Brent Mittelstadt

Tonii Leach

How do AI algorithms make decisions? Why might we need to understand this, and to what extent do we need to understand this? How can we conceptualise ‘explainable’ AI? And when we ask for an explanation, what is it that we really want to know? On the 27th of February Dr Brent Mittelstadt gave a talk at De Montfort University (Leicester, UK) on ‘Governance of AI with explanations’ to address some of these questions.

Explainability of complex AI algorithms has become a highly contested topic since the introduction of the General Data Protection Regulation (GDPR) across the EU on the 25th May 2018. Whilst it is often cited that the GDPR confers a ‘right to explanation’ for an algorithmic decision made about an individual, Dr Mittelstadt argued that this is not the case. According to the recent paper Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation (Wachter, Mittelstadt and Floridi, 2017), GDPR simply provides a right to be informed about the existence of automated processes and system functionality if a decision is solely based on automated processes and has legal or significant effects for the individual. However, no explanation about the rationale of an individual decision is required.  Whilst this to some extent curbs the requirements for a detailed explanation, in situations where legal or significant effects for the individual are likely a clear explanation of the decision is key.

In this context, then, it is vital that there is an understanding of explainability and how this could work in practice. A distinction was made between explainability (pertaining to how a specific decision is made) and interpretability (focusing on a general understanding of how the system or algorithm works).

Explanation Relevance

Where someone is presented with an explanation for how a decision was made, the accuracy, usefulness and relevance of the explanation were discussed as key elements. The case of social media adverts was considered in relation to the impact of explainability on everyday life.

When seeing a targeted paid-for advert, an explanation for why the individual has been selected is offered, which is often a combination of age, gender and location which meets the pre-selected target audience for a specified product. However, these same characteristics are offered as an explanation even for adverts that are quite clearly based on browsing history – regardless of whether the product in any way relates to those characteristics. This was discussed in relation to the usefulness of explanations when the information provided is not central (and can be merely tangential) to the way in which the decision was made.


It was discussed that, for the individual, an understanding of why a particular outcome occurred instead of an alternative outcome is often what is actually being asked for when an explanation is requested. This is of particular relevance in situations where, for example, a person was turned down for credit when they did not expect to be. In this scenario, counterfactuals can be a useful method of providing an explanation. Counterfactuals are a type of explanation that describes how a decision depends on the external factors inputted. A counterfactual explanation addresses an alternative question; rather than asking ‘what was the cause of this decision?’, counterfactuals aim to answer ‘how can I get the decision I wanted?’. In this way, the counterfactual identifies the external factors that would need to be different in order to get the desired outcome.

The usefulness of counterfactuals was discussed, both in relation to multiple and equally valid counterfactual explanations, and how they can be used to meet the expectations of explanations for the individual. A key benefit of counterfactual explanations is that multiple counterfactual explanations can often be provided for a single decision. Counterfactuals can be used to identify a range of different factor combinations to achieve the desired outcome, with the individual then having an understanding of a variety of approaches they could take to remedy the issue resulting in the unwanted decision.

By providing counterfactual explanations for algorithmic decisions the goals of the individual can be met. They are able to understand the decision by having some of the key factors and logic revealed to them. Counterfactuals also support the implementation of Article 22 (3) of the GDPR, as the individual can challenge a decision if, for example, the input factors are not accurate. They also provide the individual with the opportunity to alter future decisions by revealing the key factors that would need to be addressed to achieve the desired outcome. The benefits of counterfactuals in AI and machine learning contexts are further explored in Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR (Wachter, Mittelstadt and Russell, 2018).

This talk provided a fascinating and useful insight into the issues around the provision of explanations being explored currently, and alternative approaches being considered to address these issues. It also provided a strong overview of the contentions around explanations for algorithmic decisions between industry, regulators, and the individual.

Dr Brent Mittelstadt is a Research Fellow and British Academy Postdoctoral Fellow in data ethics at the Oxford Internet Institute, a Turing Fellow at the Alan Turing Institute, and a member of the UK National Statistician’s Data Ethics Advisory Committee. He is an ethicist focusing on auditing, interpretability, and ethical governance of complex algorithmic systems.


Wachter, S., Mittelstadt, B. & Floridi, L. 2017, “Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation”, International Data Privacy Law, vol. 7, no. 2, pp. 76-99.

Wachter, S., Mittelstadt, B. & Russell, C. 2018, “Counterfactual Explanations Without Opening The Black Box: Automated Decisions And The GDPR”, Harvard Journal of Law & Technology, vol. 31, no. 2, pp. 841-887.

Tonii Leach holds the ‘Frontrunner in Responsible Artificial Intelligence’ internship at the Centre for Computing and Social Responsibility, De Montfort University (DMU, Leicester, UK) and contributes to the DMU team of Ethics Support in the Human Brain Project. With a Masters in Modern Literature, she is currently undertaking her PhD with DMU on the topic of ethics and Human Rights in next-generation Artificial Intelligence.

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