review. Journal of the American Medical Informatics Association, 24(2), 423-431. "*ͷจ຺
    
    
    ߦ੓ [2]ɺاۀਓࣄ [3]ɺҩྍ [4] ͳͲ ;ͩΜ͔ΒࢧԉγεςϜΛར༻ͯ͠൑அ͍ͯ͠ΔͱɺࢧԉγεςϜ͕ͳ͘ͳͬͨͱ͖ʹɺ ൑அʹඞཁͳঢ়گͷݟམͱ͕͠૿͑ͨɻ λεΫը໘ ܭث1 ܭث2 ࢧԉγεςϜ͕ Πϕϯτʹج͖ͮ ߦಈΛਪન ࢀՃऀͷ൒෼͸͕ۭ͜͜ཝ ࣮ݧϓϩτίϧ [1]
61. / 86  ໨࣍ 20 1. ਓؒ͸AIΛద੾ʹ࢖͑ͳ͍ Ø AIͷաখධՁͱաେධՁ 
    ղܾͷج൫ͱͳΓ͏Δݚڀ Ø "*ٕज़։ൃܥ Ø આ໌ՄೳੑɾղऍՄೳੑ Ø ৴པɺઆಘ Ø ػցֶशΞϧΰϦζϜͷ։ൃ Ø ਓؒͷҙࢥܾఆաఔΛཧղ͢Δݚڀ
62. / 86  eXplainable AI ʢXAIɿઆ໌ՄೳAIʣ 22 എܠ AIͷ࣮Ԡ༻͕೉͍͠ҰҼʮAI͸ϒϥοΫϘοΫεͰ݁Ռɾཧ༝Λઆ໌ࠔ೉ʯ[1, 2] ར఺ AIͷग़ྗʢ༧ଌɾࣝผʣΛਓ͕ؒཧղͰ͖Δ͜ͱΛ໨తͱٕͨ͠ज़ ՝୊ આ໌ੑ [3] ɾղऍੑ [4] ɾಁ໌ੑ͸ɺඞͣ͠΋ਓؒͷҙࢥܾఆΛม͑ͳ͍ [5] ਓ͕ؒߦಈʹࢸΔաఔʹ͓͍ͯɺཧղͱҙࢥܾఆ͸ผ [1] JST ,ʢઓུϓϩϙʔβϧʣAIԠ༻γεςϜͷ҆શੑɾ৴པੑΛ֬อ͢Δ৽ੈ୅ιϑτ΢ΣΞ޻ֶͷཱ֬, https://www.jst.go.jp/crds/pdf/2018/SP/CRDS-FY2018-SP-03.pdf, 2018 [2] Molnar, C. (2018). A guide for making black box models explainable. URL: https://christophm. github. io/interpretable-ml-book, 2(3) [3] https://www.darpa.mil/program/explainable-artificial-intelligence [4] Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., ... & Herrera, F. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information Fusion, 99, 101805. [5] Weller, A. (2019). Transparency: motivations and challenges. In Explainable AI: interpreting, explaining and visualizing deep learning (pp. 23-40). Cham: Springer International Publishing. [6] https://github.com/shap/shap#citations [7] https://github.com/marcotcr/lime [8] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144). આ໌Մೳੑ ·ͨ͸ ղऍՄೳੑ [6] [8] [7]
63. / 86  eXplainable AI ʢXAIɿઆ໌ՄೳAIʣ 22 എܠ AIͷ࣮Ԡ༻͕೉͍͠ҰҼʮAI͸ϒϥοΫϘοΫεͰ݁Ռɾཧ༝Λઆ໌ࠔ೉ʯ[1, 2] ར఺ AIͷग़ྗʢ༧ଌɾࣝผʣΛਓ͕ؒཧղͰ͖Δ͜ͱΛ໨తͱٕͨ͠ज़ ՝୊ આ໌ੑɾղऍੑɾಁ໌ੑ͸ɺඞͣ͠΋ਓؒͷҙࢥܾఆΛʢ։ൃऀͷ૝ఆ௨Γʹʣ ม͑ͳ͍ [5] ਓ͕ؒߦಈʹࢸΔաఔʹ͓͍ͯɺཧղͱҙࢥܾఆ͸ผ આ໌Մೳੑ ·ͨ͸ ղऍՄೳੑ [10] https://asd.gsfc.nasa.gov/conferences/ai/program/003-XAIforNASA.pdf [20] Sørensen, K., Van den Broucke, S., Fullam, J., Doyle, G., Pelikan, J., Slonska, Z., ... & (HLS-EU) Consortium Health Literacy Project European. (2012). Health literacy and public health: a systematic review and integration of definitions and models. BMC public health, 12, 1-13. આ໌ʢཧղʣͱ "*ͷద੾ͳར༻͸ ඞͣ͠΋Ұக͠ͳ͍ [10] [20] ਓ ؒ ͕ ߦ ಈ ʹ ࢸ Δ ա ఔ ஌֮ ཧղ ධՁ ൑அɾҙࢥܾఆ ߦಈ
64. / 86  eXplainable AI ʢXAIɿઆ໌ՄೳAIʣ 22 Chen, C., Feng, S., Sharma, A., & Tan, C. (2022). Machine Explanations and Human Understanding. Transactions on Machine Learning Research. ਅͷڥք ਓ͕΋ͱ΋ͱ ͍࣋ͬͯͨڥք ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʴ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ ʔ AIͷڥք ʔ ʔ ʴ ্3ͭͷઢͷ͢΂ͯΛਓ͕ؒ೺ ѲͰ͖ͳ͍ͱ ਓؒ͸AIΛద੾ʹ࢖͑ͳ͍ XAI͕ղܾͰ͖Δͷ͸ ʔ
65. / 86  eXplainable AI ʢXAIɿઆ໌ՄೳAIʣ 22 Wang, D., Yang, Q., Abdul, A., & Lim, B. Y. (2019, May). Designing theory-driven user-centric explainable AI. In Proceedings of the 2019 CHI conference on human factors in computing systems (pp. 1-15). ਓؒͷཧղ આ໌ՄೳAI
66. / 86  XAIͱೝ஌όΠΞε 23 2022೥࣌఺Ͱର৅࿦จ37ຊͷΈʂ ·ͩ·ͩϒϧʔΦʔγϟϯʂ Bertrand, A., Belloum, R., Eagan, J. R., & Maxwell, W. (2022, July). How cognitive biases affect XAI-assisted decision-making: A systematic review. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (pp. 78-91). XAIʹΑͬͯ༠ൃ આ໌͕ݪҼͱͳΔ XAIͷධՁʹӨڹ͢Δ XAIʹΑͬͯܰݮ XAIͰ༠ൃ͞Εͨ ೝ஌όΠΞεΛܰݮ XAIٕज़
67. / 86  XAIͱೝ஌όΠΞε 23 [1] Eiband, M., Buschek, D., Kremer, A., & Hussmann, H. (2019, May). The impact of placebic explanations on trust in intelligent systems. In Extended abstracts of the 2019 CHI conference on human factors in computing systems (pp. 1-6). [2] Cadario, R., Longoni, C., & Morewedge, C. K. (2021). Understanding, explaining, and utilizing medical artificial intelligence. Nature Human Behaviour, 5(12), 1636-1642. [3] van der Waa, J., Nieuwburg, E., Cremers, A., & Neerincx, M. (2021). Evaluating XAI: A comparison of rule-based and example-based explanations. Artificial Intelligence, 291, 103404. [4] Chen, V., Liao, Q. V., Wortman Vaughan, J., & Bansal, G. (2023). Understanding the role of human intuition on reliance in human-AI decision-making with explanations. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW2), 1-32. [5] Mualla, Y., Tchappi, I., Kampik, T., Najjar, A., Calvaresi, D., Abbas-Turki, A., ... & Nicolle, C. (2022). The quest of parsimonious XAI: A human-agent architecture for explanation formulation. Artificial intelligence, 302, 103573. [6] T. Lombrozo, “Simplicity and probability in causal explanation,” Cognit. Psychol., vol. 55, no. 3, pp. 232–257, Nov. 2007 [7] Nguyen, H. A., Hofman, J. M., & Goldstein, D. G. (2022, April). Round numbers can sharpen cognition. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-15). [8] Cao, H., Spatharioti, S. E., Goldstein, D. G., & Hofman, J. M. (2023). Comparing scalable strategies for generating numerical perspectives. arXiv preprint arXiv:2308.01535. [9] Alicioglu, G., & Sun, B. (2022). A survey of visual analytics for Explainable Artificial Intelligence methods. Computers & Graphics, 102, 502-520. 9"*ͱਓؒͷҙࢥܾఆɾߦಈͷؔ܎ هड़తݚڀ u ϓϥηϘઆ໌ɺӕͷઆ໌ ◇ ਖ਼౰͕ͩ৘ใΛ఻͍͑ͯͳ͍આ໌͸ਖ਼͍͠આ໌ͱಉ౳ʹΞϓϦΛ৴པͤ͞Δ <> ◇ ਖ਼͍͠આ໌ΑΓΘ͔ͬͨؾʹͳΔઆ໌͕ਓؒͷߦಈΛม͑Δ <> u 9"*ʹ͓͚Δઆ໌ܗࣜ ◇ ൓࣮ܕઆ໌ʹࣄྫܕઆ໌Λ௥Ճ͢Δͱɺઆ໌͕ؒҧ͍ͬͯͯ΋આಘͤ͞ΒΕΔɻ ͨͩ͠ɺλεΫύϑΥʔϚϯε͸޲্͠ͳ͍ɻ<> ◇ Ҽࢠܕઆ໌͸ҙࢥܾఆΛվળͤͣɺ"*͕ޡ͍ͬͯͯ΋"*Λ৴͡Δ <> u આ໌ͷ෼ྔ ◇ QBSTJNPOZʢγϯϓϧ͔ͭద੾ʣͳઆ໌ͷ࣮૷ <> ೝ஌Պֶ෼໺ͷ஌ݟʢ࠷΋޿ൣͰγϯϓϧʣ<> ͱໃ६͠ͳ͍ ϓϥηϘɿِༀɻༀͷ੒෼͕ೖ͍ͬͯͳ͍ݟͨ໨͚ͩͷༀɻ આ ໌ ͷ ಺ ༰ આ ໌ ͷ ද ݱ u ਺஋ ◇ ࢛ࣺޒೖؙͯ͠Ίͨ਺஋Λݟͤͨํ͕ɺͦͷ਺஋Λࢥ͍ग़͢ਫ਼౓͕ߴ͍ <> ◇ จ຺ʹԠͯ͡ద੾ͳදݱ͕มΘΔ <> u ࢹ֮දݱ <> ʮ ਓʯWTʮ ਓʯ ʮ ԯυϧʯWTʮΞϝϦΧ࿈๜༧ࢉͷ໿ʯ WT
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68. / 86  ৴པɺઆಘ 24 ৴ པ ʢ 5 S V T U ʣ "*ؔ࿈෼໺ ૊৫Ϛωδϝϯτ ࣾձ৺ཧֶ l ಓಙతடংʹର͢ Δظ଴ͷ͏ͪɺ૬ खͷ಺໘ʹ͋Δਓ ؒੑ΍ࣗ෼ʹର͢ Δײ৘ͳͲͷ൑அ ʹ΋ͱ͍ͮͯͳ͞ ΕΔɺ૬खͷҙਤ ʹ͍ͭͯͷظ଴ <> ͜͜ʹهࡌͨ͠ͷ͸֤෼໺ʹ͓͚Δ୅දతͳҰྫɻۃΊͯଟ༷ͳٞ࿦͕ࠞಱͱ ͍ͯ͠ΔͨΊɺݚڀจ຺͝ͱʹద੾ͳఆٛΛ࠾༻͢Δඞཁ͕͋Δɻ l ૬खͷҙਤ΍ߦಈ ʹର͢Δߠఆతͳ ظ଴ʹج͖ͮɺ੬ ऑੑΛड͚ೖΕΔ ҙࢥΛؚΉ৺ཧঢ় ଶ <> [1] https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai [2] Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human factors, 46(1), 50-80. [3] Merritt, S. M., Heimbaugh, H., LaChapell, J., & Lee, D. (2013). I trust it, but I don’t know why: Effects of implicit attitudes toward automation on trust in an automated system. Human factors, 55(3), 520-534. [4] Jian, J. Y., Bisantz, A. M., & Drury, C. G. (2000). Foundations for an empirically determined scale of trust in automated systems. International journal of cognitive ergonomics, 4(1), 53-71. [5] Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on systems, man, and cybernetics-Part A: Systems and Humans, 30(3), 286-297. [6] Yang, X. J., Schemanske, C., & Searle, C. (2023). Toward quantifying trust dynamics: How people adjust their trust after moment-to-moment interaction with automation. Human Factors, 65(5), 862-878. [7] Schaefer, K. (2013). The perception and measurement of human-robot trust. [8] Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: A cross-discipline view of trust. Academy of management review, 23(3), 393-404. [9] ࢁ؛ढ़உ. (1998). ৴པͷߏ଄. ౦ژେֶग़൛ձ. p.46-47 ࣗಈԽγεςϜ l ෆ࣮֬ੑͱ੬ऑ͞ ʹΑͬͯಛ௃෇͚ ΒΕΔঢ়گͰɺ͋ Δݸਓͷ໨ඪͷୡ ੒Λ͋ΔΤʔδΣ ϯτ͕खॿ͚͢Δ ͩΖ͏ͱ͍͏ଶ౓ <> ʔ ଌఆई౓ <
    
    
    
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69. / 86  ৴པɺઆಘ 24 ৴ པ ʢ 5S V T U ʣ Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., ... & Herrera, F. (2023). Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence. Information Fusion, 99, 101805.
70. / 86  ৴པɺઆಘ 24 આ ಘ )$*෼໺ʢDBQUPMPHZʣ ࣾձ৺ཧֶ l ਓͷߦಈɾߟ͑ํɾଶ౓Λม͑Δ͜ͱɺ ಈػ͚ͮΔɺ௥ै͢Δ [1]  ύʔε΢ΣΠγϒٕज़͸ߟ͑ํ΍ ߦಈͷมԽʹண໨͢Δ <> l ଞऀͷଶ౓Λม͑Δ͜ͱ <>  ଶ౓ŋŋŋର৅ʹؔ͢Δɺ޷Έ΍ධՁ తͳ൑அʹج͍ͮͨ৺ཧతͳ܏޲<> [1] Fogg, B. J. (1998, April). Captology: the study of computers as persuasive technologies. In CHI 98 Conference Summary on Human Factors in Computing Systems (p. 385). [2] Fogg, B. J. Persuasive technology: using computers to change what we think and do. Morgan Kaufmann, 2003 [3] ஑ాΒɺࣾձ৺ཧֶ ิగ൛ɺ༗൹ֳɺ2019ɺp.137-158 [4] Bai, H., Voelkel, J., Eichstaedt, J., & Willer, R. (2023). Artificial intelligence can persuade humans on political issues. "*෼໺ <> ੓ࡦʹؔ͢ΔGPT-3 or 3.5ͷੜ੒จΛಡΜͰɺਓ͕ؒઆಘ͞ΕͯҙݟΛม͑ͨ ਓ͕ؒهࡌͨ͠จষͱಉఔ౓ʹ
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72. / 86  AIΛ࢖͏ਓؒͷҙࢥܾఆաఔΛཧղ͍ͨ͠ 26 ࣬ױ …ͳͲ ҙࢥܾఆ 50% ҩऀA . ݸਓɾूஂ) ü ü ( ü ü ü ( ҙࢥܾఆϞσϧ ᶃपғͷਓʑ ᶄঢ়گ …ͳͲ º ҩऀA ҙࢥܾఆաఔʹӨڹ͢Δཁૉ छྨʹ෼ྨ Hoff, K. A., & Bashir, M. (2015). Trust in automation: Integrating empirical evidence on factors that influence trust. Human factors, 57(3), 407-434.
73. / 86  AIΛ࢖͏ਓؒͷҙࢥܾఆաఔΛཧղ͍ͨ͠ 26 ҙࢥܾఆաఔʹӨڹ͢ΔཁૉɿλεΫ u ٬؍తͩͱײ͡ΔλεΫͰ"*ͷճ౴Λ࠾༻͠΍͍͢ <> u ߟྀ͢΂͖ม਺͕ଟ͍λεΫͷ৔߹ɺ"*ͷճ౴Λ࠾༻͠΍͍͢ <> u ࣗಈԽγεςϜͷಈ࡞͕ద੾͔ݕূ࡞ۀ͕൥ࡶͳ৔߹ɺࣗಈԽόΠΞε͕ ੜ͡΍͍͢ <> ྫɿΞϥʔτ͕ɺ௨ৗ࣌ʹ͸ൃಈͤͣɺظ଴͞ΕΔͱ͖ͷΈʹൃಈ͢Δ͜ͱΛ֬ೝ͢Δ [1] Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of Marketing Research, 56(5), 809-825. [2] Bansal, G., Nushi, B., Kamar, E., Lasecki, W. S., Weld, D. S., & Horvitz, E. (2019, October). Beyond accuracy: The role of mental models in human-AI team performance. In Proceedings of the AAAI conference on human computation and crowdsourcing (Vol. 7, No. 1, pp. 2-11). [3] Lyell, D., & Coiera, E. (2017). Automation bias and verification complexity: a systematic review. Journal of the American Medical Informatics Association, 24(2), 423-431.
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