In 1996, Accountability in a Computerized Society [95] issued a clarion call concerning the erosion of accountability in society due to the ubiquitous delegation of consequential functions to computerized systems. Nissenbaum [95] described four barriers to accountability that computerization presented, which we revisit in relation to the ascendance of data-driven algorithmic systems—i.e., machine learning or artificial intelligence—to uncover new challenges for accountability that these systems present. Nissenbaum’s original paper grounded discussion of the barriers in moral philosophy; we bring this analysis together with recent scholarship on relational accountability frameworks and discuss how the barriers present difficulties for instantiating a unified moral, relational framework in practice for data-driven algorithmic systems. We conclude by discussing ways of weakening the barriers in order to do so.

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Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning

The field of eXplainable Artificial Intelligence (XAI) focuses on providing explanations for AI systems’ decisions. XAI applications to AI-based Clinical Decision Support Systems (DSS) should increase trust in the DSS by allowing clinicians to investigate the reasons behind its suggestions. In this paper, we present the results of a user study on the impact of advice from a clinical DSS on healthcare providers’ judgment in two different cases: the case where the clinical DSS explains its suggestion and the case it does not. We examined the weight of advice, the behavioral intention to use the system, and the perceptions with quantitative and qualitative measures. Our results indicate a more significant impact of advice when an explanation for the DSS decision is provided. Additionally, through the open-ended questions, we provide some insights on how to improve the explanations in the diagnosis forecasts for healthcare assistants, nurses, and doctors.

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Understanding the impact of explanations on advice-taking: a user study for AI-based clinical Decision Support Systems

Limits and potential of the concept of indirect discrimination : (for the Commission)

Can satisfactory explanations for complex machine learning models be achieved in high-risk automated decision-making? How can such explanations be integrated into a data protection framework safeguarding a right to explanation? This article explores from an interdisciplinary point of view the connection between existing legal requirements for the explainability of AI systems set out in the General Data Protection Regulation (GDPR) and the current state of the art in the field of explainable AI. It studies the challenges of providing human legible explanations for current and future AI-based decision-making systems in practice, based on two scenarios of automated decision-making in credit scoring risks and medical diagnosis of COVID-19. These scenarios exemplify the trend towards increasingly complex machine learning algorithms in automated decision-making, both in terms of data and models. Current machine learning techniques, in particular those based on deep learning, are unable to make clear causal links between input data and final decisions. This represents a limitation for providing exact, human-legible reasons behind specific decisions, and presents a serious challenge to the provision of satisfactory, fair and transparent explanations. Therefore, the conclusion is that the quality of explanations might not be considered as an adequate safeguard for automated decision-making processes under the GDPR. Accordingly, additional tools should be considered to complement explanations. These could include algorithmic impact assessments, other forms of algorithmic justifications based on broader AI principles, and new technical developments in trustworthy AI. This suggests that eventually all of these approaches would need to be considered as a whole. 

Bridging the Gap Between AI and Explainability in the GDPR: Towards Trustworthiness-by-Design in Automated Decision-Making

In this paper, we present a fundamental framework for defining different types of explanations of AI systems and the criteria for evaluating their quality. Starting from a structural view of how explanations can be constructed, i.e., in terms of an explanandum (what needs to be explained), multiple explanantia (explanations, clues, or parts of information that explain), and a relationship linking explanandum and explanantia, we propose an explanandum-based typology and point to other possible typologies based on how explanantia are presented and how they relate to explanandia. We also highlight two broad and complementary perspectives for defining possible quality criteria for assessing explainability: epistemological and psychological (cognitive). These definition attempts aim to support the three main functions that we believe should attract the interest and further research of XAI scholars: clear inventories, clear verification criteria, and clear validation methods. 

Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI

Can we achieve an adequate level of explanation for complex machine learning models in high-risk AI applications when applying the EU data protection framework? In this article, we address this question, analysing from a multidisciplinary point of view the connection between existing legal requirements for the explainability of AI systems and the current state of the art in the field of explainable AI. We present a case study of a real-life scenario designed to illustrate the application of an AI-based automated decision making process for the medical diagnosis of COVID-19 patients. The scenario exemplifies the trend in the usage of increasingly complex machine-learning algorithms with growing dimensionality of data and model parameters. Based on this setting, we analyse the challenges of providing human legible explanations in practice and we discuss their legal implications following the General Data Protection Regulation (GDPR). Although it might appear that there is just one single form of explanation in the GDPR, we conclude that the context in which the decision-making system operates requires that several forms of explanation are considered. Thus, we propose to design explanations in multiple forms, depending on: the moment of the disclosure of the explanation (either ex ante or ex post); the audience of the explanation (explanation for an expert or a data controller and explanation for the final data subject); the layer of granularity (such as general, group-based or individual explanations); the level of the risks of the automated decision regarding fundamental rights and freedoms. Consequently, explanations should embrace this multifaceted environment. Furthermore, we highlight how the current inability of complex, deep learning based machine learning models to make clear causal links between input data and final decisions represents a limitation for providing exact, human-legible reasons behind specific decisions. This makes the provision of satisfactorily, fair and transparent explanations a serious challenge. Therefore, there are cases where the quality of possible explanations might not be assessed as an adequate safeguard for automated decision-making processes under Article 22(3) GDPR. Accordingly, we suggest that further research should focus on alternative tools in the GDPR (such as algorithmic impact assessments from Article 35 GDPR or algorithmic lawfulness justifications) that might be considered to complement the explanations of automated decision-making.

Ronan Hamon

Papers

Impossible Explanations?: Beyond explainable AI in the GDPR from a COVID-19 use case scenario