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    • Article
    Sélectionner

    Does Machine Learning Automate Moral Hazard and Error?

    Mullainathan, Sendhil, Obermeyer, Ziad
    The American economic review, May 2017, Vol.107(5), pp.476-480 [Revue évaluée par les pairs]
    MEDLINE/PubMed (U.S. National Library of Medicine)
    Disponible
    Plus…
    Titre: Does Machine Learning Automate Moral Hazard and Error?
    Auteur: Mullainathan, Sendhil; Obermeyer, Ziad
    Sujet: Law ; Economics;
    Description: Machine learning tools are beginning to be deployed en masse in health care. While the statistical underpinnings of these techniques have been questioned with regard to causality and stability, we highlight a different concern here, relating to measurement issues. A characteristic feature of health data, unlike other applications of machine learning, is that neither y nor x is measured perfectly. Far from a minor nuance, this can undermine the power of machine learning algorithms to drive change in the health care system--and indeed, can cause them to reproduce and even magnify existing errors in human judgment.
    Fait partie de: The American economic review, May 2017, Vol.107(5), pp.476-480
    Identifiant: 0002-8282 (ISSN); 28781376 Version (PMID); 10.1257/aer.p20171084 (DOI)

    • Livre
    Sélectionner

    Knappheit : was es mit uns macht, wenn wir zu wenig haben

    Mullainathan, Sendhil
    Freytag, Carl, Shafir, Eldar, 1959-
    [Lieu de publication non identifié] : Campus
    2010
    Disponible
    Plus…
    Chargement
    Erreur de chargement
    Titre: Knappheit : was es mit uns macht, wenn wir zu wenig haben / Sendhil Mullainathan ; Carl Freytag ; Eldar Shafir
    Auteur: Mullainathan, Sendhil
    Contributeur: Freytag, Carl; Shafir, Eldar, 1959-
    Editeur: [Lieu de publication non identifié] : Campus
    Date: 2010
    Collation: 1 e-Book
    Description: Warum bleibt die Armut weltweit bestehen? Warum grassiert die Übergewichtigkeit? Warum haben es einsame Menschen schwerer, Freunde zu finden? All diese scheinbar unverbundenen Fragen beruhen auf dem Phänomen der Knappheit - ob an Zeit, Ressourcen oder sozialen Kontakten. Sendhil Mullainathan und Eldar Shafir begründen darauf eine neue Disziplin an der Schnittstelle von Ökonomie und Psychologie: die Wissenschaft von der Knappheit. Denn alle Formen der Knappheit erzeugen dieselben psychologischen Prozesse, dieselben Herausforderungen und Spannungen, dieselben Anstrengungen und gelegentlichen Fehler. Mit diesem Buch können wir unser Handeln besser begreifen und sogar modifizieren, damit wir künftig unsere selbst gesteckten Ziele leichter erreichen.
    Identifiant: 9783593420486 (ISBN)
    No RERO: R005336179
    Permalien:
    http://data.rero.ch/01-R005336179/html?view=VS_V1

    • Livre
    Sélectionner

    Labor Market Discrimination in Delhi : Evidence from a Field Experiment

    Abhijit Bertrand, Marianne Datta, Saugato Mullainathan, Sendhil Banerjee
    2009
    Disponible
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    Titre: Labor Market Discrimination in Delhi : Evidence from a Field Experiment
    Auteur: Abhijit Bertrand, Marianne Datta, Saugato Mullainathan, Sendhil Banerjee
    Date: 2009

    • Livre
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    Public Policy and Extended Families : Evidence from Pensions in South Africa

    Marianne Mullainathan, Sendhil Miller, Douglas Bertrand
    2003
    Disponible
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    Titre: Public Policy and Extended Families : Evidence from Pensions in South Africa
    Auteur: Marianne Mullainathan, Sendhil Miller, Douglas Bertrand
    Date: 2003

    • Article
    Sélectionner

    Prediction Policy Problems

    Kleinberg, Jon, Ludwig, Jens, Mullainathan, Sendhil, Obermeyer, Ziad
    The American economic review, May 2015, Vol.105(5), pp.491-495 [Revue évaluée par les pairs]
    MEDLINE/PubMed (U.S. National Library of Medicine)
    Disponible
    Plus…
    Titre: Prediction Policy Problems
    Auteur: Kleinberg, Jon; Ludwig, Jens; Mullainathan, Sendhil; Obermeyer, Ziad
    Sujet: Coefficients – Analysis ; Machine Learning – Analysis ; Machine Learning – Usage ; Causal Inference – Analysis ; Prediction Theory – Models;
    Description: Most empirical policy work focuses on causal inference. We argue an important class of policy problems does not require causal inference but instead requires predictive inference. Solving these "prediction policy problems" requires more than simple regression techniques, since these are tuned to generating unbiased estimates of coefficients rather than minimizing prediction error. We argue that new developments in the field of "machine learning" are particularly useful for addressing these prediction problems. We use an example from health policy to illustrate the large potential social welfare gains from improved prediction.(JEL C50, C53, D83)
    Fait partie de: The American economic review, May 2015, Vol.105(5), pp.491-495
    Identifiant: 0002-8282 (ISSN); 27199498 Version (PMID)

    • Plusieurs versions

    Human Decisions and Machine Predictions *

    Kleinberg, Jon, Lakkaraju, Himabindu, Leskovec, Jure, Ludwig, Jens, Mullainathan, Sendhil
    The Quarterly Journal of Economics, 2017, Vol. 133(1), pp.237-293 [Revue évaluée par les pairs]

    • Article
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    Comparison friction: experimental evidence from medicare drug plans

    Kling, Jeffrey R, Mullainathan, Sendhil, Shafir, Eldar, Vermeulen, Lee C, Wrobel, Marian V
    The quarterly journal of economics, 2012, Vol.127(1), pp.199-235 [Revue évaluée par les pairs]
    MEDLINE/PubMed (U.S. National Library of Medicine)
    Disponible
    Plus…
    Titre: Comparison friction: experimental evidence from medicare drug plans
    Auteur: Kling, Jeffrey R; Mullainathan, Sendhil; Shafir, Eldar; Vermeulen, Lee C; Wrobel, Marian V
    Sujet: Community Participation ; Cost Savings ; Insurance, Pharmaceutical Services ; Medicare Part D ; Prescriptions ; Public Policy
    Description: Consumers need information to compare alternatives for markets to function efficiently. Recognizing this, public policies often pair competition with easy access to comparative information. The implicit assumption is that comparison friction—the wedge between the availability of comparative information and consumers' use of it—is inconsequential because when information is readily available, consumers will access this information and make effective choices. We examine the extent of comparison friction in the market for Medicare Part D prescription drug plans in the United States. In a randomized field experiment, an intervention group received a letter with personalized cost information. That information was readily available for free and widely advertised. However, this additional step—providing the information rather than having consumers actively access it—had an impact. Plan switching was 28% in the intervention group, versus 17% in the comparison group, and the intervention caused...
    Fait partie de: The quarterly journal of economics, 2012, Vol.127(1), pp.199-235
    Identifiant: 0033-5533 (ISSN); 22454838 Version (PMID)

    • Plusieurs versions

    Thinking, Fast and Slow? Some Field Experiments to Reduce Crime and Dropout in Chicago *

    Heller, Sara B, Shah, Anuj K, Guryan, Jonathan, Ludwig, Jens, Mullainathan, Sendhil, Pollack, Harold A
    The Quarterly Journal of Economics, 2016, Vol. 132(1), pp.1-54 [Revue évaluée par les pairs]

    • Article
    Sélectionner

    Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability

    Kleinberg, Jon, Mullainathan, Sendhil
    Cornell University
    Disponible
    Plus…
    Titre: Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability
    Auteur: Kleinberg, Jon; Mullainathan, Sendhil
    Sujet: Computer Science - Machine Learning ; Computer Science - Computers And Society ; Computer Science - Data Structures And Algorithms ; Computer Science - Social And Information Networks ; Statistics - Machine Learning
    Description: Algorithms are increasingly used to aid, or in some cases supplant, human decision-making, particularly for decisions that hinge on predictions. As a result, two additional features in addition to prediction quality have generated interest: (i) to facilitate human interaction and understanding with these algorithms, we desire prediction functions that are in some fashion simple or interpretable; and (ii) because they influence consequential decisions, we also want them to produce equitable allocations. We develop a formal model to explore the relationship between the demands of simplicity and equity. Although the two concepts appear to be motivated by qualitatively distinct goals, we show a fundamental inconsistency between them. Specifically, we formalize a general framework for producing simple prediction functions, and in this framework we establish two basic results. First, every simple prediction function is strictly improvable: there exists a more complex prediction function that is both strictly more efficient and also strictly more equitable. Put another way, using a simple prediction function both reduces utility for disadvantaged groups and reduces overall welfare relative to other options. Second, we show that simple prediction functions necessarily create incentives to use information about individuals' membership in a disadvantaged group --- incentives that weren't present before simplification, and that work against these individuals. Thus, simplicity transforms disadvantage into bias against the disadvantaged group. Our results are not only about algorithms but about any process that produces simple models, and as such they connect to the psychology of stereotypes and to an earlier economics literature on statistical discrimination. Comment: Updated version incorporating additional results
    Identifiant: 1809.04578 (ARXIV ID)

    • Article
    Sélectionner

    Enjoying the Quiet Life? Corporate Governance and Managerial Preferences

    Bertrand, Marianne, Mullainathan, Sendhil
    Bertrand, Marianne, and Sendhil Mullainathan. 2003. Enjoying the quiet life? Corporate governance and managerial preferences. Journal of Political Economy 111(5): 1043-1075. [Revue évaluée par les pairs]
    Harvard University Library
    Disponible
    Plus…
    Titre: Enjoying the Quiet Life? Corporate Governance and Managerial Preferences
    Auteur: Bertrand, Marianne; Mullainathan, Sendhil
    Description: Much of our understanding of corporations builds on the idea that managers, when they are not closely monitored, will pursue goals that are not in shareholders’ interests. But what goals would managers pursue? This paper uses variation in corporate governance generated by state adoption of antitakeover laws to empirically map out managerial preferences. We use plant‐level data and exploit a unique feature of corporate law that allows us to deal with possible biases associated with the timing of the laws. We find that when managers are insulated from takeovers, worker wages (especially those of white‐collar workers) rise. The destruction of old plants falls, but the creation of new plants also falls. Finally, overall productivity and profitability decline in response to these laws. Our results suggest that active empire building may not be the norm and that managers may instead prefer to enjoy the quiet life.
    Fait partie de: Bertrand, Marianne, and Sendhil Mullainathan. 2003. Enjoying the quiet life? Corporate governance and managerial preferences. Journal of Political Economy 111(5): 1043-1075.
    Identifiant: 0022-3808 (ISSN); 10.1086/376950 (DOI)