Investor Presentaiton

Executive Summary Improving Financial Health Data Employees Supply Chain Social - Fairness-as-a-Service case study Environment A capability helping to eliminate Artificial Intelligence bias in decisioning The issue There is a growing public awareness that the computer processes relied on to make financial, healthcare, hiring or housing decisions may suffer from unintentional bias. These automated systems, often introduced for cost efficiencies and handling of complex datasets, could be making decisions that are inadvertently sexist, racist or discriminatory Unfairness may come from multiple sources, including the underlying algorithm and the data introduced during development. During its training phase a machine-learning algorithm may become 'tainted' with historical bias because its learning from historical data which is already inherently biased from when these decisions were made exclusively by humans. Our response We've created and patented the first end-to- end Fairness-as-a-Service platform. It provides a unique combination of a decisioning platform, normative data and state-of-the-art algorithmic expertise, that helps answer the question 'What is fairness in AI?'. The roll-out is envisioned as a multi-industry, global offering that enables cross-selling opportunities for the Experian Ascend platform and Experian's data resources as well as supporting Experian's mission of enabling fair and impactful data-driven decisioning across the globe. Governance Policies & Data tables Appendix experian. The product It will help banks, consumers, regulators, universities and large organisations to: 1. Evaluate fairness - how fair is their data and model 2. Explain models - do they understand their model's predictions globally and also at an individual basis 3. Train fair models - so they are both predictive and fair 4. Apply fairness - so they can update their models to make them fair, as well as amend models' decisions to make those decisions fair. 36 Experian Public See p26 and p39 of FY20 Annual Report for further details

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