Brent Mittelstadt

Exploring the role of algorithms in our everyday lives, and how a “right to explanation” for decisions might be achievable in practice

Algorithmic systems (such as those deciding mortgage applications, or sentencing decisions) can be very difficult to understand, for experts as well as the general public. Image: Ken Lane (CC BY-NC 2.0).

The EU General Data Protection Regulation (GDPR) has sparked much discussion about the “right to explanation” for the algorithm-supported decisions made about us in our everyday lives. While there’s an obvious need for transparency in the automated decisions that are increasingly being made in areas like policing, education, healthcare and recruitment, explaining how these complex algorithmic decision-making systems arrive at any particular decision is a technically challenging problem—to put it mildly. In their article “Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR” which is forthcoming in the Harvard Journal of Law & Technology, Sandra Wachter, Brent Mittelstadt, and Chris Russell present the concept of “unconditional counterfactual explanations” as a novel type of explanation of automated decisions that could address many of these challenges. Counterfactual explanations describe the minimum conditions that would have led to an alternative decision (e.g. a bank loan being approved), without the need to describe the full logic of the algorithm. Relying on counterfactual explanations as a means to help us act rather than merely to understand could help us gauge the scope and impact of automated decisions in our lives. They might also help bridge the gap between the interests of data subjects and data controllers, which might otherwise be a barrier to a legally binding right to explanation. We caught up with the authors to explore the role of algorithms in our everyday lives, and how a “right to explanation” for decisions might be achievable in practice: Ed: There’s a lot of discussion about algorithmic “black boxes” — where decisions are made about us, using data and algorithms about which we (and perhaps the operator) have no direct understanding. How prevalent are these systems? Sandra: Basically, every decision that can be made by a human can now be made by an algorithm, which can be a good thing. Algorithms (when we talk about artificial intelligence) are very good at spotting patterns and…

The algorithms technology rely upon create a new type of curated media that can undermine the fairness and quality of political discourse.

The Facebook Wall, by René C. Nielsen (Flickr).

A central ideal of democracy is that political discourse should allow a fair and critical exchange of ideas and values. But political discourse is unavoidably mediated by the mechanisms and technologies we use to communicate and receive information—and content personalisation systems (think search engines, social media feeds and targeted advertising), and the algorithms they rely upon, create a new type of curated media that can undermine the fairness and quality of political discourse. A new article by Brent Mittlestadt explores the challenges of enforcing a political right to transparency in content personalisation systems. Firstly, he explains the value of transparency to political discourse and suggests how content personalisation systems undermine open exchange of ideas and evidence among participants: at a minimum, personalisation systems can undermine political discourse by curbing the diversity of ideas that participants encounter. Second, he explores work on the detection of discrimination in algorithmic decision making, including techniques of algorithmic auditing that service providers can employ to detect political bias. Third, he identifies several factors that inhibit auditing and thus indicate reasonable limitations on the ethical duties incurred by service providers—content personalisation systems can function opaquely and be resistant to auditing because of poor accessibility and interpretability of decision-making frameworks. Finally, Brent concludes with reflections on the need for regulation of content personalisation systems. He notes that no matter how auditing is pursued, standards to detect evidence of political bias in personalised content are urgently required. Methods are needed to routinely and consistently assign political value labels to content delivered by personalisation systems. This is perhaps the most pressing area for future work—to develop practical methods for algorithmic auditing. The right to transparency in political discourse may seem unusual and farfetched. However, standards already set by the U.S. Federal Communication Commission’s fairness doctrine—no longer in force—and the British Broadcasting Corporation’s fairness principle both demonstrate the importance of the idealised version of political discourse described here. Both precedents…