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Fairness and Accountability for Algorithms in Financial Services

How can credit unions differentiate on trust? This report reviews a key area where trust is increasingly at a premium: the use of consumers’ data in algorithmic credit scoring. With this change comes new questions and concerns, especially about the potential for bias and discrimination in algorithmic underwriting.

Executive Summary

Consumers have expressed distrust in the financial services industry while also indicating a high degree of trust in their own primary financial services provider. Given the rise in use of alternative data for credit scoring, credit unions have a responsibility to ensure that bias and discrimination do not occur while implementing algorithmic underwriting. With the rapid advancement of technology, the time to build authentic trustworthiness and consider the ethics of algorithmic decision-making is now. 

This workshop will help your team understand bias, algorithms, and the negative impact of discrimination, while also providing tools for taking ethical steps toward adopting algorithmic processes.

Filene thanks its members and Inner Circle sponsors for helping support this research from the Center for Emerging Technology.

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