Justice

In brief

Justice encompasses three different perspectives: (1) fairness understood as the fair treatment of people, (2) rightness as the quality of being fair or reasonable, and (3) a legal system, the scheme or system of law. Justice can be distinguished between substantive and procedural.

More in Detail

It is commonly accepted that, justice entails “the proper administration of the law; the fair and equitable treatment of all individuals under the law” [1]. Therefore, justice encompasses three different perspectives, (1) fairness understood as the fair treatment of people, (2) rightness as the quality of being fair or reasonable, and (3) a legal system, the scheme or system of law, in which every person receives his/her/its due from the system, including all rights, both natural and legal [5]. Artificial Intelligence (AI) can be a tool for administering justice in the legal system, or it can itself be subject to the requirements of fairness and rightness when used for automated decision making (ADM). In the former case, AI can be adopted at several levels of autonomy [6], e.g., from no automation to superhuman autonomous AI for legal reasoning. For a state of the art of the use of Machine Learning (ML) in the criminal justice system (mainly in the United States), see [7]. Several books and newspapers commentaries warn about the risks of using AI for justice administration [8]. In the latter case, the design of AI-based systems can benefit from discussion and theories of justice in the legal and ethical disciplines. However, the above conceptualization of justice has given rise to an endless and ongoing debate regarding whether justice is an inherent component of the law, not separate or distinct from it, or is simply a moral judgment about law [9]. In essence, the debate considers whether justice understood as fairness and rightness is independent from the law, or to what extent the law includes considerations of justice and the legal system simply applies justice to human conflicts. In conclusion, the concept of justice is as central to legal theory as it is difficult to define.

Nevertheless, a range of different components or categories of justice have been defined, both in the philosophical literature [10] and in the legal literature [11]. These can be understood along several distinctions, starting with one between substantive and procedural justice. This is the difference between considering justice in terms of the outcomes which have to meet certain standard in order to be just [12], versus considering justice in terms of a procedure which meets certain standards (and possibly considering the outcomes of any such procedure as being just regardless of the resulting distributions [13]). It is, however, common to consider procedural justice partly in terms of the results (e.g., a trial procedure is just if it – at least – mostly acquits the innocent and punishes the guilty). As such, substantive justice is the main notion to discuss here, although the use of AI in procedures also affects questions of procedural justice.

Substantive justice in turn can be viewed in different ways. First, there is a question of whether one focuses on distributive justice or on corrective justice. Distributive justice deals with the distribution of the benefits and burdens of social cooperation [12]. These can be comparative, such as the theory of (strict) egalitarianism which requires that resources are distributed to minimize overall inequality [14] and other versions of egalitarianism. For example, on Luck egalitarianism (and, closely related, Equality of Opportunity) inequalities in the final distribution may be allowed only in so far as they are not the result of luck or a difference of opportunity [15, 16]. By far the most influential, however, has been Rawls’ view of Justice as Fairness, which combines a requirement of equality of opportunity with the difference principle: unequal distributions have to satisfy a min-max condition where “they are to be to the greatest benefit of the least advantaged members of society.” [17] Alternatively, distributive notions of justice can be non-comparative. Sufficiency principles [18], requiring that everyone receives a minimally sufficient amount of resources are a clear example of a distributive justice notion that doesn’t involve comparisons between individuals. Desert-based principles [19], which hold that resources should be allocated based on what individuals deserve can also be non-comparative (if they specify absolute amounts based on what one deserves, as opposed to a share of the total). Views thus differ on what the right principles are for distributive justice. Furthermore, it is interesting that most of these principles have only a limited overlap to fairness in ADM [3] (cf. the entry on Fairness notions and metrics).

Where distributive justice focuses on the just distribution of goods, corrective justice concerns the rectification of wrongs or the undoing of transactions which can be either voluntary (contract) or involuntary (when defrauded or a victim of misrepresentation) [20, 21]. This differs from distributive justice, as corrective justice first requires a wrong that needs to be corrected, and the correction might violate the ideal distribution of goods according to distributive justice principles. As such, disagreements exist over the priority to be placed between these two principles: is corrective justice merely a way to achieve distributive justice or is corrective justice normatively prior? [22] However this issue is settled, it is a matter of fact that corrective justice is an important part of current legal systems. Similarly, retributive justice [23], which focuses on the compensation of the victim of criminal behaviour and the punishment of the lawbreaker, is crucial to our current systems.

In all these cases procedures are followed to make decisions on the distribution of resources, the appropriate corrections and potential punishments. Procedural justice relates to the normative conditions that these procedures have to meet. As such, it encompasses principles of legality, proportionality, effective remedy, fair trial, presumption of innocence and right of defence [4, 24, 25, 26]. Procedural justice is also affected by the use of AI, as this changes procedures and so new standards have to be found for when a procedure including AI is just. Such standards are also needed for transparency, and the notion of procedural justice has been used to propose such a standard by [27], who argue that what matters to determine the justness of an algorithm is the goals of the algorithm as well as how effectively they are met. That is intended to allow an evaluation of the justness of the procedure, and thus of the use of the algorithm. For instance, [28] conducted experiments involving laypeople, that showed a “fairness gap” between human judges and AI robot judges. Such a gap is reduced by enhancing the interpretability of AI decisions.

Part of the question of what procedures are just is that of which political procedures should be decided on. Political justice addresses the foundational issues of political rights and responsibilities embedded in constitutional theory and how individuals shall share the control over the shape of the constitution1 [29]. Social justice, on the other hand, addresses how members should compare under the basic structure of the society [29], and, its “primary task is not so much to save the computational infrastructure AI and ICTs rely on but rather to defend society” [30]. This concern decisions about the broad shape of society and thus cannot readily be solved with fair ML tools. Yet algorithms can help in the implementation of these decisions. As such, justice can be seen to differ from fairness: its scope is often broader, and it is not restricted to questions of equality between different groups to which an algorithm is applied. See e.g., [31] for a discussion of data justice, pertaining to “the way people are made visible, represented and treated as a result of their production of digital data”, and the literature on organizational justice theory [32, 33] for the notion of interactional justice pertaining to how workers are treated with respect and dignity (interpersonal justice) and how they are provided with explanations of business process and outcomes (informational justice). Still, the design of AI systems intersects with all of the notions of justice discussed here.

The increased use of AI and ADM reflects a tendency to solutionism [34], where technical solutions are offered to solve all social and economic problems [35]. However, unfair, discriminatory, and unjustified decisions affecting different aspects of individuals’ economy and private life has encouraged a critical reflection on questions regarding “what, then, do we talk about when we talk about governing algorithms?” [36]. Likewise, the proposed algorithmic justice [37] strives to address these unintentional effects provoked by the use of ADM for the allocation of welfare services. This novel conception of justice, founded on Nancy Fraser’s abnormal justice theory, defends “the need to expand our collective understanding of justice, beyond issues of equal access to, and equal distribution of justice” [38, 39] as referred in [37] in order to recognise, debate, diagnose and address harmful effects of ADM in the allocation of transformative services [37]. That being the case, (un)just ADM are more and more scrutinised under the lens of procedural justice as several studies [40, 41, 42] “suggest that people do not only care about whether the outcome of a decision benefits them, but also whether it meets standards of justice” [2]. To this regard, AI’s explainability and transparency would be crucial to justify and explain the algorithmic decisions and decision-making process and therefore ensure the accountability intelligibility of the decisions, and, by extension, of the process (a principle known as open justice when referring to the judicial system [43]).

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2

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3

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11

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12(1,2)

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13

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Steven L Blader and Tom R Tyler. A four-component model of procedural justice: defining the meaning of a “fair” process. Personality and social psychology bulletin, 29(6):747–758, 2003.

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Denise Meyerson and Catriona Mackenzie. Procedural justice and the law. Philosophy Compass, 13(12):e12548, 2018.

26

Min Kyung Lee, Anuraag Jain, Hea Jin Cha, Shashank Ojha, and Daniel Kusbit. Procedural justice in algorithmic fairness: leveraging transparency and outcome control for fair algorithmic mediation. Proc. ACM Hum. Comput. Interact., 3(CSCW):182:1–182:26, 2019.

27

Michele Loi, Andrea Ferrario, and Eleonora Viganò. Transparency as design publicity: explaining and justifying inscrutable algorithms. Ethics and Information Technology, 23(3):253–263, 2021.

28

Benjamin Minhao Chen, Alexander Stremitzer, and Kevin Tobia. Having your day in robot court. Harvard Journal of Law & Technology, 2022. Forthcoming. URL: https://ssrn.com/abstract=3841534.

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31

Linnet Taylor. What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 2017.

32

Sarah Bankins, Paul Formosa, Yannick Griep, and Deborah Richards. Decision making with dignity? Contrasting workers’ justice perceptions of human and AI decision making in a human resource management context. Information Systems Frontiers, 2022.

33

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36

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37(1,2,3)

Olivera Marjanovic, Dubravka Cecez-Kecmanovic, and Richard Vidgen. Theorising algorithmic justice. European Journal of Information Systems, pages 1–19, 2021.

38

Nancy Fraser. Abnormal justice. Critical inquiry, 34(3):393–422, 2008.

39

Nancy Fraser. Injustice at intersecting scales: on ‘social exclusion’and the ‘global poor’. European journal of social theory, 13(3):363–371, 2010.

40

E Allan Lind and Tom R Tyler. The social psychology of procedural justice. Springer Science & Business Media, 1988.

41

Jason A Colquitt and Jessica B Rodell. Measuring justice and fairness. In The Oxford handbook of justice in the workplace, pages 187–202. Oxford University Press, 2015.

42

Adrian Bussone, Simone Stumpf, and Dympna O'Sullivan. The role of explanations on trust and reliance in clinical decision support systems. In ICHI, 160–169. IEEE Computer Society, 2015.

43

Adam Pah, David Schwartz, Sarath Sanga, Charlotte Alexander, Kristian Hammond, Luis Amaral, and SCALES OKN Consortium. The promise of AI in an open justice system. AI Magazine, 43(1):69–74, 2022.

This entry was written by Alejandra Bringas Colmenarejo, Stefan Buijsman, and Salvatore Ruggieri.

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1

See the European Social Survey page for a survey on perceptions of political justice in Europe.