Fetching the paper…
Reading the bibliography…
As language models (LMs) advance, interest is growing in applying them to high-stakes societal decisions, such as determining financing or housing eligibility.
Racial discrimination and white-collar workers in britain
Jowell, R. and Prescott-Clarke, P · 1970
Earlier work this paper cites.
Clear and convincing evidence: Measurement of discrimination in america., 1996
Cain, G. G · 1996
Earlier work this paper cites.
Does automation bias decision-making?
Skitka, L. J., Mosier, K. L., and Burdick, M · 1999
Earlier work this paper cites.
Field experiments of discrimination in the market place
Riach, P. A. and Rich, J · 2002
Earlier work this paper cites.
Are emily and greg more employable than lakisha and jamal? a field experiment on labor market discrimination
Bertrand, M. and Mullainathan, S · 2004
Earlier work this paper cites.
The use of field experiments for studies of employment discrimination: Contributions, critiques, and directions for the future
Pager, D · 2007
Earlier work this paper cites.
Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
Earlier work this paper cites.
Automation bias: a systematic review of frequency, effect mediators, and mitigators
Goddard, K., Roudsari, A., and Wyatt, J. C · 2012
Earlier work this paper cites.
Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics
Crenshaw, K · 2013
Earlier work this paper cites.
Discrimination in online ad delivery
Sweeney, L · 2013
Earlier work this paper cites.
On causal interpretation of race in regressions adjusting for confounding and mediating variables
VanderWeele, T. J. and Robinson, W. R · 2014
Earlier work this paper cites.
Advancing a sociotechnical systems approach to workplace safety–developing the conceptual framework
Carayon, P., Hancock, P., Leveson, N., Noy, I., Sznelwar, L., and Van Hootegem, G · 2015
Earlier work this paper cites.
Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination, 2015
Datta, A., Tschantz, M. C., and Datta, A · 2015
Earlier work this paper cites.
Discrimination and disrespect
Eidelson, B · 2015
Earlier work this paper cites.
Risk, race, and recidivism: Predictive bias and disparate impact
Skeem, J. L. and Lowenkamp, C. T · 2016
Earlier work this paper cites.
Field experiments on discrimination
Bertrand, M. and Duflo, E · 2017
Earlier work this paper cites.
Algorithmic decision making and the cost of fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., and Huq, A · 2017
Earlier work this paper cites.
Proxy non-discrimination in data-driven systems, 2017
Datta, A., Fredrikson, M., Ko, G., Mardziel, P., and Sen, S · 2017
Earlier work this paper cites.
How black are lakisha and jamal? racial perceptions from names used in correspondence audit studies
Gaddis, S. M · 2017
Earlier work this paper cites.
Avoiding discrimination through causal reasoning
Kilbertus, N., Rojas Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., and Schölkopf, B · 2017
Earlier work this paper cites.
Counterfactual fairness
Kusner, M. J., Loftus, J., Russell, C., and Silva, R · 2017
Earlier work this paper cites.
Reshaping business with artificial intelligence: Closing the gap between ambition and action
Ransbotham, S., Kiron, D., Gerbert, P., and Reeves, M · 2017
Earlier work this paper cites.
Auditing black-box models for indirect influence
Adler, P., Falk, C., Friedler, S. A., Nix, T., Rybeck, G., Scheidegger, C., Smith, B., and Venkatasubramanian, S · 2018
Earlier work this paper cites.
Internal, external, and ecological validity in research design, conduct, and evaluation
Andrade, C · 2018
Earlier work this paper cites.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T · 2018
Cited alongside, same era.
Last name selection in audit studies
Crabtree, C. and Chykina, V · 2018
Cited alongside, same era.
Amazon scraps secret ai recruiting tool that showed bias against women
Dastin, J · 2018
Cited alongside, same era.
Affirmative Action
Fullinwider, R · 2018
Cited alongside, same era.
An introduction to audit studies in the social sciences
Gaddis, S. M · 2018
Cited alongside, same era.
Technical aspects of correspondence studies
Lahey, J. and Beasley, R · 2018
Cited alongside, same era.
Algorithmic auditing and social justice: Lessons from the history of audit studies. association for computing machinery, new york, ny, usa, 2021
Vecchione, B., Levy, K., and Barocas, S · 2021
Later among the works it cites.
Auditing ethics: A cost–benefit framework for audit studies
Crabtree, C. and Dhima, K · 2022
Later among the works it cites.
The algorithmic leviathan: Arbitrariness, fairness, and opportunity in algorithmic decision-making systems
Creel, K. and Hellman, D · 2022
Later among the works it cites.
Large language models are zero-shot reasoners
Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y · 2022
Later among the works it cites.
Why external validity matters for machine learning evaluation: Motivation and open problems
Liao, T. I., Taori, R., and Schmidt, L · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Albright, A · 2019
Cited alongside, same era.
Discrimination through optimization: How facebook’s ad delivery can lead to biased outcomes
Ali, M., Sapiezynski, P., Bogen, M., Korolova, A., Mislove, A., and Rieke, A · 2019
Cited alongside, same era.
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., and Mullainathan, S · 2019
Cited alongside, same era.
Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products
Raji, I. D. and Buolamwini, J · 2019
Cited alongside, same era.
Actionable recourse in linear classification
Ustun, B., Spangher, A., and Liu, Y · 2019
Cited alongside, same era.
Problematic machine behavior: A systematic literature review of algorithm audits, 2021
Bandy, J · 2021
Cited alongside, same era.
Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., and Irving, G · 2022
Later among the works it cites.
The fallacy of ai functionality
Raji, I. D., Kumar, I. E., Horowitz, A., and Selbst, A · 2022
Later among the works it cites.
Self-critiquing models for assisting human evaluators
Saunders, W., Yeh, C., Wu, J., Bills, S., Ouyang, L., Ward, J., and Leike, J · 2022
Later among the works it cites.
Prompting gpt-3 to be reliable
Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber, J., and Wang, L · 2022
Later among the works it cites.
Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature
Starke, C., Baleis, J., Keller, B., and Marcinkowski, F · 2022
Later among the works it cites.
Task ambiguity in humans and language models, 2022
Tamkin, A., Handa, K., Shrestha, A., and Goodman, N · 2022
Later among the works it cites.
Can a standardized test actually write itself?, 4 2022
Wodzak, S · 2022
Later among the works it cites.
Bias and fairness in large language models: A survey
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., and Ahmed, N. K · 2023
Closest in time.
Challenges in evaluating AI systems, 2023
Ganguli, D. and Favaro, M · 2023
Closest in time.
The capacity for moral self-correction in large language models
Ganguli, D., Askell, A., Schiefer, N., Liao, T., Lukošiūtė, K., Chen, A., Goldie, A., Mirhoseini, A., Olsson, C., Hernandez, D., et al · 2023
Closest in time.
Co-writing with opinionated language models affects users’ views
Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., and Naaman, M · 2023
Closest in time.
Discovering language model behaviors with model-written evaluations
Perez, E., Ringer, S., Lukosiute, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., Jones, A., Chen, A., Mann, B., Israel, B., Seethor, B., McKinnon, C., Olah, C., Yan, D., Amodei, D., Amodei, D., Drain, D., Li, D., Tran-Johnson, E., Khundadze, G., Kernion, J., Landis, J., Kerr, J., Mueller, J., Hyun, J., Landau, J., Ndousse, K., Goldberg, L., Lovitt, L., Lucas, M., Sellitto, M., Zhang, M., Kingsland, N., Elhage, N., Joseph, N., Mercado, N., DasSarma, N., Rausch, O., Larson, R., McCandlish, S., Johnston, S., Kravec, S., El Showk, S., Lanham, T., Telleen-Lawton, T., Brown, T., Henighan, T., Hume, T., Bai, Y., Hatfield-Dodds, Z., Clark, J., Bowman, S. R., Askell, A., Grosse, R., Hernandez, D., Ganguli, D., Hubinger, E., Schiefer, N., and Kaplan, J · 2023
Closest in time.
Ai-clinician collaboration via disagreement prediction: A decision pipeline and retrospective analysis of real-world radiologist-ai interactions
Sanchez, M., Alford, K., Krishna, V., Huynh, T. M., Nguyen, C. D., Lungren, M. P., Truong, S. Q., and Rajpurkar, P · 2023
Closest in time.
Centering health equity in large language model deployment
Singh, N., Lawrence, K., Richardson, S., and Mann, D. M · 2023
Closest in time.
Evaluating the social impact of generative ai systems in systems and society, 2023
Solaiman, I., Talat, Z., Agnew, W., Ahmad, L., Baker, D., Blodgett, S. L., au2, H. D. I., Dodge, J., Evans, E., Hooker, S., Jernite, Y., Luccioni, A. S., Lusoli, A., Mitchell, M., Newman, J., Png, M.-T., Strait, A., and Vassilev, A · 2023
Closest in time.
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., and Ting, D. S. W · 2023
Closest in time.
Veldanda, A. K., Grob, F., Thakur, S., Pearce, H., Tan, B., Karri, R., and Garg, S · 2023
Closest in time.
Bloomberggpt: A large language model for finance
Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., Kambadur, P., Rosenberg, D., and Mann, G · 2023
Closest in time.