Publications
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Showing 51 to 60 of 321 search results.
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Research Note: Credit where credit is due: How can we explain AI’s role in credit decisions for consumers?
As part of our AI research series, we explore the relative effectiveness of different methods for explaining the outputs of AI to consumers in the context of the use of determining consumers’ creditworthiness. -
Research Note: An empirical analysis of pricing differences by demographic characteristics in the UK mortgage market [pdf]
A statistical analysis of whether consumers appear to be paying different mortgage prices based on demographic characteristics. -
Research Note: An empirical analysis of pricing differences by demographic characteristics in the UK mortgage market
As part of our AI research series, we explore the potential for pricing differences by demographic characteristics in the mortgages market. -
Research Note: A pilot study into bias in natural language processing [pdf]
This research note presents the results of a technical investigation into biases in word embeddings. -
Research Note: A pilot study into bias in natural language processing
As part of our AI research series, we explore bias in a natural language context. -
Research Note: A Literature Review on Bias in Supervised Machine Learning [pdf]
Research Note: A Literature Review on Bias in Supervised Machine Learning. -
Research Note: A literature review on bias in supervised machine learning
In the first of a series of research notes on bias in artificial intelligence (AI), this literature review examines available literature on bias in the context of supervised machine learning. -
Cryptoassets consumer research 2024 (wave 5) [pdf]
Cryptoassets consumer research 2024 (wave 5). -
Research Note: Cryptoassets consumer research 2024
This analysis looks at cryptoasset holdings in the UK and consumers’ understanding, attitudes and behavioural patterns towards them. -
Research Note: A revision of our market cleanliness statistic methodology [pdf]
This Research Note reviews the methodology for calculating the market cleanliness statistic (MCS).