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Human participants play a central role in the development of modern artificial intelligence (AI) technology, in psychological science, and in user research.
Language models are few-shot learners
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Facebook users’ privacy concerns up since 2011
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Scientific objectivity
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The robotic imaginary: The human and the price of dehumanized labor
Jennifer Rhee. 2018 · 2018
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Surrogate humanity: Race, robots, and the politics of technological futures
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Patron or poison? Industry funding of HCI research. In Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing . 111–115
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The global landscape of AI ethics guidelines
Underfunding basic psychological science because of the primacy of the here and now: A scientific conundrum
Jorge Almeida. 2023 · 2023
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Ethical principles of psychologists and code of conduct
American Psychological Association. 2016 · 2023
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Introducing 100K Context Windows
Anthropic. 2023a · 2023
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Model Card and Evaluations for Claude Models
Anthropic. 2023b · 2023
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Out of one, many: Using language models to simulate human samples
Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua R. Gubler, Christopher Rytting, and David Wingate. 2023 · 2023
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Which humans?
Mohammad Atari, Mona J. Xue, Peter S. Park, Damián Blasi, and Joseph Henrich. 2023 · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Anna Jobin, Marcello Ienca, and Effy Vayena. 2019 · 2019
Cited alongside, same era.
Beyond equity as inclusion: A framework of “rightful presence” for guiding justice-oriented studies in teaching and learning
Angela Calabrese Barton and Edna Tan. 2020 · 2020
Cited alongside, same era.
Climbing towards NLU: On meaning, form, and understanding in the age of data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . 5185–5198
Emily M. Bender and Alexander Koller. 2020 · 2020
Cited alongside, same era.
Paper2Wire: A case study of user-centred development of machine learning tools for UX designers
Daniel Buschek, Charlotte Anlauff, and Florian Lachner. 2020 · 2020
Cited alongside, same era.
The phenomenology of between: An intersubjective epistemology for psychological science
Michael F. Mascolo and Eeva Kallio. 2020 · 2020
Cited alongside, same era.
Persistent anti-Muslim bias in large language models. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society . 298–306
Abubakar Abid, Maheen Farooqi, and James Zou. 2021 · 2021
Cited alongside, same era.
On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency . 610–623
Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Cited alongside, same era.
Representativeness in statistics, politics, and machine learning. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency . 77–89
Kyla Chasalow and Karen Levy. 2021 · 2021
Cited alongside, same era.
Representation in AI evaluations. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency . 519–533
A. Stevie Bergman, Lisa Anne Hendricks, Maribeth Rauh, Boxi Wu, William Agnew, Markus Kunesch, Isabella Duan, Iason Gabriel, and William Isaac. 2023 · 2023
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Sparks of artificial general intelligence: Early experiments with GPT-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
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Dispensing with humans in human-computer interaction research. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems . 1–26
Courtni Byun, Piper Vasicek, and Kevin Seppi. 2023 · 2023
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Can large language models be an alternative to human evaluations?. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Association for Computational Linguistics, 15607–15631
Cheng-Han Chiang and Hung-yi Lee. 2023 · 2023
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Computer-supported cooperative work
Luigina Ciolfi, Myriam Lewkowicz, and Kjeld Schmidt. 2023 · 2023
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Should large language models replace human participants?
Molly Crockett and Lisa Messeri. 2023 · 2023
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Can AI language models replace human participants?
Danica Dillion, Niket Tandon, Yuling Gu, and Kurt Gray. 2023 · 2023
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Towards measuring the representation of subjective global opinions in language models
Esin Durmus, Karina Nyugen, Thomas I. Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, et al · 2023
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Can AI serve as a substitute for human subjects in software engineering research?
Marco A. Gerosa, Bianca Trinkenreich, Igor Steinmacher, and Anita Sarma. 2023 · 2023
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ChatGPT outperforms crowd workers for text-annotation tasks
Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023 · 2023
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Going public: The role of public participation approaches in commercial AI labs. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency . 1162–1173
Lara Groves, Aidan Peppin, Andrew Strait, and Jenny Brennan. 2023 · 2023
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Evaluating large language models in generating synthetic HCI research data: a case study. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems . 1–19
Perttu Hämäläinen, Mikke Tavast, and Anton Kunnari. 2023 · 2023
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The impact of ChatGPT on human data collection: A case study involving typicality norming data
Tom Heyman and Geert Heyman. 2023 · 2023
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If you spend much time on AI twitter, you might have seen this tentacle monster hanging around. But what is it, and what does it have to do with ChatGPT? It’s kind of a long story. But it’s worth it! It even ends with cake. THREAD
@hlntnr. 2023 · 2023
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Large language models as simulated economic agents: What can we learn from homo silicus?
John J. Horton. 2023 · 2023
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A trend I see at #CHI23 is HCI researchers beginning to use LLMs to create synthetic user studies. E.g., https://dl.acm.org/doi/10.1145/3544548.3580688. While it is convenient, I do worry about the potential representational harms posed by such trend – experiences of minorities being erased
@infoxiao. 2023 · 2023
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Here’s the latest iteration of the “techniques like RLHF are just putting superficial smiley faces on an opaque, alien, Shoggoth intelligence that we have no real control over” AI safety meme. Artist: Anna Husfeldt, CC-BY SA 3.0
@jacyanthis. 2023 · 2023
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Reflexivity in quantitative research: A rationale and beginner’s guide
Michelle K. Jamieson, Gisela H. Govaart, and Madeleine Pownall. 2023 · 2023
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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Towards labor transparency in situated computational systems impact research. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency . 1026–1037
Felicia S. Jing, Sara E. Berger, and Juana Catalina Becerra Sandoval. 2023 · 2023
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Reinforcement learning from human feedback: A tutorial at ICML 2023
Nathan Lambert and Dmitry Ustalov. 2023 · 2023
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Human participants in AI research: Ethics and transparency in practice
Kevin R. McKee. 2023 · 2023
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Scaffolding cooperation in human groups with deep reinforcement learning
Kevin R. McKee, Andrea Tacchetti, Michiel A. Bakker, Jan Balaguer, Lucy Campbell-Gillingham, Richard Everett, and Matthew Botvinick. 2023 · 2023
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Having beer after prayer? Measuring cultural bias in large language models
Tarek Naous, Michael J. Ryan, and Wei Xu. 2023 · 2023
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Artificial Intelligence Risk Management Framework
National Institute of Standards and Technology. 2023 · 2023
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That viral image of Pope Francis wearing a white puffer coat is totally fake
Matt Novak. 2023 · 2023
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GPT-4 System Card
OpenAI. 2023 · 2023
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OpenAI. 2023 · 2023
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OpinioAI
OpinioAI. [n. d.] · 2023
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Generative agents: Interactive simulacra of human behavior
Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2023 · 2023
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Responsible sourcing of data enrichment services
Partnership on AI. [n. d.] · 2023
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A conversation with Bing’s chatbot left me deeply unsettled
Kevin Roose. 2023 · 2023
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Dario Amodei, C.E.O. of Anthropic, on the paradoxes of A.I. safety and Netflix’s “Deep Fake Love”
Kevin Roose and Casey Newton. 2023 · 2023
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Whose opinions do language models reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. 2023 · 2023
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Now class, can anyone tell me why this might be a bad idea?
@schock. 2023 · 2023
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ChatGPT: Optimizing Language Models for Dialogue
John Schulman. 2022 · 2023
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Artificial intelligence (AI) for user experience (UX) design: A systematic literature review and future research agenda
Åsne Stige, Efpraxia D. Zamani, Patrick Mikalef, and Yuzhen Zhu. 2023 · 2023
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What ChatGPT and generative AI mean for science
Chris Stokel-Walker and Richard Van Noorden. 2023 · 2023
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Synthetic Users
Synthetic Users. [n. d.] · 2023
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Code of Federal Regulations: Title 45, Part 46
U.S. Department of Health and Human Services. 2018 · 2023
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User Persona
User Persona. [n. d.] · 2023
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Here’s what happens when your lawyer uses ChatGPT
Benjamin Weiser. 2023 · 2023
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Democratic inputs to AI
Wojciech Zaremba, Arka Dhar, Lama Ahmad, Tyna Eloundou, Shibani Santurkar, Sandhini Agarwal, and Jade Leung. 2023 · 2023
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GPT and CLT: The impact of ChatGPT’s level of abstraction on consumer recommendations
Samuel N. Kirshner. 2024 · 2024
Closest in time.