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Recent work has advocated for training AI models on ever-larger datasets, arguing that as the size of a dataset increases, the performance of a model trained on that dataset will correspondingly increase (referred to as "scaling laws").
Fairness Without Demographics in Repeated Loss Minimization
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The Policy Bases of the New Deal Realignment: Evidence from Public Opinion Polls, 1936–1952
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How Evaluation Guides AI Research
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Mapping the margins: Intersectionality, identity politics, and violence against women of color
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Empirical methods for artificial intelligence
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Payments and social ties
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World values surveys and European values surveys, 1981-1984, 1990-1993, and 1995-1997
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‘Improving ratings’: audit in the British University system
Strathern, M. 1997 · 1997
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A taxonomy of web search
Broder, A. 2002 · 2002
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Liberalism and value pluralism
Crowder, G. 2002 · 2002
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Optimizing search engines using clickthrough data
Joachims, T. 2002 · 2002
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Hofstede’s model of national cultural differences and their consequences: A triumph of faith-a failure of analysis
McSweeney, B. 2002 · 2002
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Task orientation in question answering
Murdock, V.; and Croft, W. B. 2002 · 2002
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Sampling and estimation in hidden populations using respondent-driven sampling
Salganik, M. J.; and Heckathorn, D. D. 2004 · 2004
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Experimenting with a democratic ideal: Deliberative polling and public opinion
Fishkin, J. S.; and Luskin, R. C. 2005 · 2005
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A Large Scale Study of Wireless Search Behavior: Google Mobile Search
Kamvar, M.; and Baluja, S. 2006 · 2006
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Low-resource languages: A review of past work and future challenges
Magueresse, A.; Carles, V.; and Heetderks, E. 2020 · 2006
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Population structure and eigenanalysis
Patterson, N.; Price, A. L.; and Reich, D. 2006 · 2006
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Deciphering Trends in Mobile Search
Kamvar, M.; and Baluja, S. 2007 · 2007
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Value tensions in design: the value sensitive design, development, and appropriation of a corporation’s groupware system
Miller, J. K.; Friedman, B.; Jancke, G.; and Gill, B. 2007 · 2007
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Participation is not a design fix for machine learning
Sloane, M.; Moss, E.; Awomolo, O.; and Forlano, L. 2020 · 2007
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The end of theory: The data deluge makes the scientific method obsolete
Anderson, C. 2008 · 2008
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Design and the Construction of Publics
DiSalvo, C. 2009 · 2009
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Is public value pluralism paramount? The intrinsic multiplicity and hybridity of public values
Van der Wal, Z.; and Van Hout, E. T. J. 2009 · 2009
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Mobile phones and economic development in Africa
Aker, J. C.; and Mbiti, I. M. 2010 · 2010
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Under pressure, teachers tamper with tests
Gabriel, T. 2010 · 2010
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Measurement Theory and Practice: The World Through Quantification
Hand, D. J. 2010 · 2010
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Social network site changes over time: The case of MySpace
Wilkinson, D.; and Thelwall, M. 2010 · 2010
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Dimensionalizing Cultures: T he Hofstede Model in Context
Hofstede, G. 2011 · 2011
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From preemption to circumvention: if technology regulates, why do we need regulation (and vice versa)
Nissenbaum, H. 2011 · 2011
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Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon
boyd, d.; and Crawford, K. 2012 · 2012
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Advances on the Development of Evaluation Measures
Carterette, B.; Kanoulas, E.; and Yilmaz, E. 2012 · 2012
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Participatory design: The third space in human–computer interaction
Muller, M. J.; and Druin, A. 2012 · 2012
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Freedom is an endless meeting
Polletta, F. 2012 · 2012
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On NonscalabilityThe Living World Is Not Amenable to Precision-Nested Scales
Tsing, A. L. 2012 · 2012
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Twitter reaction to events often at odds with overall public opinion
Mitchell, A.; and Hitlin, P. 2013 · 2013
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Big Data, new epistemologies and paradigm shifts
Kitchin, R. 2014 · 2014
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Where diversity comes from and why it matters?
Page, S. E. 2014 · 2014
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Machine learning for science and society
Rudin, C.; and Wagstaff, K. L. 2014 · 2014
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BEER: BEtter Evaluation as Ranking
Stanojević, M.; and Sima’an, K. 2014 · 2014
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Big questions for social media big data: Representativeness, validity and other methodological pitfalls
Tufekci, Z. 2014 · 2014
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Misleading Online Content: Recognizing Clickbait as ”False News”
Chen, Y.; Conroy, N. J.; and Rubin, V. L. 2015 · 2015
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Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement
Gray, D.; Micheli, P.; and Pavlov, A. 2015 · 2015
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Is bigger always better? Potential biases of big data derived from social network sites
Hargittai, E. 2015 · 2015
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Social Media Usage: 2005-2015
Perrin, A. 2015 · 2015
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Hierarchical models for estimating state and demographic trends in US death penalty public opinion
Shirley, K. E.; and Gelman, A. 2015 · 2015
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It is not about size: a further thought on big data
Yoo, Y. 2015 · 2015
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Evaluating Machine Learning Models
Zheng, A. 2015 · 2015
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Metric power
Beer, D. 2016 · 2016
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Computational Social Science: Discovery and Prediction. Edited by R. Michael Alvarez. New York: Cambridge University Press, 2016. 337p. 34.99 paper
de Marchi, S.; and Page, S. E. 2016 · 2016
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On the (im)possibility of fairness
Friedler, S. A.; Scheidegger, C.; and Venkatasubramanian, S. 2016 · 2016
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Online Learning to Rank for Information Retrieval: SIGIR 2016 Tutorial
Grotov, A.; and de Rijke, M. 2016 · 2016
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Equality of opportunity in supervised learning
Hardt, M.; Price, E.; and Srebro, N. 2016 · 2016
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Making transparency transparent: The evolution of observation in management theory
Bernstein, E. S. 2017 · 2017
Cited alongside, same era.
Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
Chouldechova, A. 2017 · 2017
Cited alongside, same era.
Automated hate speech detection and the problem of offensive language
Davidson, T.; Warmsley, D.; Macy, M.; and Weber, I. 2017 · 2017
Cited alongside, same era.
Role of community radio for community development in Bangladesh
Khan, M. A. A.; Khan, M. M. R.; Hassan, M.; Ahmed, F.; and Haque, S. M. R. 2017 · 2017
Cited alongside, same era.
The moral machine experiment
Awad, E.; Dsouza, S.; Kim, R.; Schulz, J.; Henrich, J.; Shariff, A.; Bonnefon, J.-F.; and Rahwan, I. 2018 · 2018
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Extending Multi-Document Summarization Evaluation to the Interactive Setting
Shapira, O.; Pasunuru, R.; Ronen, H.; Bansal, M.; Amsterdamer, Y.; and Dagan, I. 2021 · 2021
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Process for adapting language models to society (palms) with values-targeted datasets
Solaiman, I.; and Dennison, C. 2021 · 2021
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A framework for understanding sources of harm throughout the machine learning life cycle
Suresh, H.; and Guttag, J. 2021 · 2021
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Fighting hate speech, silencing drag queens? artificial intelligence in content moderation and risks to lgbtq voices online
Thiago, D. O.; Marcelo, A. D.; and Gomes, A. 2021 · 2021
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Measuring algorithmically infused societies
Wagner, C.; Strohmaier, M.; Olteanu, A.; Kıcıman, E.; Contractor, N.; and Eliassi-Rad, T. 2021 · 2021
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Buolamwini, J.; and Gebru, T. 2018 · 2018
Cited alongside, same era.
Rouge 2.0: Updated and improved measures for evaluation of summarization tasks
Ganesan, K. 2018 · 2018
Cited alongside, same era.
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Kearns, M.; Neel, S.; Roth, A.; and Wu, Z. S. 2018 · 2018
Cited alongside, same era.
A call for clarity in reporting BLEU scores
Post, M. 2018 · 2018
Cited alongside, same era.
Social Media Use Continues to Rise in Developing Countries but Plateaus Across Developed Ones
Poushter, J.; Bishop, C.; and Chwe, H. 2018 · 2018
Cited alongside, same era.
A structured review of the validity of BLEU
Reiter, E. 2018 · 2018
Cited alongside, same era.
Fairness and Machine Learning: Limitations and Opportunities
Barocas, S.; Hardt, M.; and Narayanan, A. 2019 · 2019
Cited alongside, same era.
Task similarity aware meta learning: theory-inspired improvement on MAML
Zhou, P.; Zou, Y.; Yuan, X.-T.; Feng, J.; Xiong, C.; and Hoi, S. 2021 · 2021
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ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
Aribandi, V.; Tay, Y.; Schuster, T.; Rao, J.; Zheng, H. S.; Mehta, S. V.; Zhuang, H.; Tran, V. Q.; Bahri, D.; Ni, J.; Gupta, J.; Hui, K.; Ruder, S.; and Metzler, D. 2022 · 2022
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Training a helpful and harmless assistant with reinforcement learning from human feedback
Bai, Y.; Jones, A.; Ndousse, K.; Askell, A.; Chen, A.; DasSarma, N.; Drain, D.; Fort, S.; Ganguli, D.; Henighan, T.; et al. 2022 · 2022
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Cultural Re-contextualization of Fairness Research in Language Technologies in India
Bhatt, S.; Dev, S.; Talukdar, P.; Dave, S.; and Prabhakaran, V. 2022 · 2022
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The Values Encoded in Machine Learning Research
Birhane, A.; Kalluri, P.; Card, D.; Agnew, W.; Dotan, R.; and Bao, M. 2022 · 2022
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Broken Neural Scaling Laws
Caballero, E.; Gupta, K.; Rish, I.; and Krueger, D. 2022 · 2022
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Reproducible scaling laws for contrastive language-image learning
Cherti, M.; Beaumont, R.; Wightman, R.; Wortsman, M.; Ilharco, G.; Gordon, C.; Schuhmann, C.; Schmidt, L.; and Jitsev, J. 2022 · 2022
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PaLM: Scaling Language Modeling with Pathways
Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H. W.; Sutton, C.; Gehrmann, S.; Schuh, P.; Shi, K.; Tsvyashchenko, S.; Maynez, J.; Rao, A.; Barnes, P.; Tay, Y.; Shazeer, N.; Prabhakaran, V.; Reif, E.; Du, N.; Hutchinson, B.; Pope, R.; Bradbury, J.; Austin, J.; Isard, M.; Gur-Ari, G.; Yin, P.; Duke, T.; Levskaya, A.; Ghemawat, S.; Dev, S.; Michalewski, H.; Garcia, X.; Misra, V.; Robinson, K.; Fedus, L.; Zhou, D.; Ippolito, D.; Luan, D.; Lim, H.; Zoph, B.; Spiridonov, A.; Sepassi, R.; Dohan, D.; Agrawal, S.; Omernick, M.; Dai, A. M.; Pillai, T. S.; Pellat, M.; Lewkowycz, A.; Moreira, E.; Child, R.; Polozov, O.; Lee, K.; Zhou, Z.; Wang, X.; Saeta, B.; Diaz, M.; Firat, O.; Catasta, M.; Wei, J.; Meier-Hellstern, K.; Eck, D.; Dean, J.; Petrov, S.; and Fiedel, N. 2022 · 2022
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Dealing with disagreements: Looking beyond the majority vote in subjective annotations
Davani, A. M.; Díaz, M.; and Prabhakaran, V. 2022 · 2022
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The case for 4-bit precision: k-bit Inference Scaling Laws
Dettmers, T.; and Zettlemoyer, L. 2022 · 2022
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Crowdworksheets: Accounting for individual and collective identities underlying crowdsourced dataset annotation
Díaz, M.; Kivlichan, I.; Rosen, R.; Baker, D.; Amironesei, R.; Prabhakaran, V.; and Denton, E. 2022 · 2022
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Towards WinoQueer: Developing a Benchmark for Anti-Queer Bias in Large Language Models
Felkner, V. K.; Chang, H.-C. H.; Jang, E.; and May, J. 2022 · 2022
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Predictability and Surprise in Large Generative Models
Ganguli, D.; Hernandez, D.; Lovitt, L.; Askell, A.; Bai, Y.; Chen, A.; Conerly, T.; Dassarma, N.; Drain, D.; Elhage, N.; El Showk, S.; Fort, S.; Hatfield-Dodds, Z.; Henighan, T.; Johnston, S.; Jones, A.; Joseph, N.; Kernian, J.; Kravec, S.; Mann, B.; Nanda, N.; Ndousse, K.; Olsson, C.; Amodei, D.; Brown, T.; Kaplan, J.; McCandlish, S.; Olah, C.; Amodei, D.; and Clark, J. 2022a · 2022
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Scaling Laws for Reward Model Overoptimization
Gao, L.; Schulman, J.; and Hilton, J. 2022 · 2022
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Machine learning and health need better values
Ghassemi, M.; and Mohamed, S. 2022 · 2022
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Jury Learning: Integrating Dissenting Voices into Machine Learning Models
Gordon, M. L.; Lam, M. S.; Park, J. S.; Patel, K.; Hancock, J.; Hashimoto, T.; and Bernstein, M. S. 2022 · 2022
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Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments
Ivgi, M.; Carmon, Y.; and Berant, J. 2022 · 2022
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How different groups prioritize ethical values for responsible AI
Jakesch, M.; Buçinca, Z.; Amershi, S.; and Olteanu, A. 2022 · 2022
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Assessing the Fairness of AI Systems: AI Practitioners’ Processes, Challenges, and Needs for Support
Madaio, M.; Egede, L.; Subramonyam, H.; Wortman Vaughan, J.; and Wallach, H. 2022 · 2022
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The Inverse Scaling Prize
McKenzie, I.; Lyzhov, A.; Parrish, A.; Prabhu, A.; Mueller, A.; Kim, N.; Bowman, S.; and Perez, E. 2022 · 2022
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Economic statistics as political artefacts
Mügge, D. 2022 · 2022
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Forgetting practices in the data sciences
Muller, M.; and Strohmayer, A. 2022 · 2022
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Lifting the Curse of Multilinguality by Pre-training Modular Transformers
Pfeiffer, J.; Goyal, N.; Lin, X.; Li, X.; Cross, J.; Riedel, S.; and Artetxe, M. 2022 · 2022
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Striving for data-model efficiency: Identifying data externalities on group performance
Rolf, E.; Packer, B.; Beutel, A.; and Diaz, F. 2022 · 2022
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Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization
Shanmugam, D.; Diaz, F.; Shabanian, S.; Finck, M.; and Biega, A. 2022 · 2022
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The Reward Hypothesis is False
Skalse, J. M. V.; and Abate, A. 2022 · 2022
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Defining and Characterizing Reward Gaming
Skalse, J. M. V.; Howe, N. H. R.; Krasheninnikov, D.; and Krueger, D. 2022 · 2022
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Towards Intersectional Feminist and Participatory ML: A Case Study in Supporting Feminicide Counterdata Collection
Suresh, H.; Movva, R.; Dogan, A. L.; Bhargava, R.; Cruxen, I.; Cuba, Á. M.; Taurino, G.; So, W.; and D’Ignazio, C. 2022 · 2022
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Reliance on metrics is a fundamental challenge for AI
Thomas, R. L.; and Uminsky, D. 2022 · 2022
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Too Many Relevants: Whither Cranfield Test Collections?
Voorhees, E. M.; Craswell, N.; and Lin, J. 2022 · 2022
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Watkins, E. A.; McKenna, M.; and Chen, J. 2022 · 2022
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Inverse scaling can become U-shaped
Wei, J.; Tay, Y.; and Le, Q. V. 2022 · 2022
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Identifying subgroups of adult high-cost health care users: a retrospective analysis
Wick, J.; Campbell, D. J.; McAlister, F. A.; Manns, B. J.; Tonelli, M.; Beall, R. F.; Hemmelgarn, B. R.; Stewart, A.; and Ronksley, P. E. 2022 · 2022
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Scaling laws for generative mixed-modal language models
Aghajanyan, A.; Yu, L.; Conneau, A.; Hsu, W.-N.; Hambardzumyan, K.; Zhang, S.; Roller, S.; Goyal, N.; Levy, O.; and Zettlemoyer, L. 2023 · 2023
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Planning for AGI and beyond
Altman, S. 2023 · 2023
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Representation in AI Evaluations
Bergman, A. S.; Hendricks, L. A.; Rauh, M.; Wu, B.; Agnew, W.; Kunesch, M.; Duan, I.; Gabriel, I.; and Isaac, W. 2023 · 2023
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Rethink reporting of evaluation results in AI
Burnell, R.; Schellaert, W.; Burden, J.; Ullman, T. D.; Martinez-Plumed, F.; Tenenbaum, J. B.; Rutar, D.; Cheke, L. G.; Sohl-Dickstein, J.; Mitchell, M.; Kiela, D.; Shanahan, M.; Voorhees, E. M.; Cohn, A. G.; Leibo, J. Z.; and Hernandez-Orallo, J. 2023 · 2023
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When is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages
Chang, T. A.; Arnett, C.; Tu, Z.; and Bergen, B. K. 2023 · 2023
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The participatory turn in ai design: Theoretical foundations and the current state of practice
Delgado, F.; Yang, S.; Madaio, M.; and Yang, Q. 2023 · 2023
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Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms
Dennler, N.; Ovalle, A.; Singh, A.; Soldaini, L.; Subramonian, A.; Tu, H.; Agnew, W.; Ghosh, A.; Yee, K.; Peradejordi, I. F.; et al. 2023 · 2023
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Towards Measuring the Representation of Subjective Global Opinions in Language Models
Durmus, E.; Nyugen, K.; Liao, T. I.; Schiefer, N.; Askell, A.; Bakhtin, A.; Chen, C.; Hatfield-Dodds, Z.; Hernandez, D.; Joseph, N.; et al. 2023 · 2023
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Scaling laws for single-agent reinforcement learning
Hilton, J.; Tang, J.; and Schulman, J. 2023 · 2023
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“I’m fully who I am”: Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation
Ovalle, A.; Goyal, P.; Dhamala, J.; Jaggers, Z.; Chang, K.-W.; Galstyan, A.; Zemel, R.; and Gupta, R. 2023a · 2023
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Queer In AI: A Case Study in Community-Led Participatory AI
Queerinai, O. O.; Ovalle, A.; Subramonian, A.; Singh, A.; Voelcker, C.; Sutherland, D. J.; Locatelli, D.; Breznik, E.; Klubicka, F.; Yuan, H.; et al. 2023 · 2023
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On Scaling Laws, Emergent Behaviors, and AI Democratization Efforts
Rish, I. 2023 · 2023
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Whose opinions do language models reflect?
Santurkar, S.; Durmus, E.; Ladhak, F.; Lee, C.; Liang, P.; and Hashimoto, T. 2023 · 2023
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Post-growth Human–Computer Interaction
Sharma, V.; Kumar, N.; and Nardi, B. 2023 · 2023
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Value Kaleidoscope: Engaging AI with pluralistic human values, rights, and duties
Sorensen, T.; Jiang, L.; Hwang, J.; Levine, S.; Pyatkin, V.; West, P.; Dziri, N.; Lu, X.; Rao, K.; Bhagavatula, C.; et al. 2023 · 2023
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It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance
Subramonian, A.; Yuan, X.; Daumé III, H.; and Blodgett, S. L. 2023 · 2023
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Decolonial AI Alignment: Vi \ \backslash ’ { \{ s } \} esadharma, Argument, and Artistic Expression
Varshney, K. R. 2023 · 2023
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Designing responsible AI: Adaptations of UX practice to meet responsible AI challenges
Wang, Q.; Madaio, M.; Kane, S.; Kapania, S.; Terry, M.; and Wilcox, L. 2023 · 2023
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The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models
Birhane, A.; Dehdashtian, S.; Prabhu, V. U.; and Boddeti, V. 2024 · 2024
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Breaking the Curse of Multilinguality with Cross-lingual Expert Language Models
Blevins, T.; Limisiewicz, T.; Gururangan, S.; Li, M.; Gonen, H.; Smith, N. A.; and Zettlemoyer, L. 2024 · 2024
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Participation versus scale: Tensions in the practical demands on participatory AI
Young, M.; Ehsan, U.; Singh, R.; Tafesse, E.; Gilman, M.; Harrington, C.; and Metcalf, J. 2024 · 2024
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