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Despite the relevance of contractual conflict in legal practice, there is yet to be a dataset which captures the type of issues and clauses that result in cases being brought before the courts. Such a dataset would be invaluable to a machine learning algorithm that seeks to predict whether new clauses are likely to cause conflict. In this study, we analyse a dataset based on half a million United Kingdom court decisions decided between 1709 and 2021, from which we extract 60,379 cases dealing with contracts. We characterise the language of this dataset using Latent Dirichlet Allocation to approximate legal topic modelling. We augment the data by plotting it with the court names and dates for each case, which allows for a racing bar chart visualisation. This is the first study of its kind to provide easy access to legal researchers on cases dealing with contracts in the United Kingdom.

Despite the relevance of contractual conflict in legal practice, there is yet to be a dataset which captures the type of issues and clauses that result in cases being brought before the courts. Such a dataset would be invaluable to a machine learning algorithm that seeks to predict whether new clauses are likely to cause conflict. In this study, we analyse a dataset based on half a million United Kingdom court decisions decided between 1709 and 2021, from which we extract 60,379 cases dealing with contracts. We characterise the language of this dataset using Latent Dirichlet Allocation to approximate legal topic modelling. We augment the data by plotting it with the court names and dates for each case, which allows for a racing bar chart visualisation. This is the first study of its kind to provide easy access to legal researchers on cases dealing with contracts in the United Kingdom.

Consider how much data is created and used based on our online behaviours and choices. Converging foundational technologies now enable analytics of the vast data required for machine learning. As a result, businesses now use algorithmic technologies to inform their processes, pricing and decisions. This article examines the implications of algorithmic decision-making in consumer credit markets from economic and normative perspectives. This article fills a gap in the literature to explore a multi-disciplinary approach to framing economic and normative issues for algorithmic decision-making in the private sector. This article identifies optimal and suboptimal outcomes in the relationships between companies and consumers. The economic approach of this article demonstrates that more data allows for more information which may result in better contracting outcomes. However, it also identifies potential risks of inaccuracy, bias and discrimination, and ‘gaming’ of algorithmic systems for personal benefit. Then, this article argues that these economic costs have normative implications. Connecting economic outcomes to a normative analysis contextualises the challenges in designing and regulating ML fairly. In particular, it identifies the normative implications of the process, as much as the outcome, concerning trust, privacy and autonomy and potential bias and discrimination in ML systems. Credit scoring, as a case study, elucidates the issues relating to private companies. Legal norms tend to mirror economic theory. Therefore, this article frames the critical economic and normative issues required for further regulatory work.

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