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題名 談話型AI如何扭曲與塑形全新的使用者體驗？
How do biases in conversational artificial intelligence distort and shape a new user experience?作者 舒天宓
Saquet, Thémis貢獻者 莊皓鈞
Chuang, Howard
舒天宓
Thémis Saquet關鍵詞 偏見
對話式人工智能
對話式營銷
用戶體驗
Bias
Conversational Artificial Intelligence
Conversational Marketing
User Experience日期 2023 上傳時間 6-Jul-2023 16:34:53 (UTC+8) 摘要 In our post-COVID-19 societies, more and more consumers rely on conversational AI such as voice assistants or chatbots to perform any kind of task, from asking for the weather to having personal conversations. Companies have seized on this demand to continue developing their conversational AI to create an ever-better user experience. However, the racial or sexist biases implemented in these AIs distort the original user experience, sometimes creating a new one depending on when the bias is implemented. We will try to analyze the effect of these different biases on the user experience and know how they can distort the user experience, especially depending on the moment when these biases appear. To do so, we will analyze the biases in the case of voice assistants and interactive social chatbots through a case study between XiaoIce and MicrosoftTay. We will analyze the appearance of these biases and their effects using the three-stage framework for artificial intelligence in marketing. The main conclusions are that racial biases, mainly embedded because of insufficiently diverse data and engineers, and gender biases, tend to reinforce the structural inequalities that affect our societies. By reinforcing these inequalities, the user experience is negatively impacted in terms of accessibility, representation, and experience conveyed by the use of the product. 參考文獻 Bibliography\n1. De Cosmo, L. (2022) Google Engineer claims AI chatbot is sentient: Why that matters, Scientific American. Scientific American.\nAvailable at: https://www.scientificamerican.com/article/google-engineer-claims-ai-chatbot-is-sentient-why-that-matters/\n2. Pourquoi l`IA Conversationnelle ? IBM.\nAvailable at: https://www.ibm.com/fr-fr/topics/conversational-ai#:~:text=L`intelligence%20artificielle%20(IA),auxquelles%20les%20utilisateurs%20peuvent%20parler\n3. Le fonctionnement des assistants vocaux en 5 étapes (2020) CNIL. Commission nationale de l`informatique et des libertés.\nAvailable at: https://www.cnil.fr/fr/le-fonctionnement-des-assistants-vocaux-en-5-etapes#:~:text=Un%20assistant%20vocal%20est%20un,la%20requ%C3%AAte%20d`un%20utilisateur\n4. Qu`est-Ce Qu`un Biais Cognitif ? Définition Biais cognitif (2023) USABILIS.\nAvailable at: https://www.usabilis.com/definition-biais-cognitifs/\n5. Spradlin, L.K. (2012) Diversity matters: Understanding diversity in schools. Belmont, CA: Wadsworth, Cengage Learning.\n6. Braswell, P. (2022) This is the difference between racism and racial bias, Fast Company. Available at: https://www.fastcompany.com/90796690/this-is-the-difference-between-racism-and-racial-bias\n7. Eliminating gender bias in conversational AI: Strategies for fair and Inclusive Conversations (2023) Aivo.\nAvailable at: https://www.aivo.co/blog/eliminating-gender-bias-in-conversational-ai-strategies-for-fair-and-inclusive-conversations\n8. User experience (2020) What is User Experience? | Definition and Overview.\nAvailable at: https://www.productplan.com/glossary/user-experience/\n9. What is user experience (UX) design? (2022) The Interaction Design Foundation. Interaction Design Foundation.\nAvailable at: https://www.interaction-design.org/literature/topics/ux-design\n10. Huang, M.-H. and Rust, R.T. (2020) A strategic framework for artificial intelligence in marketing - journal of the Academy of Marketing Science, SpringerLink. Springer US. Available at: https://link.springer.com/article/10.1007/s11747-020-00749-9\n11. Edison Research (2020) The smart audio report 2020 from NPR and Edison Research, Edison Research. Edison Research http://www.edisonresearch.com/wp-content/uploads/2014/06/edison-logo-300x137.jpg.\nAvailable at: https://www.edisonresearch.com/the-smart-audio-report-2020-from-npr-and-edison-research/\n12. Transcript: Ai from above (EP3) (2022) The Internet Health Report 2022.\nAvailable at: https://2022.internethealthreport.org/transcript-ai-from-above-ep3/\n13. Harwell, D. (2018) The accent gap: How Amazon`s and Google`s smart speakers leave certain voices behind, The Washington Post. WP Company.\nAvailable at: https://www.washingtonpost.com/graphics/2018/business/alexa-does-not-understand-your-accent/\n14. Sidnell, J. (2019). African American Vernacular English. [online] Hawaii.edu.\nAvailable at: https://www.hawaii.edu/satocenter/langnet/definitions/aave.html.\n15. Koenecke, A. et al. (2020) Racial disparities in automated speech recognition | PNAS, Proceedings of the National Academy of Sciences, PNAS.\nAvailable at: https://www.pnas.org/doi/10.1073/pnas.1915768117\n16. Online resources for African American language (no date) CORAAL | Online Resources for African American Language.\nAvailable at: https://oraal.uoregon.edu/coraal\n17. Lloreda, C.L. (2020). Speech Recognition Tech Is Yet Another Example of Bias.\nScientific American.\nAvailable at: https://www.scientificamerican.com/article/speech-recognition-tech-is-yet-another-example-of-bias/\n\n18. Cantone, J.A., Martinez, L.N., Willis-Esqueda, C. and Miller, T. (2019). Sounding guilty:\nHow accent bias affects juror judgments of culpability. Journal of Ethnicity in Criminal Justice, 17(3), pp.228–253.\ndoi:https://doi.org/10.1080/15377938.2019.1623963\n\n19. Baquiran, C.L.C. and Nicoladis, E. (2019). A Doctor’s Foreign Accent Affects\nPerceptions of Competence. Health Communication, 35(6), pp.726–730. doi:https://doi.org/10.1080/10410236.2019.1584779\n\n20. Baraniuk, C. (2022). Why your voice assistant might be sexist. [online] www.bbc.com.\nAvailable at: https://www.bbc.com/future/article/20220614-why-your-voice-assistant-might-be-sexist\n\n21. CNN, B.B.G. (n.d.). Why computer voices are mostly female. [online] CNN.\nAvailable at: https://edition.cnn.com/2011/10/21/tech/innovation/female-computer-voices/\n\n22. Gizmodo. (n.d.). No, Women’s Voices Are Not Easier to Understand Than Men’s Voices.\n[online]\nAvailable at: https://gizmodo.com/no-siri-is-not-female-because-womens-voices-are-easier-1683901643\n\n23. Halo· (2023). How many copies did Halo sell? — 2023 statistics | LEVVVEL. [online]\nlevvvel.com.\nAvailable at: https://levvvel.com/halo-statistics/#:~:text=The%20Halo%20series%20has%20sold%2081%20million%20copies.&text=The%20series%20went%20from%2065\n\n24. Franceinfo. (2020). Nouveau monde. Pourquoi les prénoms de Siri et d’Alexa pour des\nassistants vocaux ? [online]\nAvailable at: https://www.francetvinfo.fr/replay-radio/nouveau-monde/nouveau-monde-pourquoi-les-prenoms-de-siri-et-d-alexa-pour-des-assistants-vocaux_4045705.html\n\n25. Gizmodo. (2015). Why Is My Digital Assistant So Creepy? [online]\nAvailable at: https://gizmodo.com/why-is-my-digital-assistant-so-creepy-1682216423\n\n26. World Economic Forum. (n.d.). Hey Siri, you’re sexist, finds UN report on gendered\ntechnology. [online]\nAvailable at: https://www.weforum.org/agenda/2019/05/hey-siri-youre-sexist-finds-u-n-report-on-gendered-technology/\n\n27. www.ibm.com. (n.d.). Qu’est-ce qu’un agent conversationnel ? | IBM. [online]\nAvailable at: https://www.ibm.com/fr-fr/topics/chatbots\n\n28., 29. & 30. Salesforce.com. (n.d.). State of the Connected Customer Report. [online]\nAvailable at: https://www.salesforce.com/resources/research-reports/state-of-the-connected-customer/.\n\n31. Drift. (2021). 2021 State of Conversational Marketing. [online]\nAvailable at: https://www.drift.com/books-reports/conversational-marketing-trends/#state-of-convo-marketing.\n\n32. Weizenbaum, J. (1966). ELIZA---a computer program for the study of natural language\ncommunication between man and machine. Communications of the ACM, [online] 9(1), pp.36–45.\ndoi:https://doi.org/10.1145/365153.365168.\n\n33. Colby, K.M., Weber, S. and Hilf, F.D. (1971). Artificial Paranoia. Artificial Intelligence,\n2(1), pp.1–25.\ndoi:https://doi.org/10.1016/0004-3702(71)90002-6.\n\n34. Wallace, R.S. (2007). The Anatomy of A.L.I.C.E. Parsing the Turing Test, [online]\npp.181–210.\ndoi:https://doi.org/10.1007/978-1-4020-6710-5_13.\n\n35. Zhou, L., Gao, J., Li, D. and Shum, H.-Y. (2020). The Design and Implementation of\nXiaoIce, an Empathetic Social Chatbot. Computational Linguistics, 46(1), pp.53–93.\ndoi:https://doi.org/10.1162/coli_a_00368.\n\n36. Quach, K. (n.d.). Microsoft chatbots: Sweet XiaoIce vs foul-mouthed Tay. [online]\nwww.theregister.com.\nAvailable at: https://www.theregister.com/2016/09/29/microsofts_chatbots_show_cultural_differences_between_the_east_and_west/\n\n37. Microsoft chatbot is taught to swear on Twitter. (2016). BBC News. [online] 24 Mar.\nAvailable at: https://www.bbc.com/news/technology-35890188.\n\n38. Hunt, E. (2018). Tay, Microsoft’s AI chatbot, gets a crash course in racism from Twitter.\n[online] The Guardian.\nAvailable at: https://www.theguardian.com/technology/2016/mar/24/tay-microsofts-ai-chatbot-gets-a-crash-course-in-racism-from-twitter.\n\n39. Explains, K. (2020). Remembering Microsoft’s Chatbot disaster. [online] Medium.\nAvailable at: https://uxplanet.org/remembering-microsofts-chatbot-disaster-3a49d4a6331f\n\n40. Editor (2016). XiaoIce Vs. Tay: Two A.I. Chatbots, Two Different Outcomes - Sampi. co.\n[online]\nAvailable at: https://sampi.co/chinese-chatbot-xiaoice-vs-tay/\n\n41. & 42. Miller, K.W., Wolf, M.J. and Grodzinsky, F.S. (2017). Why We Should Have Seen\nThat Coming. ORBIT Journal, 1(2).\ndoi:https://doi.org/10.29297/orbit.v1i2.49\n\n43. Leetaru, K. (n.d.). How Twitter Corrupted Microsoft’s Tay: A Crash Course In the\nDangers Of AI In The Real World. [online] Forbes.\nAvailable at: https://www.forbes.com/sites/kalevleetaru/2016/03/24/how-twitter-corrupted-microsofts-tay-a-crash-course-in-the-dangers-of-ai-in-the-real-world/?sh=ffeebf926d28\n\n44. https://plus.google.com/+UNESCO (2021). Problème de fille : briser les préjugés dans\nl’IA. [online] UNESCO.\nAvailable at: https://fr.unesco.org/girltrouble\n\n45. Zendesk. (n.d.). 7 ways to reduce bias in conversational AI. [online]\nAvailable at: https://www.zendesk.tw/blog/7-ways-reduce-bias-conversational-ai/#georedirect\n\n46. SoundHound. (n.d.). How to Overcome Cultural Bias in Voice AI Design. [online]\nAvailable at: https://www.soundhound.com/resources/how-to-overcome-cultural-bias-in-voice-ai-design/\n47. Dotan, R. (2023) Example of gender bias in CHATGPT, Ravit Dotan. Available at: https://www.ravitdotan.com/post/example-of-gender-bias-in-chatgpt\n48. Shafik and Case (eds.) (2022) Advances in manufacturing technology XXXV, IOS Press. Available at: https://www.iospress.com/catalog/books/advances-in-manufacturing-technology-xxxv\n49. Kotek, H. (2023) Doctors can’t get pregnant and other gender biases in chatgpt, Doctors can’t get pregnant and other gender biases in ChatGPT. Available at: https://hkotek.com/blog/gender-bias-in-chatgpt/\n\nAdditional resources\n\n1. Wheeler, D.R. (2014). Why are Cortana and Siri female? [online] CNN.\nAvailable at: https://edition.cnn.com/2014/04/04/opinion/wheeler-cortana-siri/\n\n2. Abby Ohlheiser, covering digital culture (n.d.). Trolls turned Tay, Microsoft’s fun millennial AI bot, into a genocidal maniac. [online] Washington Post.\nAvailable at: https://www.washingtonpost.com/news/the-intersect/wp/2016/03/24/the-internet-turned-tay-microsofts-fun-millennial-ai-bot-into-a-genocidal-maniac/\n\n3. Mathur, V., Stavrakas, Y. and Singh, S. (2016). Intelligence analysis of Tay Twitter bot. [online] ResearchGate.\nAvailable at: https://www.researchgate.net/publication/316727714_Intelligence_\nanalysis_of_Tay_Twitter_bot\n\n4. Nouri, S. (n.d.). Council Post: The Role Of Bias In Artificial Intelligence. [online] Forbes.\nAvailable at: https://www.forbes.com/sites/forbestechcouncil/2021/02/04/the-role-of-bias-in-artificial-intelligence/?sh=3d43f9f7579d\n\n5. UN News. (2019). Are robots sexist? UN report shows gender bias in talking digital tech.\n[online]\nAvailable at: https://news.un.org/en/story/2019/05/1038691\n\n6. West, M., Kraut, R. and Chew, H.E. (2019). I’d blush if I could: closing gender divides in\ndigital skills through education.\nAvailable at: https://unesdoc.unesco.org/ark:/48223/pf0000367416.page=1 描述 碩士
國立政治大學
國際經營管理英語碩士學位學程(IMBA)
111933056資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111933056 資料類型 thesis dc.contributor.advisor 莊皓鈞 zh_TW dc.contributor.advisor Chuang, Howard en_US dc.contributor.author (Authors) 舒天宓 zh_TW dc.contributor.author (Authors) Thémis Saquet en_US dc.creator (作者) 舒天宓 zh_TW dc.creator (作者) Saquet, Thémis en_US dc.date (日期) 2023 en_US dc.date.accessioned 6-Jul-2023 16:34:53 (UTC+8) - dc.date.available 6-Jul-2023 16:34:53 (UTC+8) - dc.date.issued (上傳時間) 6-Jul-2023 16:34:53 (UTC+8) - dc.identifier (Other Identifiers) G0111933056 en_US dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=165286 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 國際經營管理英語碩士學位學程(IMBA) zh_TW dc.description (描述) 111933056 zh_TW dc.description.abstract (摘要) In our post-COVID-19 societies, more and more consumers rely on conversational AI such as voice assistants or chatbots to perform any kind of task, from asking for the weather to having personal conversations. Companies have seized on this demand to continue developing their conversational AI to create an ever-better user experience. However, the racial or sexist biases implemented in these AIs distort the original user experience, sometimes creating a new one depending on when the bias is implemented. We will try to analyze the effect of these different biases on the user experience and know how they can distort the user experience, especially depending on the moment when these biases appear. To do so, we will analyze the biases in the case of voice assistants and interactive social chatbots through a case study between XiaoIce and MicrosoftTay. We will analyze the appearance of these biases and their effects using the three-stage framework for artificial intelligence in marketing. The main conclusions are that racial biases, mainly embedded because of insufficiently diverse data and engineers, and gender biases, tend to reinforce the structural inequalities that affect our societies. By reinforcing these inequalities, the user experience is negatively impacted in terms of accessibility, representation, and experience conveyed by the use of the product. en_US dc.description.tableofcontents 1 INTRODUCTION 1\n2 VOICE ASSISTANTS 8\n2.1 RACIAL BIAS IN VOICE ASSISTANTS 9\n2.1.1 Marketing Research: How the critical step of data collection may influence the user experience from the very conception of the product 9\n2.1.2 Marketing Strategy: How bias becomes a barrier to relevant positioning 14\n2.1.3 An empirical measurement of biases to be corrected 16\n2.1.4 Marketing Action: How a greater personalization through more diverse data and workforce is the way to go for companies 19\n2.2 GENDER BIAS IN VOICE ASSISTANTS 21\n2.2.1 Marketing Research and Strategy: How has a persisting historical bias made its way to these critical steps around the user experience? 21\n2.2.2 Marketing Action: How better marketing practices can help stop these everlasting stereotypes? 27\n3 CHATBOT 29\n3.1 CONVERSATIONAL MARKETING 30\n3.1.1 The benefits of unbiased conversational marketing 32\n3.1.2 What strategies are used for conversational marketing? 39\n3.1.3 Creating a bias-free user experience thanks to conversational marketing by leveraging AI in the different stages of marketing 40\n3.1.3.1 Marketing Research 40\n3.1.3.2 Marketing Strategy 42\n3.1.3.3 Marketing Action 43\n3.2 THE STUDY CASE OF INTERACTIVE SOCIAL CHATBOTS: HOW LETTING USERS IMPLEMENT THOSE BIASES CAN LEAD TO A TERRIBLE USER EXPERIENCE 44\n4 CONCLUSION 52\n5 BIBLIOGRAPHY 56\n6 ADDITIONAL RESOURCES 61 zh_TW dc.format.extent 2705327 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111933056 en_US dc.subject (關鍵詞) 偏見 zh_TW dc.subject (關鍵詞) 對話式人工智能 zh_TW dc.subject (關鍵詞) 對話式營銷 zh_TW dc.subject (關鍵詞) 用戶體驗 zh_TW dc.subject (關鍵詞) Bias en_US dc.subject (關鍵詞) Conversational Artificial Intelligence en_US dc.subject (關鍵詞) Conversational Marketing en_US dc.subject (關鍵詞) User Experience en_US dc.title (題名) 談話型AI如何扭曲與塑形全新的使用者體驗？ zh_TW dc.title (題名) How do biases in conversational artificial intelligence distort and shape a new user experience? en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Bibliography\n1. De Cosmo, L. (2022) Google Engineer claims AI chatbot is sentient: Why that matters, Scientific American. Scientific American.\nAvailable at: https://www.scientificamerican.com/article/google-engineer-claims-ai-chatbot-is-sentient-why-that-matters/\n2. Pourquoi l`IA Conversationnelle ? IBM.\nAvailable at: https://www.ibm.com/fr-fr/topics/conversational-ai#:~:text=L`intelligence%20artificielle%20(IA),auxquelles%20les%20utilisateurs%20peuvent%20parler\n3. Le fonctionnement des assistants vocaux en 5 étapes (2020) CNIL. Commission nationale de l`informatique et des libertés.\nAvailable at: https://www.cnil.fr/fr/le-fonctionnement-des-assistants-vocaux-en-5-etapes#:~:text=Un%20assistant%20vocal%20est%20un,la%20requ%C3%AAte%20d`un%20utilisateur\n4. Qu`est-Ce Qu`un Biais Cognitif ? Définition Biais cognitif (2023) USABILIS.\nAvailable at: https://www.usabilis.com/definition-biais-cognitifs/\n5. Spradlin, L.K. (2012) Diversity matters: Understanding diversity in schools. Belmont, CA: Wadsworth, Cengage Learning.\n6. Braswell, P. (2022) This is the difference between racism and racial bias, Fast Company. Available at: https://www.fastcompany.com/90796690/this-is-the-difference-between-racism-and-racial-bias\n7. Eliminating gender bias in conversational AI: Strategies for fair and Inclusive Conversations (2023) Aivo.\nAvailable at: https://www.aivo.co/blog/eliminating-gender-bias-in-conversational-ai-strategies-for-fair-and-inclusive-conversations\n8. User experience (2020) What is User Experience? | Definition and Overview.\nAvailable at: https://www.productplan.com/glossary/user-experience/\n9. What is user experience (UX) design? (2022) The Interaction Design Foundation. Interaction Design Foundation.\nAvailable at: https://www.interaction-design.org/literature/topics/ux-design\n10. Huang, M.-H. and Rust, R.T. (2020) A strategic framework for artificial intelligence in marketing - journal of the Academy of Marketing Science, SpringerLink. Springer US. Available at: https://link.springer.com/article/10.1007/s11747-020-00749-9\n11. Edison Research (2020) The smart audio report 2020 from NPR and Edison Research, Edison Research. Edison Research http://www.edisonresearch.com/wp-content/uploads/2014/06/edison-logo-300x137.jpg.\nAvailable at: https://www.edisonresearch.com/the-smart-audio-report-2020-from-npr-and-edison-research/\n12. Transcript: Ai from above (EP3) (2022) The Internet Health Report 2022.\nAvailable at: https://2022.internethealthreport.org/transcript-ai-from-above-ep3/\n13. Harwell, D. (2018) The accent gap: How Amazon`s and Google`s smart speakers leave certain voices behind, The Washington Post. WP Company.\nAvailable at: https://www.washingtonpost.com/graphics/2018/business/alexa-does-not-understand-your-accent/\n14. Sidnell, J. (2019). African American Vernacular English. [online] Hawaii.edu.\nAvailable at: https://www.hawaii.edu/satocenter/langnet/definitions/aave.html.\n15. Koenecke, A. et al. (2020) Racial disparities in automated speech recognition | PNAS, Proceedings of the National Academy of Sciences, PNAS.\nAvailable at: https://www.pnas.org/doi/10.1073/pnas.1915768117\n16. Online resources for African American language (no date) CORAAL | Online Resources for African American Language.\nAvailable at: https://oraal.uoregon.edu/coraal\n17. Lloreda, C.L. (2020). Speech Recognition Tech Is Yet Another Example of Bias.\nScientific American.\nAvailable at: https://www.scientificamerican.com/article/speech-recognition-tech-is-yet-another-example-of-bias/\n\n18. Cantone, J.A., Martinez, L.N., Willis-Esqueda, C. and Miller, T. (2019). Sounding guilty:\nHow accent bias affects juror judgments of culpability. Journal of Ethnicity in Criminal Justice, 17(3), pp.228–253.\ndoi:https://doi.org/10.1080/15377938.2019.1623963\n\n19. Baquiran, C.L.C. and Nicoladis, E. (2019). A Doctor’s Foreign Accent Affects\nPerceptions of Competence. Health Communication, 35(6), pp.726–730. doi:https://doi.org/10.1080/10410236.2019.1584779\n\n20. Baraniuk, C. (2022). Why your voice assistant might be sexist. [online] www.bbc.com.\nAvailable at: https://www.bbc.com/future/article/20220614-why-your-voice-assistant-might-be-sexist\n\n21. CNN, B.B.G. (n.d.). Why computer voices are mostly female. [online] CNN.\nAvailable at: https://edition.cnn.com/2011/10/21/tech/innovation/female-computer-voices/\n\n22. Gizmodo. (n.d.). No, Women’s Voices Are Not Easier to Understand Than Men’s Voices.\n[online]\nAvailable at: https://gizmodo.com/no-siri-is-not-female-because-womens-voices-are-easier-1683901643\n\n23. Halo· (2023). How many copies did Halo sell? — 2023 statistics | LEVVVEL. [online]\nlevvvel.com.\nAvailable at: https://levvvel.com/halo-statistics/#:~:text=The%20Halo%20series%20has%20sold%2081%20million%20copies.&text=The%20series%20went%20from%2065\n\n24. Franceinfo. (2020). Nouveau monde. Pourquoi les prénoms de Siri et d’Alexa pour des\nassistants vocaux ? [online]\nAvailable at: https://www.francetvinfo.fr/replay-radio/nouveau-monde/nouveau-monde-pourquoi-les-prenoms-de-siri-et-d-alexa-pour-des-assistants-vocaux_4045705.html\n\n25. Gizmodo. (2015). Why Is My Digital Assistant So Creepy? [online]\nAvailable at: https://gizmodo.com/why-is-my-digital-assistant-so-creepy-1682216423\n\n26. World Economic Forum. (n.d.). Hey Siri, you’re sexist, finds UN report on gendered\ntechnology. [online]\nAvailable at: https://www.weforum.org/agenda/2019/05/hey-siri-youre-sexist-finds-u-n-report-on-gendered-technology/\n\n27. www.ibm.com. (n.d.). Qu’est-ce qu’un agent conversationnel ? | IBM. [online]\nAvailable at: https://www.ibm.com/fr-fr/topics/chatbots\n\n28., 29. & 30. Salesforce.com. (n.d.). State of the Connected Customer Report. [online]\nAvailable at: https://www.salesforce.com/resources/research-reports/state-of-the-connected-customer/.\n\n31. Drift. (2021). 2021 State of Conversational Marketing. [online]\nAvailable at: https://www.drift.com/books-reports/conversational-marketing-trends/#state-of-convo-marketing.\n\n32. Weizenbaum, J. (1966). ELIZA---a computer program for the study of natural language\ncommunication between man and machine. Communications of the ACM, [online] 9(1), pp.36–45.\ndoi:https://doi.org/10.1145/365153.365168.\n\n33. Colby, K.M., Weber, S. and Hilf, F.D. (1971). Artificial Paranoia. Artificial Intelligence,\n2(1), pp.1–25.\ndoi:https://doi.org/10.1016/0004-3702(71)90002-6.\n\n34. Wallace, R.S. (2007). The Anatomy of A.L.I.C.E. Parsing the Turing Test, [online]\npp.181–210.\ndoi:https://doi.org/10.1007/978-1-4020-6710-5_13.\n\n35. Zhou, L., Gao, J., Li, D. and Shum, H.-Y. (2020). The Design and Implementation of\nXiaoIce, an Empathetic Social Chatbot. Computational Linguistics, 46(1), pp.53–93.\ndoi:https://doi.org/10.1162/coli_a_00368.\n\n36. Quach, K. (n.d.). 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Dotan, R. (2023) Example of gender bias in CHATGPT, Ravit Dotan. Available at: https://www.ravitdotan.com/post/example-of-gender-bias-in-chatgpt\n48. Shafik and Case (eds.) (2022) Advances in manufacturing technology XXXV, IOS Press. Available at: https://www.iospress.com/catalog/books/advances-in-manufacturing-technology-xxxv\n49. Kotek, H. (2023) Doctors can’t get pregnant and other gender biases in chatgpt, Doctors can’t get pregnant and other gender biases in ChatGPT. Available at: https://hkotek.com/blog/gender-bias-in-chatgpt/\n\nAdditional resources\n\n1. Wheeler, D.R. (2014). Why are Cortana and Siri female? [online] CNN.\nAvailable at: https://edition.cnn.com/2014/04/04/opinion/wheeler-cortana-siri/\n\n2. Abby Ohlheiser, covering digital culture (n.d.). Trolls turned Tay, Microsoft’s fun millennial AI bot, into a genocidal maniac. [online] Washington Post.\nAvailable at: https://www.washingtonpost.com/news/the-intersect/wp/2016/03/24/the-internet-turned-tay-microsofts-fun-millennial-ai-bot-into-a-genocidal-maniac/\n\n3. Mathur, V., Stavrakas, Y. and Singh, S. (2016). Intelligence analysis of Tay Twitter bot. [online] ResearchGate.\nAvailable at: https://www.researchgate.net/publication/316727714_Intelligence_\nanalysis_of_Tay_Twitter_bot\n\n4. Nouri, S. (n.d.). Council Post: The Role Of Bias In Artificial Intelligence. [online] Forbes.\nAvailable at: https://www.forbes.com/sites/forbestechcouncil/2021/02/04/the-role-of-bias-in-artificial-intelligence/?sh=3d43f9f7579d\n\n5. UN News. (2019). Are robots sexist? UN report shows gender bias in talking digital tech.\n[online]\nAvailable at: https://news.un.org/en/story/2019/05/1038691\n\n6. West, M., Kraut, R. and Chew, H.E. (2019). I’d blush if I could: closing gender divides in\ndigital skills through education.\nAvailable at: https://unesdoc.unesco.org/ark:/48223/pf0000367416.page=1 zh_TW
