#Equal credit opportunity code#

This is one of the more straightforward areas for the FTC to enforce against, and typically where the FTC issues guidance about a particular technology, they are vigilant about misrepresentations related to that technology. Don't exaggerate what your algorithms can do or whether it can deliver fair or unbiased results: The FTC reminds organizations to not exaggerate what their algorithms can do, as exaggerations may run afoul of the deception provisions of Section 5.The 2020 guidance further notes the importance of being transparent with consumers regarding the use of automated tools – including the factors used to generate any automated decisions.

#Equal credit opportunity code#

Embrace transparency and independence: In order to reduce the potential for discriminatory outcomes, the FTC suggests embracing transparency and independent review by, for example, conducting and publishing independent audits and publishing source code for outside inspection.(4) Does the particular reliance on big data raise ethical or fairness concerns? (3) How accurate are the predictions based on big data? (2) Does the data model account for biases? Some questions the FTC suggests to assess the fairness of algorithms are: Additionally, the FTC's 2020 guidance notes that organizations should consider the potential for disparate impact in an AI system's outcomes. Watch out for discriminatory outcomes: The FTC recommends testing algorithms before use and regularly thereafter to 'make sure that discriminate on the basis of race, gender or other protected class.' Again, this builds off the 20 recommendations designed to make AI outcomes fair and ethical.The FTC has previously noted that when evaluating the legality of AI, it will consider inputs to the model, 'such as whether the model includes ethnically-based factors, or proxies for such factors, such as census tract.' This builds on the FTC's 2020 guidance, which recommended that companies validate and revalidate data sets to not only ensure accuracy but also to avoid unlawful discrimination, as well as the FTC's 2016 big data report, which details the importance of relying on representative data sets and vetting data sets for bias. And organizations should consider how they can improve their data sets or establish controls for AI to address any gaps, including limiting how and where the algorithm is used (depending on the potential data shortcomings). Start with the right foundation: The FTC states that the key to addressing disparate treatment of protected groups is to assess, from the beginning, whether training data sets have gaps.The FTC's updated guidance provides insight into the expectations for organizations using AI.