Sharon A Ferguson
ABSTRACT
Recent advances in Human-AI interaction have highlighted the possibility of employing AI in collaborative decision-making contexts, particularly in cases where the decision is subjective, without one ground truth. In these contexts, researchers argue that AI could be used not just to provide a final decision recommendation, but to surface new perspectives, rationales, and insights. In this late-breaking work, we describe the initial findings from an empirical study investigating how complementary AI input influences humans’ rationale in ambiguous decision-making. We use subtle sexism as an example of this context, and GPT-3 to create explanation-like text. We find that participants change the language, level of detail, and even the argumentative stance of their explanations after seeing the AI explanation text. They often borrow language directly from this complementary text. We discuss the implications for collaborative decision-making and the next steps in this research agenda.
Footnotes
1 https://openai.com/blog/chatgpt
Footnote2 Reddit ( www.reddit.com), The Everyday Sexism Project ( www.everydaysexism.com) and Twitter ( www.twitter.com)
Footnote3 https://www.surveymonkey.com/
Footnote
Supplemental Material
- Amina Adadi and Mohammed Berrada. 2018. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE access 6 (2018), 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052Google Scholar
Cross Ref
- HLEG AI. 2019. High-level expert group on artificial intelligence. Technical Report. 6 pages.Google Scholar
- Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information fusion 58 (2020), 82–115.Google ScholarDigital Library
- Gagan Bansal, Besmira Nushi, Ece Kamar, Daniel S Weld, Walter S Lasecki, and Eric Horvitz. 2019. Updates in human-ai teams: Understanding and addressing the performance/compatibility tradeoff. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 2429–2437.Google ScholarDigital Library
- Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel Weld. 2021. Does the whole exceed its parts? the effect of ai explanations on complementary team performance. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1–16.Google ScholarDigital Library
- Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77–101.Google Scholar
- Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models Are Few-Shot Learners. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Vancouver, BC, Canada) (NIPS’20). Curran Associates Inc., Red Hook, NY, USA, Article 159, 25 pages.Google ScholarDigital Library
- Erik Cambria, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica, and Navid Nobani. 2023. A survey on XAI and natural language explanations. Information Processing & Management 60, 1 (2023), 103111.Google ScholarDigital Library
- Samuel Carton, Qiaozhu Mei, and Paul Resnick. 2020. Feature-Based Explanations Don’t Help People Detect Misclassifications of Online Toxicity. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 14. 95–106.Google ScholarCross Ref
- Brian X Chen. 2022. How to Use ChatGPT and Still Be a Good Person. The New York Times (12 2022). https://www.nytimes.com/2022/12/21/technology/personaltech/how-to-use-chatgpt-ethically.htmlnGoogle Scholar
- Eric Chu, Deb Roy, and Jacob Andreas. 2020. Are visual explanations useful? a case study in model-in-the-loop prediction. arXiv preprint arXiv:2007.12248 (2020).Google Scholar
- Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey. 2015. Algorithm aversion: people erroneously avoid algorithms after seeing them err.Journal of Experimental Psychology: General 144, 1 (2015), 114.Google Scholar
- Kate Donahue, Alexandra Chouldechova, and Krishnaram Kenthapadi. 2022. Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness. In 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22). Association for Computing Machinery, New York, NY, USA, 1639–1656. https://doi.org/10.1145/3531146.3533221Google ScholarDigital Library
- D Doran, SC Schulz, and TR Besold. 2018. What Does Explainable AI Really Mean? A New Conceptualization of Perspectives. In CEUR Workshop Proceedings, Vol. 2071.Google Scholar
- Ben Green and Yiling Chen. 2019. The principles and limits of algorithm-in-the-loop decision making. Proceedings of the ACM on Human-Computer Interaction 3, CSCW (2019), 1–24.Google ScholarDigital Library
- Ronan Hamon, Henrik Junklewitz, Ignacio Sanchez, Gianclaudio Malgieri, and Paul De Hert. 2022. Bridging the gap between AI and explainability in the GDPR: towards trustworthiness-by-design in automated decision-making. IEEE Computational Intelligence Magazine 17, 1 (2022), 72–85.Google ScholarDigital Library
- Patrick Hemmer, Max Schemmer, Michael Vössing, and Niklas Kühl. 2021. Human-AI Complementarity in Hybrid Intelligence Systems: A Structured Literature Review.PACIS (2021), 78.Google Scholar
- Alex Hern. 2022. AI-assisted plagiarism? ChatGPT bot says it has an answer for that. The Guardian (2022). https://www.theguardian.com/technology/2022/dec/31/ai-assisted-plagiarism-chatgpt-bot-says-it-has-an-answer-for-thatGoogle Scholar
- Nils Köbis and Luca D Mossink. 2021. Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry. Computers in human behavior 114 (2021), 106553.Google Scholar
- Vivian Lai, Chacha Chen, Q Vera Liao, Alison Smith-Renner, and Chenhao Tan. 2021. Towards a science of human-ai decision making: a survey of empirical studies. arXiv preprint arXiv:2112.11471 (2021).Google Scholar
- Zachary C Lipton. 2018. The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery.Queue 16, 3 (2018), 31–57.Google ScholarDigital Library
- Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage, and Himabindu Lakkaraju. 2020. When does uncertainty matter?: Understanding the impact of predictive uncertainty in ML assisted decision making. arXiv preprint arXiv:2011.06167 (2020).Google Scholar
- Tim Miller. 2019. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence 267 (2019), 1–38.Google Scholar
- Chelsea Mitamura, Lynnsey Erickson, and Patricia G Devine. 2017. Value-based standards guide sexism inferences for self and others. Journal of Experimental Social Psychology 72 (2017), 101–117.Google ScholarCross Ref
- Maayan Pereg, Uri Hertz, Ido Ben-Artzi, and Nitzan Shahar. 2022. Disentangling the contribution of individual and social learning processes in human advice-taking behavior. https://doi.org/10.31234/osf.io/28js3Google ScholarCross Ref
- Schaekermann, Mike. 2020. Human-AI Interaction in the Presence of Ambiguity: From Deliberation-based Labeling to Ambiguity-aware AI. PhD Thesis. UWSpace. http://hdl.handle.net/10012/16284Google Scholar
- Giulia Vilone and Luca Longo. 2020. Explainable artificial intelligence: a systematic review. arXiv preprint arXiv:2006.00093 (2020).Google Scholar
- Xinru Wang and Ming Yin. 2021. Are explanations helpful? a comparative study of the effects of explanations in ai-assisted decision-making. In 26th International Conference on Intelligent User Interfaces. 318–328.Google ScholarDigital Library
- Zeerak Waseem and Dirk Hovy. 2016. Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. In Proceedings of the NAACL Student Research Workshop. Association for Computational Linguistics, San Diego, California, 88–93. https://doi.org/10.18653/v1/N16-2013Google ScholarCross Ref
- Feiyu Xu, Hans Uszkoreit, Yangzhou Du, Wei Fan, Dongyan Zhao, and Jun Zhu. 2019. Explainable AI: A brief survey on history, research areas, approaches and challenges. In CCF international conference on natural language processing and Chinese computing. Springer, 563–574.Google ScholarDigital Library
- Yunfeng Zhang, Q Vera Liao, and Rachel KE Bellamy. 2020. Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 295–305.Google ScholarDigital Library
Index Terms
- Something Borrowed: Exploring the Influence of AI-Generated Explanation Text on the Composition of Human Explanations
Recommendations
Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making
CSCWArtificial intelligence (AI) is increasingly being considered to assist human decision-making in high-stake domains (e.g. health). However, researchers have discussed an issue that humans can over-rely on wrong suggestions of the AI model instead of ...
Identifying Explanation Needs of End-users: Applying and Extending the XAI Question Bank
MuC '23: Proceedings of Mensch und Computer 2023Explainable Artificial Intelligence (XAI) is concerned with making the decisions of AI systems interpretable to humans. Explanations are typically developed by AI experts and focus on algorithmic transparency and the inner workings of AI systems. ...
The Impact of Imperfect XAI on Human-AI Decision-Making
CSCWExplainability techniques are rapidly being developed to improve human-AI decision-making across various cooperative work settings. Consequently, previous research has evaluated how decision-makers collaborate with imperfect AI by investigating ...
Comments