Brent Mittelstadt

Bio: Brent Mittelstadt is an academic researcher from University of Oxford. The author has contributed to research in topics: Data Protection Act 1998 & Big data. The author has an hindex of 21, co-authored 53 publications receiving 4362 citations. Previous affiliations of Brent Mittelstadt include De Montfort University & The Turing Institute.

Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR

Abstract: There has been much discussion of the “right to explanation” in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to explanation that opens the ‘black box’ of algorithmic decision-making faces major legal and technical barriers. Explaining the functionality of complex algorithmic decision-making systems and their rationale in specific cases is a technically challenging problem. Some explanations may offer little meaningful information to data subjects, raising questions around their value. Data controllers have an interest to not disclose information about their algorithms that contains trade secrets, violates the rights and freedoms of others (e.g. privacy), or allows data subjects to game or manipulate decision-making. Explanations of automated decisions need not hinge on the general public understanding how algorithmic systems function. Even though such interpretability is of great importance and should be pursued, explanations can, in principle, be offered without opening the black box. Looking at explanations as a means to help a data subject act rather than merely understand, one could gauge the scope and content of explanations according to the specific goal or action they are intended to support. From the perspective of individuals affected by automated decision-making, we propose three aims for explanations: (1) to inform and help the individual understand why a particular decision was reached, (2) to provide grounds to contest the decision if the outcome is undesired, and (3) to understand what would need to change in order to receive a desired result in the future, based on the current decision-making model. We assess how each of these goals finds support in the GDPR, and the extent to which they hinge on opening the ‘black box’. We suggest data controllers should offer a particular type of explanation, ‘unconditional counterfactual explanations’, to support these three aims. These counterfactual explanations describe the smallest change to the world that can be made to obtain a desirable outcome, or to arrive at the “closest possible world.” As multiple variables or sets of variables can lead to one or more desirable outcomes, multiple counterfactual explanations can be provided, corresponding to different choices of nearby possible worlds for which the counterfactual holds. Counterfactuals describe a dependency on the external facts that lead to that decision without the need to convey the internal state or logic of an algorithm. As a result, counterfactuals serve as a minimal solution that bypasses the current technical limitations of interpretability, while striking a balance between transparency and the rights and freedoms of others (e.g. privacy, trade secrets).

The ethics of algorithms: Mapping the debate:

Abstract: In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.

Counterfactual Explanations without Opening the Black Box: Automated Decisions and the Gdpr

Abstract: There has been much discussion of the “right to explanation” in the EU General Data Protection Regulation, and its existence, merits, and disadvantages. Implementing a right to explanation that opens the ‘black box’ of algorithmic decision-making faces major legal and technical barriers. Explaining the functionality of complex algorithmic decisionmaking systems and their rationale in specific cases is a technically challenging problem. Some explanations may offer little meaningful information to data subjects, raising questions around their value. Data controllers have an interest to not disclose information about their algorithms that contains trade secrets, violates the rights and freedoms of others (e.g. privacy), or allows data subjects to game or manipulate decision-making. Explanations of automated decisions need not hinge on the general public understanding how algorithmic systems function. Even though interpretability is of great importance and should be pursued, explanations can, in principle, be offered without opening the black box. Looking at explanations as a means to help a data subject act rather than merely understand, one can gauge the scope and content of explanations according to the specific goal or action they are intended to support. From the perspective of individuals affected by automated decision-making, we propose three aims for explanations: (1) to inform and help the individual understand why a particular decision was reached, (2) to provide grounds to contest the decision if the outcome is undesired, and (3) to understand what could be changed to receive a desired result in the future, based on the current decision-making model. We assess how each of these goals finds support in the GDPR, and the extent to which they hinge on opening the ‘black box’. We suggest data controllers should offer a particular type of explanation, ‘unconditional counterfactual explanations’, to support these three aims. These counterfactual explanations describe the smallest change to the world that would obtain a desirable outcome, or to arrive at a “close possible world.” As multiple variables or sets of variables can lead to one or more desirable outcomes, multiple counterfactual explanations can be provided, corresponding to different choices of nearby possible worlds for which the counterfactual holds. Counterfactuals describe a dependency on the external facts that lead to that decision without the need to convey the internal state or logic of an algorithm. As a result, counterfactuals serve as a minimal solution that bypasses the current technical limitations of interpretability, while striking a balance between transparency and the rights and freedoms of others (e.g. privacy, trade secrets).

Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation

Abstract: Since approval of the EU General Data Protection Regulation (GDPR) in 2016, it has been widely and repeatedly claimed that a ‘right to explanation’ of decisions made by automated or artificially intelligent algorithmic systems will be legally mandated by the GDPR. This right to explanation is viewed as an ideal mechanism to enhance the accountability and transparency of automated decision-making. However, there are several reasons to doubt both the legal existence and the feasibility of such a right. In contrast to the right to explanation of specific automated decisions claimed elsewhere, the GDPR only mandates that data subjects receive limited information (Articles 13-15) about the logic involved, as well as the significance and the envisaged consequences of automated decision-making systems, what we term a ‘right to be informed’. Further, the ambiguity and limited scope of the ‘right not to be subject to automated decision-making’ contained in Article 22 (from which the alleged ‘right to explanation’ stems) raises questions over the protection actually afforded to data subjects. These problems show that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against automated decision-making, and therefore runs the risk of being toothless. We propose a number of legislative steps that, if taken, may improve the transparency and accountability of automated decision-making when the GDPR comes into force in 2018.

The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts

Abstract: The capacity to collect and analyse data is growing exponentially. Referred to as ‘Big Data’, this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of the ethical implications of Big Data lags behind. In order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations. Particular attention is paid to biomedical Big Data due to the inherent sensitivity of medical information. By means of a meta-analysis of the literature, a thematic narrative is provided to guide ethicists, data scientists, regulators and other stakeholders through what is already known or hypothesised about the ethical risks of this emerging and innovative phenomenon. Five key areas of concern are identified: (1) informed consent, (2) privacy (including anonymisation and data protection), (3) ownership, (4) epistemology and objectivity, and (5) ‘Big Data Divides’ created between those who have or lack the necessary resources to analyse increasingly large datasets. Critical gaps in the treatment of these themes are identified with suggestions for future research. Six additional areas of concern are then suggested which, although related have not yet attracted extensive debate in the existing literature. It is argued that they will require much closer scrutiny in the immediate future: (6) the dangers of ignoring group-level ethical harms; (7) the importance of epistemology in assessing the ethics of Big Data; (8) the changing nature of fiduciary relationships that become increasingly data saturated; (9) the need to distinguish between ‘academic’ and ‘commercial’ Big Data practices in terms of potential harm to data subjects; (10) future problems with ownership of intellectual property generated from analysis of aggregated datasets; and (11) the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources. Considered together, these eleven themes provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices.

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Abstract: Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare and computer vision. There has been a recent rise of interest in developing methods for ‘explainable AI’, where models are created to explain how a first ‘black box’ machine learning model arrives at a specific decision. It can be argued that instead efforts should be directed at building inherently interpretable models in the first place, in particular where they are applied in applications that directly affect human lives, such as in healthcare and criminal justice.

A Survey of Methods for Explaining Black Box Models

Abstract: In recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

Abstract: At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.