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Cited by 105

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Administration by Algorithm? Public Management Meets Public Sector Machine Learning

Veale, Michael, Brass, Irina·2019·Algorithmic Regulation
DOI 10.1093/oso/9780198838494.003.0006
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Abstract

This chapter first explains the types of machine learning systems used in the public sector, detailing the processes and tasks that they aim to support. It then looks at three levels of government—the macro, meso, and the street-level—to map out, analyse, and evaluate how machine learning in the public sector more broadly is framed and standardized across government. It concludes that, while the use of machine learning in the public sector is mostly discussed with regard to its ‘transformative effect’ versus ‘the dynamic conservatism’ characteristic of public bureaucracies that embrace new technological developments, it also raises several concerns about the skills, capacities, processes, and practices that governments currently employ, the forms of which can have value-laden, political consequences.

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Research works printed but not linked: 84

  1. 2018, Public Administration,
  2. 2006, Argumentation for Decision Support, Database and Expert Systems Applications Springer Berlin Heidelberg
  3. 2015, Privacy on the Ground: Driving Corporate Behavior in the United States and Europe
  4. 2005, E-Government and Administrative Power: The One-Stop-Shop Meets the Turf War, Electronic Government
  5. 2016, Big Data’s Disparate Impact, California Law Review
  6. 2003, Why Measure Performance? Different Purposes Require Different Measures, Public Administration Review
  7. Binns R, ‘Fairness in Machine Learning: Lessons from Political Philosophy’ Proceedings of the First Conference on Fairness, Accountability and Transparency (FAT*) (2018).
  8. Binns R, Veale M, Van Kleek M, et al, ‘Like Trainer Like Bot? Inheritance of Bias in Algorithmic Content Moderation’ in GL Ciampaglia, A Mashhadi, and T Yasseri (eds), Social Informatics: 9th International Conference, SocInfo 2017, Oxford, UK, September 13-15 2017 Proceedings Part II (Springer International Publishing 2017) doi: 10.1007/978-3-319-67256-4_32. · DOI
  9. Binns R, Van Kleek M, Veale M, et al, ‘ “It’s Reducing a Human Being to a Percentage”; Perceptions of Justice in Algorithmic Decisions’ Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI’18) (2018) doi: 10.1145/3173574.3173951. · doi:10.31235/osf.io/9wqxr
  10. 2005, The Emergence of Risk-Based Regulation and the New Public Risk Management in the United Kingdom, Public Law
  11. 1982, The Pressman-Wildavsky Paradox: Four Addenda or Why Models Based on Probability Theory Can Predict Implementation Success and Suggest Useful Tactical Advice for Implementers, Journal of Public Policy
  12. 2016, Undoing the Neutrality of Big Data, Florida Law Review Forum
  13. 2012, Critical Questions for Big Data: Provocations for a Cultural Technological and Scholarly Phenomenon, Information, Communication and Society
  14. 2009, Accountable and Countable: Information Management Systems and the Bureaucratization of Social Work, British Journal of Social Work
  15. 2001, Automated Profiling: Minding the Machine: Article 15 of the EC Data Protection Directive and Automated Profiling, Computer Law & Security Review
  16. 2017, Semantics Derived Automatically from Language Corpora Contain Human-Like Biases, Science
  17. 2018, The New Digital Future for Welfare: Debts Without Legal Proofs or Moral Authority?, UNSW Law Journal Forum Sydney Law School Research Paper No. 18/15
  18. Chouldechova A, Benavides-Prado D, Fialko O, et al, ‘A Case Study of Algorithm-Assisted Decision Making in Child Maltreatment Hotline Screening Decisions’ in SA Friedler and C Wilson (eds), Proceedings of the 1st Conference on Fairness, Accountability and Transparency, Proceedings of Machine Learning Research Vol 81 New York NY, USA (2018).
  19. 1986, An Essay on Discretion, Duke Law Journal
  20. 2012, Lessons from Cancelling the InterCity West Coast Franchise Competition
  21. 2018, Bias Detectives: The Researchers Striving to Make Algorithms Fair, Nature
  22. The Trouble with Bias, NIPS 2017 (Keynote
  23. 2014, Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms, Boston College Law Review
  24. 2011, The Merit of Meritocratization: Politics Bureaucracy and the Institutional Deterrents of Corruption, Political Research Quarterly
  25. 2015, The New Public Service Revisited, Public Administration Review
  26. 2007, The New Public Service: Serving Not Steering
  27. 2000, The New Public Service: Serving Rather than Steering, Public Administration Review
  28. 1967, A Realistic Look at the Final Payoffs from Urban Data Systems, Public Administration Review
  29. 1994, From Old Public Administration to New Public Management, Public Money & Management
  30. 2006, Digital Era Governance: IT Corporations, the State and e-Government
  31. 2013, How Reason Almost Lost Its Mind: The Strange Career of Cold War Rationality
  32. 1997, Taboo Trade-offs: Reactions to Transactions That Transgress the Spheres of Justice, Political Psychology
  33. 1993, Science for the Post-Normal Age, Futures
  34. 2017, On Fairness, Diversity and Randomness in Algorithmic Decision Making, arXiv [stat.ML]
  35. Heinrich CJ, ‘Measuring Public Sector Performance and Effectiveness’ in BG Peters and J Pierre (eds), The SAGE Handbook of Public Administration (SAGE 2003). · DOI
  36. Hood C, ‘The Tools of Government in the Information Age’ in RE Goodin, M Moran, and M Rein (eds), The Oxford Handbook of Public Policy (Oxford University Press 2008). · DOI
  37. 2000, The Art of the State: Culture Rhetoric and Public Management
  38. 2018, Algorithms in Decision-Making (HC 351)
  39. 2015, Smart Technologies and the End(s) of Law
  40. “Toxic” Whitehall Power Culture Fingered for GDS’s Fall from Grace, The Register
  41. 2013, Review of Quality Assurance of Government Models
  42. 2015, The Aqua Book: Guidance on Producing Quality Analysis for Government
  43. 2004, The Middle Aging of New Public Management: Into the Age of Paradox?, Journal of Public Administration Research and Theory
  44. 2011, The Thesis of Incongruent Implementation: Revisiting Pressman and Wildavsky, Public Policy and Administration
  45. 2014, Studying Implementation Beyond Deficit Analysis: The Top-Down View Reconsidered, Public Policy and Administration
  46. 2016, Government Policy (Section 35): Freedom of Information Act
  47. 2018, Technology Strategy 2018–2021
  48. Kamarinou D, Millard C, and Singh J, ‘Machine Learning with Personal Data’ (2016) Queen Mary School of Law Legal Studies Research Paper No 247/2016. <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2865811>.
  49. Kamiran F, Calders T, and Pechenizkiy M, ‘Techniques for Discrimination-Free Predictive Models’ in B Custers, T Calders, B Schermer, and T Zarsky (eds), Discrimination and Privacy in the Information Society (Springer 2012). · DOI
  50. 2018, Centrelink Robo-Debt Program Accused of Enforcing “Illegal” Debts, The Guardian
  51. 1988, Professionalism and Bureaucratic Responsiveness: Conflict or Compatibility?, Public Administration Review
  52. 2014, The Scored Society: Due Process for Automated Predictions, Washington Law Review
  53. Kilbertus N, Gascon A, Kusner M, et al, ‘Blind Justice: Fairness with Encrypted Sensitive Attributes’ Proceedings of the 35th International Conference on Machine Learning, ICML 2018, International Machine Learning Society (IMLS) (2018).
  54. 1982, Formulation and Implementation of Air Quality Control Programmes: Patterns of Interest Consideration, Policy & Politics
  55. 2013, Google: The Rise and Rise of Online Intermediaries in the Governance of the Internet and Beyond (Part 2), International Journal of Law and Information Technology
  56. 2016, Accountable Algorithms, University of Pennsylvania Law Review
  57. 2009, New Public Management: The Cruellest Invention of the Human Spirit? 1, Abacus
  58. 2014, The Problem-Solving Capacity of the Modern State: Governance Challenges and Administrative Capacities
  59. 1999, Information Technology in Government: Britain and America
  60. How AI Could Help the Public Sector, Harvard Business Review
  61. 1995, Synthesizing the Implementation Literature: The Ambiguity-Conflict Model of Policy Implementation, Journal of Public Administration Research and Theory
  62. 2017, Tax Analytics: Artificial Intelligence and Machine Learning
  63. 1997, Machine Learning
  64. 2014, Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images, arXiv:1412.1897 [cs]. · arXiv
  65. 1990, Payoffs from Computerization: Lessons over Time, Public Administration Review
  66. 2018, Algorithm-Assisted Decision-Making in the Public Sector: Framing the Issues Using Administrative Law Rules Governing Discretionary Power, Philosophical Transactions of the Royal Society of London. Series A: Mathematical and Physical Sciences
  67. 2012, Simulating Nature: A Philosophical Study of Computer-Simulation Uncertainties and their Role in Climate Science and Policy Advice
  68. 2013, Guidance for Uncertainty Assessment and Communication
  69. 2016, Standardisation—The End of Professional Discretion?, European Journal of Social Work
  70. 1997, Logical Tools for Modelling Legal Argument
  71. 2018, Algorithmic Impact Assessments: A Practical Framework for Public Agency Accountability
  72. 2000, Cross Pressures of Accountability: Initiative, Command, and Failure in the Ron Brown Plane Crash, Public Administration Review
  73. Seaver N, ‘Knowing Algorithms’ Paper presented at Media in Transition 8, Cambridge, MA (2013).
  74. 2017, Algorithms as Culture: Some Tactics for the Ethnography of Algorithmic Systems, Big Data & Society
  75. 2002, A Way Out of the Credibility Crisis of Models Used in Integrated Environmental Assessment, Futures
  76. 2010, Automating the Public Sector and Organizing Accountabilities, Communications of the Association for Information Systems
  77. 2002, Electronic Governance: Implications for Citizens, Politicians and Public Servants, International Review of Administrative Sciences
  78. 2002, Risk and Reason: Safety, Law, and the Environment
  79. 2017, Data Management and Use: Governance in the 21st Century
  80. 2018, Clarity, Surprises, and Further Questions in the Article 29 Working Party Draft Guidance on Automated Decision-Making and Profiling, Computer Law & Security Review
  81. Veale M, Van Kleek M, and Binns R, ‘Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making’ Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI’18) (2018) doi: 10.1145/3173574.3174014. · DOI · Paper page
  82. 2016, Service Automation Robots and the Future of Work
  83. 2004, The Split-Up Project: Induction, Context and Knowledge Discovery in Law, Law, Probability and Risk
  84. Žliobaitė I, Pechenizkiy M, and Gama J, ‘An Overview of Concept Drift Applications’ in N Japkowicz and J Stefanowski. (eds), Big Data Analysis: New Algorithms for a New Society Studies in Big Data (Springer International Publishing 2016) doi: 10.1007/978-3-319-26989-4_4. · DOI · Paper page

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Metadata

Authors
Veale, M., & Brass, I.
Venue
Algorithmic Regulation
Publisher
Oxford University PressOxford
Published
2019-09-05
Pages
121-149
DOI
10.1093/oso/9780198838494.003.0006
Cited by
105
References
30
Language
en

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