Algorithms, Big Data, Justice and Accountability - Articulating and Formalizing Fairness in Machine Learning

Transcription

1 Algorithms, Big Data, Justice and Accountability - Articulating and Formalizing Fairness in Machine Learning Laurens Naudts, Doctoral Researcher in Law, KU Leuven CiTiP Laurens.Naudts@kuleuven.be

2 Outline Fairness in Machine Learning Equality and Non-Discrimination in Machine Learning Luck Equality as a Use-Case Accountability and Machine Learning Conclusion

3 Fairness in Machine Learning Distributive Justice and Machine Learning

4 Machine Learning Clustering Classification Source: Fayyad et al. 1997

5 Almost all papers concerning algorithms and machine learning contain a sentence comparable to: Increasingly, automated processes are deployed to make decisions that have a significant impact on individuals lives

6 Theories of Distributive Justice and Machine Learning Substantive Justice Focus on the allocation of benefits and burdens to individuals within society (Fair Share) Strict Equality Equality of Resources (Dworkin) Luck Egalitarianism (Dworkin et al.) Welfare (Bentham, Mill) Libertarian (Nozick) Others Procedural Justice Focus on the procedures, e.g. processes, logic and deliberation of a decision, to determine the allocation of benefits and burdens to individuals within society (Fair Treatment) Formalizing Fair Machine Learning Fair Machine Learning Processes Fair Machine Learning Outcomes

7 National Security / Law Enforceme nt Fair Machine Learning Outcomes Formalizing Fair Machine Learning Fair Machine Learning Procedures Employmen t Depends on perspective one takes: Egalitarianism Strict Egalitarianism Equality of (Initial) Opportunity Equality of Welfare Sufficientarianism Prioritarianism (Parfit) Capability Approach (Sen, Nussbaum) Insurance System Functionality: Logic General Functionality Individual Decision-Making: Rationale Reasons Individual Circumstances In respect of Data Protection Laws? Social Credit Libertarian Utilitarian Banking Other Principles (Beauchamp, Childress): Personal Autonomy/Identity Beneficence Nonmaleficence Recommen der Systems

8 Distributive Justice in Machine Learning Equality as a Principle of Justice

9 MIT Technology Review De Standaard, 2017 The Guardian, 2016 The New York Times, 2015 Pro Publica, Knack, 2018.

10 Parity Preference Treatment Treatment Parity Preferred Treatment Impact (Results) Group Fairness Individual Fairness Equality of Opportunity Preferred Impact Source: Gajane (2017)

11 Parity in Machine Learning Treatment Parity: Avoid the use of sensitive attributes in machine learning processes process Impact Parity: Avoid disparity in the fraction of users belonging to different sensitive attribute groups that receive beneficial decision outcomes. Group Fairness (Statistical/Demographic Parity): The prediction of a particular outcome for individuals across groups should have an almost equal probability. The protected group is statistically treated similar to the general population. (// affirmative action) (Feldman et al., Dwork et al) Individual Fairness: Similar individuals (in relation to purpose of the task at hand) should be treated similarly (receive similar outputs) (See for instance: Dwork et al.) Equality of Opportunity/Equalized Odds/Disparate Mistreatment: Individuals who qualify for a desirable outcome should have an equal chance of being correctly classified for this outcome (See for instance: Hardt et al.; Zafar et al.)

12 Preference in Machine Learning Preference: Given the choice between various sets of decision outcomes, any group of users would collectively prefer the set that contains the largest fraction (or the greatest number) of beneficial decision outcomes for that group (Zafar et al.). Preferred Treatment: every sensitive attribute group (e.g., men and women) prefers the set of decisions they receive over the set of decisions they would have received had they collectively presented themselves to the system as members of a different sensitive group. Preferred Impact: every sensitive attribute group (e.g., men and women) prefers the set of decisions they receive over the set of decisions they would have received under the criterion of impact parity.

13 Source: Friedler et al. 2018

14 Historical Data of Corporation Bad Applicant Good Applicant Ethnicity (Sensitive Attribute) Geographic Location ( Driver s License Gender (Sensitive Attribute) Income previous profession

15 Ethnicity (Sensitive Attribute) Geographic Location Driver s License Ideal Employee Key Problem: The Formalization of Fair Machine Learning is unlikely to take into account future societal/individual changes as a result of machine learning itself! Gender (Sensitive Attribute) Income previous profession Correlation Location/License and Ethnicity Random-Group Differentiation Direct Discrimination on the basis of a sensitive attribute Indirect Discrimination on the basis of a sensitive attribute through proxy Fair or Unfair Differentiation?

16 Random-Group Differentiation Random Groups/Non-distributive outcomes (// over and under inclusion, faulty generalisation fallacy): Can generate and can make systemic new differentiation grounds Even if, at one point, they could be considered a proxy for traditional discrimination grounds (though the latter shouldn t necessarily be the case). // Stereotyping, Stigmatization Statements about individual as member of group versus Statements about individuals in their own right Both statements (algorithmic and reality/perception) are true (in some sense), but contradictory (Vedder & Naudts, 2017) // De-individualization

17 Fair Machine Learning and Luck Equality The Articulation of Fair Machine Learning through Option and Brute Luck

18 Just/Fair Inequalities Option Luck: Outcomes due to Choice (Volition) Brute Luck: Outcomes not foreseeable by choice (unavoidable). Option Luck: Events or Outcomes Choice or Volition Reasonably avoidable Reasonably foreseeable Influencable Choice versus Chance? Brute Luck: Events or Outcomes Unavoidable Not reasonably avoidable Not reasonably foreseeable Not influenceable Unjust/Unfair Inequalities

19 An Algorithmic Outcome is due to Brute Luck and thus Unfair When Based on: Unavoidable, including I. Not reasonably foreseeable II. Not influenceable A. (Supposed) information concerning the affected individual (and group); or B. (Supposed) actions/behavior of the affected individual (group); C. For the affected individual, no clear link exists between A, B and the Algorithmic Outcome/Categorization Interpretation might change over time, e.g. due to increasing awareness concerning algorithms

20 Machine Learning, Ethics and the Law Accountability Mechanisms in the GDPR: Towards Fair Machine Learning?

21 Accountability in the GDPR Self-Assessment Accountability Measures: Privacy and Data Protection By Design (Art. 25 GDPR) // Fair Machine Learning Record Keeping Obligations (Art. 30 GDPR) Data Protection Impact Assessment (Art. 35 GDPR) Codes of Conduct (Recital 99, Art. 45 GDPR) External Accountability Measures: Transparency Requirements (Recitals 39, 58 and78 GDPR; Art. 4 (1), 5 1 (a), 12, 13 2 (f), Art (g) and Art GDPR) Right to an Explanation? (Recital 71, Art. 22 GDPR) Informational Justice (See inter alia Colquitt, Binns et el.) Binns et al.: Receiving a thorough explanation (informational justice) is important in helping people to assess whether the decision-making procedure is just (procedural justice). In turn, decisions perceived to be procedurally just are more likely to be perceived as substantively just.

22 Data Protection Impact Assessment and Codes of Conduct Data Protection Impact Assessment (Micro-Level): Where a type of processing in particular using new technologies, and taking into account the nature, scope, context and purposes of the processing, is likely to result in a high risk to the rights and freedoms of natural persons (Art. 35 GDPR) Natural persons, rather than data subjects Rights and Freedoms, rather than data protection Equality and Non-Discrimination Codes of Conduct (Macro-Level): Specify amongst others fair and transparent processing, information to be provided to the public Stakeholder involvement through consultation (Recital 99 GDPR) E.g. citizen s interests bodies, ethics boards, data subjects, etc.

23 Conclusion Morality is complex Machine Learning is complex Articulating morality is complex Formalizing morality is complex Fair Machine learning is complex Interdisciplinary Research and Dialogue amongst communities remains necessary

24 Bibliography Binns, Reuben. Fairness in Machine Learning: Lessons from Political Philosophy. In Proceedings of Machine Learning Research. New York City, Binns, Reuben, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao, and Nigel Shadbolt. It s Reducing a Human Being to a Percentage ; Perceptions of Justice in Algorithmic Decisions. ArXiv: [Cs], 31 January Colquitt, Jason A., Donald E. Conlon, Michael J. Wesson, Christopher O. L. H. Porter, and K. Yee Ng. Justice at the Millennium: A Meta-Analytic Review of 25 Years of Organizational Justice Research. Journal of Applied Psychology 86, no. 3 (June 2001): Dwork, Cynthia, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Rich Zemel. Fairness Through Awareness. ArXiv: [Cs], 19 April Dwork, Cynthia, Nicole Immorlica, Adam Tauman Kalai, and Max Leiserson. Decoupled Classifiers for Fair and Efficient Machine Learning. ArXiv: [Cs], 20 July Dworkin, R. (1981). What is equality? Part 2: equality of resources. Philos Public Aff, 10, Fayyad, Usama, Gregory Piatetsky-Shapiro, and Padhraic Smyth. From Data Mining to Knowledge Discovery in Databases, n.d., 18. Feldman, Michael, Sorelle Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and Removing Disparate Impact. ArXiv: [Cs, Stat], 11 December Friedler, Sorelle A., Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P. Hamilton, and Derek Roth. A Comparative Study of Fairness-Enhancing Interventions in Machine Learning. ArXiv: [Cs, Stat], 12 February Gajane, Pratik. On Formalizing Fairness in Prediction with Machine Learning. ArXiv: [Cs, Stat], 9 October Hardt, Moritz, Eric Price, and Nathan Srebro. Equality of Opportunity in Supervised Learning. ArXiv: [Cs], 7 October Naudts, Laurens. Fair or Unfair Differentiation? Luck Egalitarianism as a Lens for Evaluating Algorithmic Decision-Making. London, Vallentyne, P. (2002). Brute luck, option luck and equality of initial opportunities. Ethics 112, Vedder, Anton, and Laurens Naudts. Accountability for the Use of Algorithms in a Big Data Environment. International Review of Law, Computers & Technology 31, no. 2 (4 May 2017): Zafar, Muhammad Bilal, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi, and Adrian Weller. From Parity to Preference-Based Notions of Fairness in Classification. ArXiv: [Cs, Stat], 30 June

25 Thank you for your attention!