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Machine learning (ML) is increasingly being used to support high-stakes decisions.
Clinical versus statistical prediction: A theoretical analysis and a review of the evidence
Meehl, P. E. (1954) · 1955
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An optimum character recognition system using decision functions
Chow, C.-K. (1957) · 1957
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A coefficient of agreement for nominal scales
Cohen, J. (1960) · 1960
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On optimum recognition error and reject tradeoff
Chow, C. (1970) · 1970
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Assessment of local influence
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Decision theory in expert systems and artificial intelligence
Horvitz, E. J., Breese, J. S., and Henrion, M. (1988) · 1988
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Decision support system effectiveness: a review and an empirical test
Sharda, R., Barr, S. H., and MCDonnell, J. C. (1988) · 1988
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Clinical versus actuarial judgment
Dawes, R. M., Faust, D., and Meehl, P. E. (1989) · 1989
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Interjudge agreement and the maximum value of kappa
Umesh, U. N., Peterson, R. A., and Sauber, M. H. (1989) · 1989
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Local justice: How institutions allocate scarce goods and necessary burdens
Elster, J. (1992) · 1992
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Competence in experts: The role of task characteristics
Shanteau, J. (1992) · 1992
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Efficient and adaptive estimation for semiparametric models
Bickel, P. J., Klaassen, C. A., Bickel, P. J., Ritov, Y., Klaassen, J., Wellner, J. A., and Ritov, Y. (1993) · 1993
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Beyond kappa: A review of interrater agreement measures
Banerjee, M., Capozzoli, M., McSweeney, L., and Sinha, D. (1999) · 1999
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
Platt, J. et al. (1999) · 1999
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Clinical versus mechanical prediction: a meta-analysis
Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., and Nelson, C. (2000) · 2000
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Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R. (2005) · 2005
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Procedural interference in perceptual classification: Implicit learning or cognitive complexity?
Nosofsky, R. M., Stanton, R. D., and Zaki, S. R. (2005) · 2005
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An empirical comparison of supervised learning algorithms
Caruana, R. and Niculescu-Mizil, A. (2006) · 2006
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Google news personalization: scalable online collaborative filtering
Das, A. S., Datar, M., Garg, A., and Rajaram, S. (2007) · 2007
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Cheap and fast—but is it good?: evaluating non-expert annotations for natural language tasks
Snow, R., O’Connor, B., Jurafsky, D., and Ng, A. Y. (2008) · 2008
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A survey of robot learning from demonstration
Argall, B. D., Chernova, S., Veloso, M., and Browning, B. (2009) · 2009
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Robot programming by demonstration
Calinon, S. (2009) · 2009
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Assessing screening and evaluation decision support systems: A resource-matching approach
Tan, C.-H., Teo, H.-H., and Benbasat, I. (2010) · 2010
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An automatic finite-sample robustness metric: When can dropping a little data make a big difference?
Broderick, T., Giordano, R., and Meager, R. (2020) · 2011
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Crowdsourcing systems on the world-wide web
Doan, A., Ramakrishnan, R., and Halevy, A. Y. (2011) · 2011
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Agreement/disagreement based crowd labeling
Amirkhani, H. and Rahmati, M. (2014) · 2014
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A machine learning approach to improving dynamic decision making
Meyer, G., Adomavicius, G., Johnson, P. E., Elidrisi, M., Rush, W. A., Sperl-Hillen, J. M., and O’Connor, P. J. (2014) · 2014
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., and Elhadad, N. (2015) · 2015
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Algorithm aversion: People erroneously avoid algorithms after seeing them err
Dietvorst, B. J., Simmons, J. P., and Massey, C. (2015) · 2015
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Why task domains (still) matter for understanding expertise
Shanteau, J. (2015) · 2015
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Big data’s disparate impact
Barocas, S. and Selbst, A. D. (2016) · 2016
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Productivity and selection of human capital with machine learning
Chalfin, A., Danieli, O., Hillis, A., Jelveh, Z., Luca, M., Ludwig, J., and Mullainathan, S. (2016) · 2016
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Learning with rejection
Cortes, C., DeSalvo, G., and Mohri, M. (2016) · 2016
Cited alongside, same era.
Doubting the diagnosis: How artificial intelligence increases ambiguity during professional decision making
Lebovitz, S., Levina, N., and Lifshitz-Assaf, H. (2019) · 2019
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Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., and Mullainathan, S. (2019) · 2019
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Problem formulation and fairness
Passi, S. and Barocas, S. (2019) · 2019
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Emergency department triage prediction of clinical outcomes using machine learning models
Raita, Y., Goto, T., Faridi, M. K., Brown, D. F., Camargo, C. A., and Hasegawa, K. (2019) · 2019
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Allegheny family screening tool
Allegheny County Department of Human Services (2020) · 2020
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“explaining” machine learning reveals policy challenges
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On the (im) possibility of fairness
Friedler, S. A., Scheidegger, C., and Venkatasubramanian, S. (2016) · 2016
Cited alongside, same era.
Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · 2016
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Semiparametric theory and empirical processes in causal inference
Kennedy, E. H. (2016) · 2016
Cited alongside, same era.
Algorithms need managers, too
Luca, M., Kleinberg, J., and Mullainathan, S. (2016) · 2016
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.
Human decisions and machine predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., and Mullainathan, S. (2017) · 2017
Cited alongside, same era.
Coyle, D. and Weller, A. (2020) · 2020
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Fairness is not static: deeper understanding of long term fairness via simulation studies
D’Amour, A., Srinivasan, H., Atwood, J., Baljekar, P., Sculley, D., and Halpern, Y. (2020) · 2020
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A case for humans-in-the-loop: Decisions in the presence of erroneous algorithmic scores
De-Arteaga, M., Fogliato, R., and Chouldechova, A. (2020) · 2020
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Who is a better decision maker? data-driven expert ranking under unobserved quality
Geva, T. and Saar-Tsechansky, M. (2020) · 2020
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Predictive multiplicity in classification
Marx, C., Calmon, F., and Ustun, B. (2020) · 2020
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International evaluation of an ai system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. C., Darzi, A., et al. (2020) · 2020
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Mitigating bias in algorithmic hiring: Evaluating claims and practices
Raghavan, M., Barocas, S., Kleinberg, J., and Levy, K. (2020) · 2020
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A human-centered review of algorithms used within the us child welfare system
Saxena, D., Badillo-Urquiola, K., Wisniewski, P. J., and Guha, S. (2020) · 2020
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Learning to complement humans
Wilder, B., Horvitz, E., and Kamar, E. (2020) · 2020
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Search personalization using machine learning
Yoganarasimhan, H. (2020) · 2020
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To trust or to think: cognitive forcing functions can reduce overreliance on ai in ai-assisted decision-making
Buçinca, Z., Malaya, M. B., and Gajos, K. Z. (2021) · 2021
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You’d better stop! understanding human reliance on machine learning models under covariate shift
Chiang, C.-W. and Yin, M. (2021) · 2021
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Fair machine learning under partial compliance
Dai, J., Fazelpour, S., and Lipton, Z. (2021) · 2021
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On the validity of arrest as a proxy for offense: Race and the likelihood of arrest for violent crimes
Fogliato, R., Xiang, A., Lipton, Z., Nagin, D., and Chouldechova, A. (2021) · 2021
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Algorithmic risk assessments can alter human decision-making processes in high-stakes government contexts
Green, B. and Chen, Y. (2021) · 2021
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Measurement and fairness
Jacobs, A. Z. and Wallach, H. (2021) · 2021
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Mimic-iv-ed (version 1.0)
Johnson, A., Bulgarelli, L., Pollard, T., Celi, L. A., Mark, R., and Horng, S. (2021) · 2021
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Algorithmic recommendations and human discretion
Angelova, V., Dobbie, W., and Yang, C. S. (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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Diversity in sociotechnical machine learning systems
Fazelpour, S. and De-Arteaga, M. (2022) · 2022
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Toward supporting perceptual complementarity in human-ai collaboration via reflection on unobservables
Holstein, K., De-Arteaga, M., Tumati, L., and Cheng, Y. (2023) · 2023
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