Experian
social fairness as a service case study a newly created service helping to eliminate artificial intelligence bias in the issue our response the product there is a growing public awareness that the computer processes relied on to make financial hiring or housing decisions may suffer from unintentional bias these systems often introduced for cost efficiencies and handling of complex could be making decisions that are inadvertently 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 we created and patented the first end to end fairness as a service platform it will help banks consumers regulators universities and large to it provides a unique combination of a platform normative data and state of the art algorithmic that helps answer the question what is fairness in the roll out is envisioned as a industry global offering that enables cross selling opportunities for the ascend platform and data resources as well as supporting mission of enabling fair and data driven across the globe evaluate fairness how fair is their data and model explain models do they understand their model predictions globally and also at an individual basis train fair models so they are both predictive and fair apply fairness so they can update their models to make them fair as well as amend models decisions to make those decisions fair | Experian
Company
Deck Type
ESG
Sector
Industry
Deck date
June 2022
Slide
29 of 91
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