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Trust and ethical considerations in a multi-modal, explainable AI-driven chatbot tutoring system: The case of collaboratively solving Rubik's Cube (2402.01760v2)

Published 30 Jan 2024 in cs.CY and cs.AI

Abstract: AI has the potential to transform education with its power of uncovering insights from massive data about student learning patterns. However, ethical and trustworthy concerns of AI have been raised but are unsolved. Prominent ethical issues in high school AI education include data privacy, information leakage, abusive language, and fairness. This paper describes technological components that were built to address ethical and trustworthy concerns in a multi-modal collaborative platform (called ALLURE chatbot) for high school students to collaborate with AI to solve the Rubik's cube. In data privacy, we want to ensure that the informed consent of children, parents, and teachers, is at the center of any data that is managed. Since children are involved, language, whether textual, audio, or visual, is acceptable both from users and AI and the system can steer interaction away from dangerous situations. In information management, we also want to ensure that the system, while learning to improve over time, does not leak information about users from one group to another.

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References (41)
  1. Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE access, 6:52138–52160, 2018.
  2. Solving the Rubik’s cube with deep reinforcement learning and search. Nature Machine Intelligence, 1(8):356–363, 2019.
  3. Artificial intelligence in education: Addressing ethical challenges in k-12 settings, Sep 2021. URL https://pubmed.ncbi.nlm.nih.gov/34790956/.
  4. Efficient black-box planning using macro-actions with focused effects. arXiv preprint arXiv:2004.13242, 2020.
  5. Hindsight experience replay. In Advances in Neural Information Processing Systems, pp. 5048–5058, 2017.
  6. The promise, reality and dilemmas of secondary school teacher-student interactions in facebook: The teacher perspective . Computers & Education, 85:134–148, 2015.
  7. The effect of asking about exposure to violence on ptsd symptoms: An experiment in north east nigeria. Working paper, UCLA, Unpublished.
  8. Rasa: Open source language understanding and dialogue management. arXiv preprint arXiv:1712.05181, 2017.
  9. Language models are few-shot learners. arXiv preprint arXiv:2005.14165, 2020.
  10. Learning programs by learning from failures. Machine Learning, 110(4):801–856, 2021.
  11. Automated hate speech detection and the problem of offensive language. In Proceedings of the 11th International AAAI Conference on Web and Social Media, ICWSM ’17, 2017.
  12. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
  13. Queens are powerful too: Mitigating gender bias in dialogue generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.  8173–8188, Online, November 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.emnlp-main.656. URL https://aclanthology.org/2020.emnlp-main.656.
  14. Ferenc, D. How to solve the rubik’s cube - beginners method. https://ruwix.com/the-rubiks-cube/how-to-solve-the-rubiks-cube-beginners-method/, 2021.
  15. Survey of the state of the art in natural language generation: Core tasks, applications and evaluation. J. Artif. Int. Res., 61(1):65–170, jan 2018. ISSN 1076-9757.
  16. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2):100–107, July 1968. ISSN 0536-1567. doi: 10.1109/TSSC.1968.300136.
  17. Ethical challenges in data-driven dialogue systems. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’18, pp.  123–129, New York, NY, USA, 2018. Association for Computing Machinery. ISBN 9781450360128. doi: 10.1145/3278721.3278777. URL https://doi.org/10.1145/3278721.3278777.
  18. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media, volume 8, 2014.
  19. Examining gender and race bias in two hundred sentiment analysis systems. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pp.  43–53, New Orleans, Louisiana, June 2018. Association for Computational Linguistics. doi: 10.18653/v1/S18-2005. URL https://aclanthology.org/S18-2005.
  20. Allure: A multi-modal guided environment for helping children learn to solve a rubik’s cube with automatic solving and interactive explanations. In Proc. AAAI, 2022.
  21. Rating sentiment analysis systems for bias through a causal lens, 2023. URL https://arxiv.org/abs/2302.02038.
  22. A measure for dialog complexity and its application in streamlining service operations. CoRR, abs/1708.04134, 2017. URL http://arxiv.org/abs/1708.04134.
  23. Lipton, Z. C. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3):31–57, 2018.
  24. Solving the rubik’s cube with approximate policy iteration. In International Conference on Learning Representations, 2018.
  25. McDonald, D. D. Natural language generation. Handbook of Natural Language Processing, 2:121–144, 2010.
  26. SemEval-2018 task 1: Affect in tweets. In Proceedings of The 12th International Workshop on Semantic Evaluation, pp.  1–17, New Orleans, Louisiana, June 2018. Association for Computational Linguistics. doi: 10.18653/v1/S18-1001. URL https://www.aclweb.org/anthology/S18-1001.
  27. Muggleton, S. Inductive logic programming. New generation computing, 8(4):295–318, 1991.
  28. MUNAWIR, A. Online game and childrens’s language behavior. volume 7. ISSN 2548-4192, 2019.
  29. Rose: Tool and data resources to explore the instability of sentiment analysis systems. In Research Gate, 02 2022. doi: 10.13140/RG.2.2.12533.04323.
  30. Automation, algorithms, and politics— talking to bots: Symbiotic agency and the case of tay. In International Journal Of Communication, 10, 17. Retrieved from http://ijoc.org/index.php, 2017.
  31. Pearl, J. Causality. Cambridge University Press, Cambridge, UK, 2 edition, 2009. ISBN 978-0-521-89560-6. doi: 10.1017/CBO9780511803161.
  32. Piano, S. L. Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward. Palgrave Communications, 7:1–7, 2020.
  33. Education, privacy, and big data algorithms: Taking the persons out of personalized learning. First Monday, 24(11), Nov. 2019. doi: 10.5210/fm.v24i11.10094. URL https://journals.uic.edu/ojs/index.php/fm/article/view/10094.
  34. Recipes for building an open-domain chatbot. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pp. 300–325, Online, April 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.eacl-main.24. URL https://aclanthology.org/2021.eacl-main.24.
  35. Schmidhuber, J. Deep learning in neural networks: An overview. Neural networks, 61:85–117, 2015.
  36. Student. The probable error of a mean. Biometrika, pp.  1–25, 1908.
  37. Reinforcement learning: An introduction. MIT press, 2018.
  38. Attention is all you need. In Advances in neural information processing systems, pp. 5998–6008, 2017.
  39. Vishnu, K. Sentiment analyzer using convolutional neural network. In https://digitaltesseract.com/sentiment-analyzer-using-convolutional-neural-network/, 2020.
  40. Cursing in english on twitter. In CSCW, 2014.
  41. Why we should have seen that coming: comments on microsoft’s tay experiment and wider implications. In ACM SIGCAS Computers and Society, https://doi.org/10.1145/3144592.3144598, 2017.
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