2019 ◽  
Author(s):  
Reuben Binns

There are various arguments in favour of tempering algorithmic decision-making with human judgement. One common family of arguments appeal to concepts and criteria derived from legal philosophy about the nature of law and legal reasoning, and argue that algorithmic systems cannot satisfy them (but humans can). This paper argues that among the latter family of arguments, there is often an implicit appeal to the notion that each case needs to be assessed on its own merits, without comparison to or generalisation from previous cases. This notion of ‘individual justice’ has featured in jurisprudential debates about the granularity of rules and tests, and the (in)justice of discrimination, but has not yet been explicitly imported into debates about justice and algorithmic decision-making. This paper has several aims. The first is to provide an overview of the concept of individual justice and distinguish it from related but distinct arguments about the value of human discretion. Equipped with this account of human judgement as a guarantor of individual justice, the second aim is to consider its place within and beside algorithmic decision-making. It argues that in so far as individual justice is valuable, it can only be meaningfully served through human judgement, because it antithetical to the kind of pre-determined reasoning that characterises algorithmic systems. This suggests that – to the extent that individual justice is deemed important – a requirement for human intervention and oversight over algorithmic decisions is necessary. The third aim is to consider how individual justice relates to other dimensions of justice, namely consistency and fairness or non-discrimination. Finally, the article discusses two challenges that are raised by this account. The first challenge concerns how individual justice can be accommodated alongside other dimensions of justice in the socio-technical contexts in which humans-in-the-loop are situated. The second concerns the potential inequities in individual justice that might result from an uneven application of human judgement in algorithmic settings.


2020 ◽  
Vol 11 (1) ◽  
pp. 18-50 ◽  
Author(s):  
Maja BRKAN ◽  
Grégory BONNET

Understanding of the causes and correlations for algorithmic decisions is currently one of the major challenges of computer science, addressed under an umbrella term “explainable AI (XAI)”. Being able to explain an AI-based system may help to make algorithmic decisions more satisfying and acceptable, to better control and update AI-based systems in case of failure, to build more accurate models, and to discover new knowledge directly or indirectly. On the legal side, the question whether the General Data Protection Regulation (GDPR) provides data subjects with the right to explanation in case of automated decision-making has equally been the subject of a heated doctrinal debate. While arguing that the right to explanation in the GDPR should be a result of interpretative analysis of several GDPR provisions jointly, the authors move this debate forward by discussing the technical and legal feasibility of the explanation of algorithmic decisions. Legal limits, in particular the secrecy of algorithms, as well as technical obstacles could potentially obstruct the practical implementation of this right. By adopting an interdisciplinary approach, the authors explore not only whether it is possible to translate the EU legal requirements for an explanation into the actual machine learning decision-making, but also whether those limitations can shape the way the legal right is used in practice.


2021 ◽  
Vol 13 (4) ◽  
pp. 1948
Author(s):  
Qiaoning Zhang ◽  
Xi Jessie Yang ◽  
Lionel P. Robert

Automated vehicles (AV) have the potential to benefit our society. Providing explanations is one approach to facilitating AV trust by decreasing uncertainty about automated decision-making. However, it is not clear whether explanations are equally beneficial for drivers across age groups in terms of trust and anxiety. To examine this, we conducted a mixed-design experiment with 40 participants divided into three age groups (i.e., younger, middle-age, and older). Participants were presented with: (1) no explanation, or (2) explanation given before or (3) after the AV took action, or (4) explanation along with a request for permission to take action. Results highlight both commonalities and differences between age groups. These results have important implications in designing AV explanations and promoting trust.


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