A Right to Explanation for Algorithmic Credit Decisions in the UK

Algorithmic processing of credit data is widely used in UK banks.‘Algorithmic processing’ refers to the processing of personal and non-personal data by automated systems. This includes artificial intelligence (AI) systems such as machine learning models. The widely used description:‘black box’ phenomenon, refers to the challenges of how and why algorithms arrive at a decision, given specific data input. The phrase ‘black box’ is used because such machine learning algorithms are very complex and constantly adapt to new input through ‘deep learning’. ‘Deep learning’ is a sub-set of machine learning. It allows computers to learn and think independently. In ‘deep learning’ technology, the decision-making process is often opaque and difficult to explain in a way that can be easily understood by humans. This inscrutability can lead to distrust in algorithms. The stakes are especially high when such algorithms are applied to decisions on access to finance because some of the most inscrutable machine learning models are used. I argue for a statutory right to explanation in automated credit decision-making in the UK, as transparency and accountability are central to the rule of law. This is based on two premises. First, from a moral standpoint, I demonstrate that there is a double level of distrust in financial services and algorithms. Algorithms are unpredictable and can make unreliable, strange decisions. Algorithmic challenges such as bias, discrimination and unfairness are exacerbated by the opacity problem commonly known as the ‘black box’ phenomenon. The informed consent process in automated credit decision-making is thus incomplete, which requires an ex-post right to explanation for completing the informed consent procedure. Secondly, the doctrinal and comparative legal methodologies reveal that countries such as the USA, Canada, European Union, China and Poland already provide a right to explanation to credit applicants under certain circumstances. Central to my argument is the introduction of new empirical evidence of public surveys regarding a desire from the public to have a right to explanation for unsuccessful credit applications. I argue for a statutory right to meaningful and accessible local feature-based information to automated credit decision making, which should include objective criteria and weightings used by banks.

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