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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

rinabouk
November 16, 2023
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 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

rinabouk

November 16, 2023
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  1. / 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
  2. / 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༻ <> ◇ ࢘๏ $0.1"4<> ◇ ܉ࣄ %"31"9"*ϓϩδΣΫτ <> u "*ͷ඼࣭อূ ◇"*ΨόφϯεϗϫΠτϖʔύʔWFS <> ◇ػցֶश඼࣭ϚωδϝϯτΨΠυϥΠϯ <> u ࿦จࣥචͷΨΠυϥΠϯ ◇%&$*%&"* ͳͲ < > u (޿ౡ"*ϓϩηε (σδλϧɾٕज़ֳ྅੠໌ <> u "*ࣾձݪଇͳͲ ◇೔ຊ੓෎ʮਓؒத৺ͷ"*ࣾձݪଇʯ<> ݪଇΛ೥಺ܾఆํ਑ʁ ◇&6ʮ&UIJDT (VJEFMJOFTGPSUSVTUXPSUIZ"*ʯ<> ◇*&&&ʮ&UIJDBMMZ "MJHOFE%FTJHOʯ<> ◇0&$%ʮ3FDPNNFOEBUJPOPGUIF$PVODJMPO "SUJGJDJBM*OUFMMJHFODFʯ<> ◇6&4$0ʮ6/&4$0`T3FDPNNFOEBUJPOPO UIF&UIJDT PG"SUJGJDJBM*OUFMMJHFODFLFZ GBDUTʯ<> u ͦͷଞɺࠃࡍతͳ૊৫ ◇ࠃ࿈ʮ)JHIMFWFM"EWJTPSZ#PEZPO"SUJGJDJBM *OUFMMJHFODFʯ<> ࣾձʹ͓͚Δ"*ͷՁ஋ΛߴΊͯɺ"*͕ࣾձͷՁ஋ΛΑΓߴΊΔͨΊʹ "*͸ຊ౰ʹ૝ఆ௨Γʹ࡞ΒΕͯ࢖ΘΕ͍ͯΔͷ͔ʁຊ౰ʹ࢖ΘΕΔ"*ͱ͸Կ͔ʁ
  3. / 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
  4. / 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
  5. / 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
  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). 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
  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࿦ ޙ ॲ ཧ γ ε ς Ϝ Խ 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 ྺ࢙తͳภΓ ɾஉঁͰҟͳΔ ऩೖσʔλ͕஝ੵ ୅දͷภΓ ɾ೥ྸ΍ਓछͷภΓ ܭଌͷภΓ ɾܭଌޡࠩ ɾσʔλͷඪ४Խෆ଍ ධՁͷภΓ ɾෆద੾ͳධՁࢦඪ ɾϕϯνϚʔΫσʔλ͕ ฼ूஂΛ୅ද͍ͯ͠ͳ͍ ࣮૷ͷภΓ ɾ࠶൜༧ଌϞσϧΛܐظܾఆ γεςϜʹ૊ΈࠐΉ ɾར༻ऀͷࣗ཯ੑΛଛͳ͏ઃܭ
  9. / 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
  10. / 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
  11. / 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
  12. / 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࿩୊
  13. / 86  ͓͜ͱΘΓ 02 u "* ʜ ػցֶशΛ͸͡Ίͱͨ͠਺ཧతͳΞϧΰϦζϜʹجͮ͘ख๏શൠ ࣗಈԽγεςϜͳͲͷ࿩୊΋ؚ·ΕΔ

    u ೝ஌όΠΞεʜߴ࣍ೝ஌ಛੑʹىҼͯ͠ɺਓؒͷҙࢥܾఆ͕ภΔ͜ͱ AI։ൃऀ͕ཧ૝తͩͱ૝ఆ͢ΔͰ͋Ζ͏ ҙࢥܾఆΛج४ͱͯ͠ ͜ͱ͹ͷఆٛ u "*͕ਓؒΛ׬શʹ୅ସ͢Δ৔໘ʢҙࢥܾఆʹਓ͕ؒҰ੾ؔ༩͠ͳ͍৔໘ʣ u ࠩผɺެฏੑɺσδλϧɾσΟόΠυ u ϑΣΠΫχϡʔεɺEJTJOGPSNBUJPO u ηΩϡϦςΟɺ৘ใ࿙Ӯɺݸਓ৘ใอޢ u ࣗવݴޠॲཧɾը૾ॲཧɾϩϘοτ΍ΤʔδΣϯτʹݶఆ͞Εͨ࿩ ࠓ೔͸࿩͞ͳ͍͜ͱ ஫ʣͱͯ΋޿͍ҙຯͰ࢖͍ͬͯ·͢ɻ
  14. / 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
  15. / 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 ղܾͷͨΊͷݚڀ Ø ٕज़తͳհೖ Ø ೝ஌ಛੑΛ௚઀తʹѻ͏հೖ Ø ͦͷଞ u ܭଌͷෆ͔֬͞ɺܭଌٕज़։ൃ ᵋ #.*ٕज़ɺηϯαʔ։ൃɺͳͲ u σʔλɾ৘ใަ׵ن໿ͷඪ४Խ ࠓ೔͸࿩͞ͳ͍͜ͱ
  16. / 86  ໨࣍ 10  σʔλ࡞੒ͱೝ஌όΠΞε Ø ݸਓͰͷҙࢥܾఆ Ø

    ूஂతҙࢥܾఆ Ø σʔλԽʹΑΔ৘ใͷܽམ Ø ҉໧తͳ஌Λѻ͍͍ͨ ղܾͷͨΊͷݚڀ Ø ٕज़తͳհೖ Ø ೝ஌ಛੑΛ௚઀తʹѻ͏հೖ Ø ͦͷଞ
  17. / 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
  18. / 86  ࡞੒͞ΕΔσʔλͷภΓ1 11 WJTVBMQSFTFOUBUJPOSPVOEOVNCFS74QSFDJTFOVNCFSͳͲ u ཭ࢄई౓Likertई౓͸Կஈ֊ʹ͢΂͖͔ [2] u

    ਺஋هࡌΩϦͷ͍͍਺ࣈ(round number) ʹภΔ [3] u બ୒ࢶઃܭʹΑΓճ౴͕࿪Ή[4] [1] [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] Leung, S. O. (2011). A comparison of psychometric properties and normality in 4-, 5-, 6-, and 11-point Likert scales. Journal of social service research, 37(4), 412-421. [3] Jansen, C. J., & Pollmann, M. M. (2001). On round numbers: Pragmatic aspects of numerical expressions. Journal of quantitative linguistics, 8(3), 187-201. [4] ૿ాΒɺ৺ཧֶ͕ඳ͘ϦεΫͷੈք Advancedɺܚጯٛक़େֶग़൛ձɺ2023ɺp.12-21
  19. / 86  ࡞੒͞ΕΔσʔλͷภΓ2 12   https://www.toyo.co.jp/medica l/casestudy/detail/id=5525 https://github.com/ieee8023/c

    ovid-chestxray-dataset #$72A5,0@ : KHMC 38/,8 ※εϥΠυʹهࡌ ͨ͠ϥϕϧ͸ྫͰ͢
  20. / 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 #$72A5,0@ : KHMC 38/,8
  21. / 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 #$72A5,0@ : KHMC 38/,8
  22. / 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
  23. / 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
  24. / 86  ࡞੒͞ΕΔσʔλͷภΓ2 12 ϓϥΠϛϯάʢQSJNJOHʣΞϯΧϦϯάʢBODIPSJOHʣ [3] ਓؒͷ൓Ԡ͕ɺલͷܹࢗʹӨڹΛड͚Δ < 

    > [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.
  25. / 86  ࡞੒͞ΕΔσʔλͷภΓ2 12 ϓϥΠϛϯάʢQSJNJOHʣΞϯΧϦϯάʢBODIPSJOHʣ [3] ਓؒͷ൓Ԡ͕ɺલͷܹࢗʹӨڹΛड͚Δ < 

    > [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.
  26. / 86  ࡞੒͞ΕΔσʔλͷภΓ2 12 ϓϥΠϛϯάʢQSJNJOHʣΞϯΧϦϯάʢBODIPSJOHʣ ਓؒͷ൓Ԡ͕ɺલͷܹࢗʹӨڹΛड͚Δ <  >

    [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]
  27. / 86  ࡞੒͞ΕΔσʔλͷภΓ3 13 ·ͱΊ A B A A

    A B ͋ͬ… Bͬ͢ϋΠ B A ͩͬ ͭͬͯΜͩΖΦϥ Aාͬ Aාͬ AશձҰக͢͝ʔ͍
  28. / 86  ࡞੒͞ΕΔσʔλͷภΓ3 13 ܦ࿏ґଘੑಉௐ ूஂ 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]
  29. / 86  σʔλԽʹΑΔ৘ใͷܽམ1 ա౓ͳ୯७Խ 14  & *$#' %(")!

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  30. / 86  σʔλԽʹΑΔ৘ใͷܽམ1 ա౓ͳ୯७Խ 14 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).  & *$#' %(")! lDCJd A@H?5D6J> 9711J>9,;2; 2 5>@ BA2=B C@AE 8@.+A6/
  31. / 86  σʔλԽʹΑΔ৘ใͷܽམ2 Ұҙʹఆ·Βͳ͍஌ 15 Krauss, J. C., Boonstra,

    P. S., Vantsevich, A. V., & Friedman, C. P. (2016). Is the problem list in the eye of the beholder? An exploration of consistency across physicians. Journal of the American Medical Informatics Association, 23(5), 859-865. Kagawa, R., Shinohara, E., Imai, T., Kawazoe, Y., & Ohe, K. (2019). Bias of inaccurate disease mentions in electronic health record-based phenotyping. International Journal of Medical Informatics, 124, 90-96. A@H?5D6J> 9711J>9,;2; 2 5>@ BA2=B C@AE 8@.+A6/  & *$#' %(")! lDCJd Y]dDC; f9 .ÆÜÃÙ";
  32. / 86  ղܾʹ޲͚ͨࢼΈ 16 ٕ ज़ ։ ൃ u

    φοδҙࢥܾఆΛಛఆͷํ޲ʹม͑Δબ୒ΞʔΩςΫνϟͷཁૉ <> u ϒʔετೝ஌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 <>
  33. / 86  ղܾʹ޲͚ͨࢼΈ 16 ٕ ज़ ։ ൃ u

    φοδҙࢥܾఆΛಛఆͷํ޲ʹม͑Δબ୒ΞʔΩςΫνϟͷཁૉ <> u ϒʔετೝ஌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 <>
  34. / 86  ղܾʹ޲͚ͨࢼΈ 16 ٕ ज़ ։ ൃ u

    φοδҙࢥܾఆΛಛఆͷํ޲ʹม͑Δબ୒ΞʔΩςΫνϟͷཁૉ <> u ϒʔετೝ஌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 <>
  35. / 86  ·ͱΊ 19 Θ͔͍ͬͯΔ͜ͱɾͰ͖͍ͯΔ͜ͱ ͜Ε͔Βͷ՝୊ u AIɾσʔλαΠΤϯε෼໺ʹ͓͚Δೝ஌όΠΞεͷཧղ ◇

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  36. / 86  ͸͡Ίʹ 20  ਆͷΈͧ஌Δ ਅͷੈք σʔλੜ੒ α

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  37. / 86  ͸͡Ίʹ 20  ֶशɾਪ࿦ ޙ ॲ ཧ

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  38. / 86  ͸͡Ίʹ 20  ֶशɾਪ࿦ ޙ ॲ ཧ

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  39. / 86  ໨࣍ 20  ਓؒ͸"*Λద੾ʹ࢖͑ͳ͍ Ø "*ͷաখධՁͱաେධՁ ղܾͷج൫ͱͳΓ͏Δݚڀ

    Ø "*ٕज़։ൃܥ Ø આ໌ՄೳੑɾղऍՄೳੑ Ø ৴པɺઆಘ Ø ػցֶशΞϧΰϦζϜͷ։ൃ Ø ਓؒͷҙࢥܾఆաఔΛཧղ͢Δݚڀ
  40. / 86  AI΁ͷաখධՁɾաେධՁ 21 աখධՁ "*Λॿݴͱͯ͠ར༻  ࣾձ৺ཧֶ +VEHF"EWJTPS4ZTUFN

    +"4 ʹै͏ o ҩྍ਍அࢧԉ o ࠶൜༧ଌ ͳͲ "*Λ؂ࢹ͢Δ  ΤϧΰϊϛΫεɾώϡʔϚϯϑΝΫλʔ o ඈߦػͷࣗಈૢॎ o ंͷࣗಈӡస Ϩϕϧ2ʙ4 o ޻৔಺Ͱͷނোݕ஌ ͳͲ ࣗݾத৺త ॿݴׂҾ <> ࠷ॳʹਓ͔"*͔ΛબͿ  ϚʔέςΟϯά o ঎඼ߪೖɾ޿ࠂ ͳͲ ࣗಈԽγεςϜ EJTVTF<> NJTVTF<> BMHPSJUINBWFSTJPO ớ Ξ ϧ ΰ Ϧ ζ Ϝ ݏ ѱ Ờ <  > DPNQMBDFODZ <> աେධՁ NBDIJOFIFVSJTUJDT<  > QFSGFDUBVUPNBUJPOTDIFNB<> BVUPNBUJPO CJBT<> 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
  41. / 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
  42. / 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
  43. / 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
  44. / 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
  45. / 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
  46. / 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
  47. / 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   
  48. / 86  AI΁ͷաখධՁɾաେධՁ 21 աখධՁ "*Λॿݴͱͯ͠ར༻  ࣾձ৺ཧֶ +VEHF"EWJTPS4ZTUFN

    +"4 ʹै͏ o ҩྍ਍அࢧԉ o ࠶൜༧ଌ ͳͲ "*Λ؂ࢹ͢Δ  ΤϧΰϊϛΫεɾώϡʔϚϯϑΝΫλʔ o ඈߦػͷࣗಈૢॎ o ंͷࣗಈӡస Ϩϕϧ2ʙ4 o ޻৔಺Ͱͷނোݕ஌ ͳͲ ࣗݾத৺త ॿݴׂҾ <> ࠷ॳʹਓ͔"*͔ΛબͿ  ϚʔέςΟϯά o ঎඼ߪೖɾ޿ࠂ ͳͲ ࣗಈԽγεςϜ EJTVTF<> NJTVTF<> BMHPSJUINBWFSTJPO ớ Ξ ϧ ΰ Ϧ ζ Ϝ ݏ ѱ Ờ <  > DPNQMBDFODZ <> աେධՁ NBDIJOFIFVSJTUJDT<  > QFSGFDUBVUPNBUJPOTDIFNB<> BVUPNBUJPO CJBT<> 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
  49. / 86  AI΁ͷաখධՁɾաେධՁ 21 աখධՁ ΞϧΰϦζϜݏѱʢBMHPSJUINBWFSTJPOʣ "*΋ؒ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]
  50. / 86  AI΁ͷաখධՁɾաେධՁ 21 աখධՁ ࣗݾத৺తॿݴׂҾʢFHPDFOUSJDBEWJDFEJTDPVOUJOHʣ ࣗ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] ˞͜ͷҰ࿈ͷաఔΛ+VEHFBEWJTPSTZTUFNʢ+"4ʣͱݺͿɻඪ४తͳ࣮ݧखॱͰ΋͋Δ
  51. / 86  AI΁ͷաখධՁɾաେධՁ 21 աେධՁ ࣗಈԽόΠΞεʢBVUPNBUJPOCJBTʣ ࣗ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. "*ͷจ຺ߦ੓ [2]ɺاۀਓࣄ [3]ɺҩྍ [4] ͳͲ ;ͩΜ͔ΒࢧԉγεςϜΛར༻ͯ͠൑அ͍ͯ͠ΔͱɺࢧԉγεςϜ͕ͳ͘ͳͬͨͱ͖ʹɺ ൑அʹඞཁͳঢ়گͷݟམͱ͕͠૿͑ͨɻ λεΫը໘ ܭث1 ܭث2 ࢧԉγεςϜ͕ Πϕϯτʹج͖ͮ ߦಈΛਪન ࢀՃऀͷ൒෼͸͕ۭ͜͜ཝ ࣮ݧϓϩτίϧ [1]
  52. / 86  ໨࣍ 20 1. ਓؒ͸AIΛద੾ʹ࢖͑ͳ͍ Ø AIͷաখධՁͱաେධՁ ղܾͷج൫ͱͳΓ͏Δݚڀ

    Ø "*ٕज़։ൃܥ Ø આ໌ՄೳੑɾղऍՄೳੑ Ø ৴པɺઆಘ Ø ػցֶशΞϧΰϦζϜͷ։ൃ Ø ਓؒͷҙࢥܾఆաఔΛཧղ͢Δݚڀ
  53. / 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]
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    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
  55. / 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
  56. / 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
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    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
  58. / 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 WTʮΞϝϦΧਓ͋ͨΓ໿ υϧʯ
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    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.
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  64. / 86  AIΛ࢖͏ਓؒͷҙࢥܾఆաఔΛཧղ͍ͨ͠ 26 ҙࢥܾఆաఔʹӨڹ͢ΔཁૉɿλεΫ u ٬؍తͩͱײ͡ΔλεΫͰ"*ͷճ౴Λ࠾༻͠΍͍͢ <> u

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  65. / 86  AIΛ࢖͏ਓؒͷҙࢥܾఆաఔΛཧղ͍ͨ͠ 26 ҙࢥܾఆաఔʹӨڹ͢Δཁૉɿҙࢥܾఆऀ ஌ͬͯಘ͢Δଌఆई౓ u ೝ஌ख़ྀੑςετʢ$PHOJUJWF3FqFDUJPOUFTUɿ$35ʣ<> u

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