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"All models are wrong, but some are useful", wrote George E.
Cross-validation, risk estimation, and model selection
Wager, Stefan · 1909
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Value-laden disciplinary shifts in machine learning
Dotan, Ravit and Smitha Milli · 1912
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Jacobs, Abigail Z. and Hanna Wallach · 1912
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On the mathematical foundations of theoretical statistics
Fisher, Ronald A · 1922
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Why do we sometimes get nonsense-correlations between time-series?—A study in sampling and the nature of time-series
Yule, G. Udny · 1926
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The Lanarkshire Milk Experiment
“Student” (William Sealy Gosset) · 1931
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Science and sanity: An introduction to non-Aristotelian systems and general semantics
Korzybski, Alfred · 1933
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Professor Tinbergen’s method
Keynes, John Maynard · 1939
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Note on the sampling error of the difference between correlated proportions or percentages
McNemar, Quinn · 1947
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Essays in positive economics
Friedman, Milton · 1953
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Historians’ fallacies: Toward a logic of historical thought
Fischer, David Hackett · 1970
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Pedagogy of the oppressed
Freire, Paulo · 1970
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Scientific knowledge and its social problems
Ravetz, Jerome R · 1971
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Sex bias in graduate admissions: Data from Berkeley
Bickel, Peter J., Eugene A. Hammel, and J. W. O’Connell · 1975
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Assessing the impact of planned social change
Campbell, Donald T · 1975
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Problems of monetary management: The UK experience , chapter 4, pages 1–20
Goodhart, Charles A. E · 1975
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A humanist view, 30 May 1975
Morrison, Toni · 1975
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Artificial intelligence meets natural stupidity
McDermott, Drew · 1976
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Robustness in the strategy of scientific model building
Box, George E. P · 1979
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Statistical computing
Kennedy, William J., Jr. and James E. Gentle · 1980
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Son of seven sexes: The social destruction of a physical phenomenon
Collins, Henry M · 1981
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Rational prediction
Salmon, Wesley C · 1981
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Network autocorrelation: A simulation study of a foundational problem in regression and survey research
Dow, Malcolm M., Michael L. Burton, and Douglas R. White · 1982
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Subaltern studies: Deconstructing historiography
Spivak, Gayatri Chakravorty · 1985
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Statistics and causal inference
Holland, Paul W · 1986
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The campus climate revisited: Chilly for women faculty, administrators, and graduate students
Sandler, Bernice R. and Roberta M. Hall · 1986
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Beyond incremental processing: Tracking concept drift
Schlimmer, Jeffrey C. and Richard H. Granger, Jr · 1986
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Transcending general linear reality
Abbott, Andrew · 1988
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Prediction and theory evaluation: The case of light bending
Brush, Stephen G · 1989
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Classical probability in the Enlightenment
Daston, Lorraine · 1989
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From Kwajalein to Armageddon? Testing and the social construction of missile accuracy
MacKenzie, Donald A · 1989
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Role of models in statistical analysis
Cox, David R · 1990
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The taming of chance
Hacking, Ian · 1990
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Comparison of two bandwidth selectors with dependent errors
Chu, Chih-Kang and James Stephen Marron · 1991
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When genius errs: R. A. Fisher and the lung cancer controversy
Stolley, Paul D · 1991
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Quadratic forms in random variables: Theory and applications
Mathai, A. M. and Serge B. Provost · 1992
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The struggle for the soul of health insurance
Stone, Deborah A · 1993
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Seductions of sim: Policy as a simulation game
Starr, Paul · 1994
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Lies, damn lies and arrest statistics
Elliott, Delbert S · 1995
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Suffering and its professional transformation: Toward an ethnography of interpersonal experience
Kleinman, Arthur and Joan Kleinman · 1995
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Bilinear Forms and Zonal Polynomials
Mathai, A. M., Serge B. Provost, and Takesi Hayakawa · 1995
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Databases as discourse, or electronic interpellations
Poster, Mark · 1995
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From association to causation via regression
Freedman, David A · 1996
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The grand leap
Humphreys, Paul and David Freedman · 1996
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Learning in the presence of concept drift and hidden contexts
Widmer, Gerhard and Miroslav Kubat · 1996
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Towards a critical technical practice: Lessons learned from trying to reform AI
Agre, Philip E · 1997
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On bias, variance, 0/1-loss, and the curse-of-dimensionality
Friedman, Jerome H · 1997
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Machine learning
Mitchell, Tom M · 1997
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‘Improving ratings’: Audit in the British university system
Strathern, Marilyn · 1997
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The embeddedness of economic markets in economics
Callon, Michel · 1998
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Approximate statistical tests for comparing supervised classification learning algorithms
Dietterich, Thomas G · 1998
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The zeroth problem
Mallows, Colin · 1998
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What we know about spreadsheet errors
Panko, Raymond R · 1998
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Predicting the future: An introduction to the theory of forecasting
Rescher, Nicholas · 1998
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Measurement in psychology: Critical history of a methodological concept
Michell, Joel · 1999
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A unified bias-variance decomposition and its applications
Domingos, Pedro · 2000
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Star crushing: Theoretical practice and the theoreticians’ regress
Kennefick, Daniel · 2000
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Consistent cross-validatory model-selection for dependent data: h v hv -block cross-validation
Racine, Jeff · 2000
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Statistical modeling: The two cultures (with comments and a rejoinder by the author)
Breiman, Leo · 2001
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Nonparametric regression with correlated errors
Opsomer, Jean, Yuedong Wang, and Yuhong Yang · 2001
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When formality works: Authority and abstraction in law and organizations
Stinchcombe, Arthur L · 2001
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From factors to actors: Computational sociology and agent-based modeling
Macy, Michael W. and Robert Willer · 2002
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Gene expression profiling predicts clinical outcome of breast cancer
van’t Veer, Laura J., Hongyue Dai, Marc J. van de Vijver, Yudong D. He, Augustinus A. M. Hart, Mao Mao, Hans L. Peterse, Karin van der Kooy, Matthew J. Marton, Anke T. Witteveen, George J. Schreiber, Ron M. Kerkhoven, Chris Roberts, Peter S. Linsley, René Bernards, and Stephen H. Friend · 2002
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Constructing a market, performing theory: The historical sociology of a financial derivatives exchange
MacKenzie, Donald A. and Yuval Millo · 2003
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Overfitting in making comparisons between variable selection methods
Reunanen, Juha · 2003
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Making things happen: A theory of causal explanation
Woodward, James · 2003
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The estimation of prediction error: Covariance penalties and cross-validation
Efron, Bradley · 2004
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Graphical models for causation, and the identification problem
Freedman, David A · 2004
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Positivism and realism
Payne, Geoff and Judy Payne · 2004
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In the public interest: The case–and the research–that forever connected psychology and policy
Tomes, Henry · 2004
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Subjective well-being and Kahneman’s ‘objective happiness’
Alexandrova, Anna · 2005
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Linear statistical models for causation: A critical review
Freedman, David A · 2005
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Simulation for the social scientist
Gilbert, Nigel and Klaus G. Troitzsch · 2005
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Research paradigms and meaning making: A primer
Krauss, Steven Eric · 2005
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Mind as machine: A history of cognitive science
Boden, Margaret A · 2006
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An engine, not a camera: How financial models shape markets
MacKenzie, Donald A · 2006
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A scientist’s nightmare: Software problem leads to five retractions
Miller, Greg · 2006
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On model selection consistency of lasso
Zhao, Peng and Bin Yu · 2006
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An analysis of the New York City Police Department’s “stop-and-frisk” policy in the context of claims of racial bias
Gelman, Andrew, Jeffrey Fagan, and Alex Kiss · 2007
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Scorecards as devices for consumer credit: The case of Fair, Isaac & Company Incorporated
Poon, Martha · 2007
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Interference between units in randomized experiments
Rosenbaum, Paul R · 2007
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An empirical evaluation of supervised learning in high dimensions
Caruana, Rich, Nikos Karampatziakis, and Ainur Yessenalina · 2008
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Actors’ and analysts’ categories in the social analysis of science
Collins, Harry · 2008
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Natural and field experiments: The role of qualitative methods
Dunning, Thad · 2008
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When is discrimination wrong?
Hellman, Deborah · 2008
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Cross validation of prediction models for seasonal time series by parametric bootstrapping
Kunst, Robert M · 2008
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Qualitative versus quantitative methods: A relevant argument?
Pierce, Roger · 2008
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On the dangers of cross-validation: An experimental evaluation
Rao, R. Bharat, Glenn Fung, and Romer Rosales · 2008
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When analytic narratives explain
Alexandrova, Anna · 2009
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Network analysis in the social sciences
Borgatti, Stephen P., Ajay Mehra, Daniel J. Brass, and Giuseppe Labianca · 2009
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Power-law distributions in empirical data
Clauset, Aaron, Cosma Rohilla Shalizi, and Mark E. J. Newman · 2009
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Statistical models and causal inference: A dialogue with the social sciences
Freedman, David A · 2009
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A statistician’s perspective on Mostly harmless econometrics: An empiricist’s companion
Gelman, Andrew · 2009
Cited alongside, same era.
Detecting influenza epidemics using search engine query data
Ginsberg, Jeremy, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, and Larry Brilliant · 2009
Cited alongside, same era.
The elements of statistical learning: Data mining, inference and prediction
Hastie, Trevor, Robert Tibshirani, and Jerome Friedman · 2009
Cited alongside, same era.
Probabilistic graphical models: Principles and techniques
Koller, Daphne and Nir Friedman · 2009
Cited alongside, same era.
Causality: Models, reasoning, and inference
Pearl, Judea · 2009
Cited alongside, same era.
A cautionary note on the use of matching to estimate causal effects: An empirical example comparing matching estimates to an experimental benchmark
Causal discovery and inference: Concepts and recent methodological advances
Spirtes, Peter and Kun Zhang · 2016
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Be(com)ing a reflexive researcher: A developmental approach to research methodology
Attia, Mariam and Julian Edge · 2017
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A note on the validity of cross-validation for evaluating autoregressive time series prediction
Bergmeir, Christoph, Rob J. Hyndman, and Bonsoo Koo · 2017
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On individual risk
Dawid, Philip · 2017
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Scale development: Theory and applications
DeVellis, Robert F · 2017
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Lensing machines – Representing perspective in machine learning
Dinakar, Karthik · 2017
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Arceneaux, Kevin, Alan S. Gerber, and Donald P. Green · 2010
Cited alongside, same era.
A survey of cross-validation procedures for model selection
Arlot, Sylvain and Alain Celisse · 2010
Cited alongside, same era.
On over-fitting in model selection and subsequent selection bias in performance evaluation
Cawley, Gavin C. and Nicola L. C. Talbot · 2010
Cited alongside, same era.
Data handling errors spur debate over clinical trial
Hutson, Stu · 2010
Cited alongside, same era.
Stability selection
Meinshausen, Nicolai and Peter Bühlmann · 2010
Cited alongside, same era.
Computational science: …error
Merali, Zeeya · 2010
Cited alongside, same era.
On Chomsky and the two cultures of statistical learning, 2010
Norvig, Peter · 2010
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
Doshi-Velez, Finale and Been Kim · 2017
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Manufacturing an Artificial Intelligence revolution, November 2017
Katz, Yarden · 2017
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Creditworthy: A history of consumer surveillance and financial identity in America
Lauer, Josh · 2017
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Machine learners: Archaeology of a data practice
Mackenzie, Adrian · 2017
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Machine learning: An applied econometric approach
Mullainathan, Sendhil and Jann Spiess · 2017
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Medicine and the McNamara fallacy
O’Mahony, Seamus · 2017
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Simulating the dynamics of socio-economic systems
Pfeffer, Jürgen and Momin M. Malik · 2017
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Estimation and inference of heterogeneous treatment effects using random forests
Wager, Stefan and Susan Athey · 2017
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Choosing prediction over explanation in psychology: Lessons from machine learning
Yarkoni, Tal and Jacob Westfall · 2017
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Interventions over predictions: Reframing the ethical debate for actuarial risk assessment
Barabas, Chelsea, Madars Virza, Karthik Dinakar, Joichi Ito, and Jonathan Zittrain · 2018
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W. E. B. Du Bois’s data portraits: Visualizing Black America
Battle-Baptiste, Whitney and Britt Rusert, editors · 2018
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Statistics versus machine learning
Bzdok, Danilo, Naomi Altman, and Martin Krzywinski · 2018
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Design justice, A.I., and escape from the matrix of domination
Costanza-Chock, Sasha · 2018
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#MoreThanCode: Practitioners reimagine the landscape of technology for justice and equity
Costanza-Chock, Sasha, Maya Wagoner, Berhan Taye, Caroline Rivas, Chris Schweidler, Georgia Bullen, and the Tech for Social Justice Project · 2018
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The impact of algorithms on judicial discretion: Evidence from regression discontinuities, 2018
Cowgill, Bo · 2018
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Automating inequality: How high-tech tools profile, police, and punish the poor
Eubanks, Virginia · 2018
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Robust physical-world attacks on deep learning visual classification
Eykholt, Kevin, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song · 2018
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Artificial intelligence and inclusion: Formerly gang-involved youth as domain experts for analyzing unstructured Twitter data
Frey, William R., Desmond U. Patton, Michael B. Gaskell, and Kyle A. McGregor · 2018
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Breaking NLI systems with sentences that require simple lexical inferences
Glockner, Max, Vered Shwartz, and Yoav Goldberg · 2018
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What is discrimination, when is it wrong and why?
Hellman, Deborah · 2018
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The numbers don’t speak for themselves: Racial disparities and the persistence of inequality in the criminal justice system
Hetey, Rebecca C. and Jennifer L. Eberhardt · 2018
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Artificial intelligence faces reproducibility crisis
Hutson, Matthew · 2018
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Some papers don’t reproduce. Should we care?
Irpan, Alex · 2018
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How we became instrumentalists (again): Data positivism since World War II
Jones, Matthew L · 2018
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The misgendering machines: Trans/HCI implications of automatic gender recognition
Keyes, Os · 2018
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Frequentist, Bayes, or other?
Lavine, Michael · 2018
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The mythos of model interpretability
Lipton, Zachary C · 2018
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Troubling trends in machine learning scholarship
Lipton, Zachary C. and Jacob Steinhardt · 2018
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Bias and beyond in digital trace data
Malik, Momin M · 2018
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Deep learning: A critical appraisal
Marcus, Gary · 2018
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People’s councils for ethical machine learning
McQuillan, Dan · 2018
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Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election
Meng, Xiao-Li · 2018
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Explanation in artificial intelligence: Insights from the social sciences
Miller, Tim · 2018
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Algorithms of oppression: How search engines reinforce racism
Noble, Safiya Umoja · 2018
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Beyond legitimation: Rethinking fairness, interpretability, and accuracy in machine learning
Ochigame, Rodrigo, Chelsea Barabas, Karthik Dinakar, Madars Virza, and Joichi Ito · 2018
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The book of why: The new science of cause and effect
Pearl, Judea and Dana Mackenzie · 2018
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Model evaluation, model selection, and algorithm selection in machine learning
Raschka, Sebastian · 2018
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Priming neural networks
Rosenfeld, Amir, Mahdi Biparva, and John K. Tsotsos · 2018
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From fixed-X to random-X regression: Bias-variance decompositions, covariance penalties, and prediction error estimation
Rosset, Saharon and Ryan J. Tibshirani · 2018
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Many analysts, one data set: Making transparent how variations in analytic choices affect results
Silberzahn, Raphael, Eric L. Uhlmann, Dan P. Martin, Pasquale Anselmi, Frederik Aust, Eli Awtrey, Štěpán Bahník, Feng Bai, Colin Bannard, Evelina Bonnier, Rickard Carlsson, Felix Cheung, Garret Christensen, Russ Clay, Maureen A. Craig, Anna Dalla Rosa, Lammertjan Dam, Mathew H. Evans, Ismael Flores Cervantes, Nathan Fong, Monica Gamez-Djokic, Andreas Glenz, Shauna Gordon-McKeon, Tim J. Heaton, Karin Hederos, Moritz Heene, Alicia J. Mohr, Fabia Högden, Kent Hui, Magnus Johannesson, Jonathan Kalodimos, Erikson Kaszubowski, Deanna M. Kennedy, Ryan Lei, Thomas A. Lindsay, Silvia Liverani, Christopher R. Madan, Daniel Molden, Eric Molleman, Richard D. Morey, Laetitia B. Mulder, Bernard R. Nijstad, Nolan G. Pope, Bryson Pope, Jason M. Prenoveau, Floor Rink, Egidio Robusto, Hadiya Roderique, Anna Sandberg, Elmar Schlüter, Felix D. Schönbrodt, Martin F. Sherman, S. Amy Sommer, Kristin Sotak, Seth Spain, Christoph Spörlein, Tom Stafford, Luca Stefanutti, Susanne Tauber, Johannes Ullrich, Michelangelo Vianello, Eric-Jan Wagenmakers, Maciej Witkowiak, Sangsuk Yoon, and Brian A. Nosek · 2018
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The main reason why almost all econometric models are wrong
Syll, Lars · 2018
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Excess optimism: How biased is the apparent error of an estimator tuned by SURE?
Tibshirani, Ryan J. and Saharon Rosset · 2018
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Computational social science ≠ \neq computer science + social data
Wallach, Hanna · 2018
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Is Yelp actually cleaning up the restaurant industry? A re-analysis on the relative usefulness of consumer reviews
Altenburger, Kristen M. and Daniel E. Ho · 2019
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Race after technology: Abolitionist tools for the New Jim Code
Benjamin, Ruha · 2019
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Characterization of leptazolines A-D, polar oxazolines from the cyanobacterium Leptolyngbya
Bhandari Neupane, Jayanti, Ram P. Neupane, Yuheng Luo, Wesley Y. Yoshida, Rui Sun, and Philip G. Williams · 2019
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Statistical thinking, machine learning
Bian, Jiang, Iain Buchan, Yi Guo, and Mattia Prosperi · 2019
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A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
Christodoulou, Evangelia, Jie Ma, Gary S. Collins, Ewout W. Steyerberg, Jan Y. Verbakel, and Ben Van Calster · 2019
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Show your work: Improved reporting of experimental results
Dodge, Jesse, Suchin Gururangan, Dallas Card, Roy Schwartz, and Noah A. Smith · 2019
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An open source AutoML benchmark
Gijsbers, Pieter, Erin LeDell, Janek Thomas, Sébastien Poirier, Bernd Bischl, and Joaquin Vanschoren · 2019
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Ghost work: How to stop Silicon Valley from building a new global underclass
Gray, Mary L. and Siddharth Suri · 2019
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The smart enough city: Putting technology in its place to reclaim our urban future
Green, Ben · 2019
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Disparate interactions: An algorithm-in-the-loop analysis of fairness in risk assessments
Green, Ben and Yiling Chen · 2019
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Prediction and the moral order: Contesting fairness in consumer data capitalism
Kiviat, Barbara · 2019
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Eddie Murphy and the dangers of counterfactual causal thinking about detecting racial discrimination
Kohler-Hausmann, Issa · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
McCoy, Tom, Ellie Pavlick, and Tal Linzen · 2019
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Digital character in “The Scored Society”: FICO, social networks, and competing measurements of creditworthiness
Nopper, Tamara K · 2019
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Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil Mullainathan · 2019
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Social data: Biases, methodological pitfalls, and ethical boundaries
Olteanu, Alexandra, Carlos Castillo, Fernando Diaz, and Emre Kıcıman · 2019
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How a machine learns and fails — a grammar of error for Artificial Intelligence
Pasquinelli, Matteo · 2019
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Decolonising AI: A transfeminist approach to data and social justice
Peña, Paz and Joana Varon · 2019
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Retraction note: Limited individual attention and online virality of low-quality information
Qiu, Xiaoyan, Diego F. M. Oliveira, Alireza Sahami Shirazi, Alessandro Flammini, and Filippo Menczer · 2019
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An agenda for decolonizing data science
Raval, Noopur · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Rudin, Cynthia · 2019
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Fairness and abstraction in sociotechnical systems
Selbst, Andrew D., danah m. boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi · 2019
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Algorithmic risk assessment in the hands of humans
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Energy and policy considerations for deep learning in NLP
Strubell, Emma, Ananya Ganesh, and Andrew McCallum · 2019
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What do you learn from context? probing for sentence structure in contextualized word representations
Tenney, Ian, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R. Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, and Ellie Pavlick · 2019
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ELF OpenGo: An analysis and open reimplementation of AlphaZero
Tian, Yuandong, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, and Larry Zitnick · 2019
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Statistics versus machine learning: definitions are interesting (but understanding, methodology, and reporting are more important)
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Algorithms were supposed to make Virginia judges fairer. What happened was far more complicated
Van Dam, Andrew · 2019
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The rhetoric and reality of anthropomorphism in Artificial Intelligence
Watson, David · 2019
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‘Forget Facts, I’ve Had Experiences!’ Racism and the problem with anecdotes, bad data, and dog breed analogies
Wise, Tim · 2019
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Humans can decipher adversarial images
Zhou, Zhenglong and Chaz Firestone · 2019
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The hidden costs of automated thinking
Zittrain, Jonathan · 2019
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Roles for computing in social change
Abebe, Rediet, Solon Barocas, Jon Kleinberg, Karen Levy, Manish Raghavan, and David G. Robinson · 2020
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The hidden assumptions behind counterfactual explanations and principal reasons
Barocas, Solon, Andrew D. Selbst, and Manish Raghavan · 2020
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Garbage in, garbage out? Do machine learning application papers in social computing report where human-labeled training data comes from?
Geiger, R. Stuart, Kevin Yu, Yanlai Yang, Mindy Dai, Jie Qiu, Rebekah Tang, and Jenny Huang · 2020
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Robustness in machine learning explanations: Does it matter?
Hancox-Li, Leif · 2020
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What’s sex got to do with machine learning?
Hu, Lily and Issa Kohler-Hausmann · 2020
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Toward situated interventions for algorithmic equity: Lessons from the field
Katell, Michael, Meg Young, Dharma Dailey, Bernease Herman, Vivian Guetler, Aaron Tam, Corinne Binz, Daniella Raz, and P. M. Krafft · 2020
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The long history of algorithmic fairness
Ochigame, Rodrigo · 2020
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Epidemic illusions: On the coloniality of global public health
Richardson, Eugene T · 2020
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Case study: Predictive fairness to reduce misdemeanor recidivism through social service interventions
Rodolfa, Kit T., Erika Salomon, Lauren Haynes, Iván Higuera Mendieta, Jamie Larson, and Rayid Ghani · 2020
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‘the human body is a black box’: Supporting clinical decision-making with deep learning
Sendak, Mark, Madeleine Clare Elish, Michael Gao, Joseph Futoma, William Ratliff, Marshall Nichols, Armando Bedoya, Suresh Balu, and Cara O’Brien · 2020
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