Developing Trust in Algorithmically Driven Services by Enhancing Explainable and Fair Machine Learning Through Co-Creation and Customer Interactions

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Aims and Intended ImpactResearch Questions:To what extent can co-creation methods with end-users be used to innovate FairML/XAI?How does interaction design influence the trust end-user's have in FairML and/or XAI algorithm decision making.What improvements do such innovations bring from an end-user and Financial Institution point of view.Outcomes and contributions of the work:Understanding of state of the art and issues to be addressedMethods for user-centered FairML/XAIFairML/XAI innovations evaluated in lab and deployed settingsEvaluating and designing FairML practices is difficult for three broad reasons: First, arriving at a suitable definition of fairness is hard - great thinkers have debated about this for centuries - even in focused pragmatic contexts there are often complexities and value-based tensions [1]; Second, developing a data-driven algorithm that performs fairly across society has been shown to be the exception rather than the norm, recent years have been littered with many examples of how this can go wrong [2]-[5]; Third, the deployment, uptake and impact on the end-user of such automated decision systems is too often a secondary consideration or retrospectively appraised, if at all.PhD yearly plan outlineIn the first year, the work will involve the PhD researcher immersing themselves in the state-of-the art in terms of interfaces and interactions with FairML and XAI algorithms etc. Inaddition, they will carry out primary research by surveying a range of existing fintech services to document and characterise the approaches currently deployed. This work will furtherinvolve focus groups, workshops, surveys with customers and potential customers to understand any perceptions they have of bias/ lack of fairness etc.Year two will begin with a series of investigations that attempt to draw out potential ways of improving the fairness and justice of the relationship being built between FairML approachesto services and customers: this work would attempt to tease out the extent to which people believe services are treating them fairly and without bias. This might involve a range of empirical techniques such as: wizard-of-oz where users interact with an automated service (which is in fact being controlled by a human) and where they are proactively asked about whether they understand and trust decisions/ interactions (and how the service could improve it); prototypes that expose various components of FairML models or XAI explanations of service decisions to end-users; communities to supplement answers/ decisions to be contextualized; a-b style experiments where users responses are analysed and compared etc.Year three, will allow for robust follow-up, iteration and refinement of the experimental work carried out in year two with further exposure to the real world-context. Year 1 and year 2 will enable the PhD researcher to gather a range of data that could be then used to consider patterns that surface key factors that lead to performance improvements and increasedperception of bias or fairness. If successful, the PhD will be able to then demonstrate a suite of tools and/or design approaches that organizations can use to uncover these nuanced biases and use them to assess/ improve their models. Year 1 and 2 can carry out a pilot experiment to test this hypothesis with Year 3 being used for a more substantial test and full write up of the thesis.