Opening the black box: helping AI to persuade without bias

The overall aim of the project is to establish how AI can use natural language to persuade humans in transparent and bias-free ways. AI systems are becoming an integral part of our daily lives, from simple recommendations on YouTube or Netflix, to life-changing decisions such as shortlisting job applicants or recommending loans. However, these systems lack transparency and objective indicators of fairness, producing poorly understood 'advice' that could be fuelling biased decisions on a growing scale right across society. Biased advice that treats individuals differently based on their gender is a particular concern (e.g. in employment contexts). Detecting and removing this bias is extremely difficult. It arises from stereotyped human labelling and categorisation residing within huge, cobbled-together data sets typically used to train AI. Because this bias is 'hidden', its transmission to humans interacting with AI is correspondingly difficult to establish, and even more difficult to eliminate. In this Phd, we propose and test a novel solution which combines experimental psychology with computing science to offer a way of detecting when biased advice from AI passes into human judgements. The student will engineer their own AI into which known patterns of gender-bias, deriving from our work on cognitive stereotypes, can be 'injected'. Using advanced natural language models, the AI will have brief 'conversations' with human participants in a series of studies that will provide a rich source of data on acts of rejection or conformity to biased and unbiased advice. Using cutting-edge machine learning techniques, the student will analyse this new bespoke data set to identify language features that signal persuasion. Within these features the student will seek to isolate ones specifically linked to acceptance of biased content. Crucially, these features may be used to detect the transmission of hidden biases in real-life applications.