Keep the “Fair” in Fair Lending
Ensuring that lending practices don’t discriminate based on race, gender, and a host of other considerations is not just the law — it’s the right thing to do, and it’s good for business.
However, as lending institutions increasingly incorporate artificial intelligence into their application review and decisionmaking practices, they increase their risk of making unintended discriminatory decisions. Poorly designed algorithms, flawed data, or even benign changes to system logic can lead to unfair and potentially illegal lending practices. Fixing these issues when they occur and preventing them from becoming part of AI modeling is imperative to regulatory compliance.
Fairly’s proprietary anti-bias auditing technology enables banks and other lending institutions to assess the results of their AI algorithms and then identify, determine the source of, and provide fixes for issues that we find. Fairly can also advise institutions about best practices to avoid issues going forward.
With Fairly guiding unbiased AI, financial institutions can rest assured that their lending are good for business, as well as fair and compliant.
Reject Bias in Hiring
Consider that one individual online job posting can attract hundreds, if not thousands of applications. Then multiply that by the dozens of open positions companies can have simultaneously. It’s no wonder that organizations are looking at AI solutions to help them do an initial sort through the digital stacks of responses they receive and spotlight the candidates with the highest potential.
But experience has shown how easy it is for unintentional bias to make its way into AI algorithms and leave companies exposed to unintended unfair hiring practices. Company leaders want and need to hire the best people for the job. They also want and need to avoid any chance or even the perception that they are giving preferential treatment to certain applicants, or worse, disadvantaging legally protected classes.
Fairly helps hiring teams root out bias in their AI systems and ensure that every qualified candidate for a job is given equal and fair consideration.
Diagnose, then Cure the Problem
AI is playing an increasingly important role in healthcare. From assessing patient x-rays and scans, to diagnosing cancer, to managing emergency room flow, health care providers and hospital administrators are using AI to help inform diagnoses and make critical decisions. So it’s no surprise that having accurate and unbiassed data feeding into AI algorithms is a matter of life and death.
But outdated or erroneous assumptions, demographic inaccuracies, and myriad other biases can too easily infiltrate healthcare AI, not only impacting patient care and outcomes, but also placing healthcare providers at legal risk.
Fairly can help reveal and eliminate hidden bias in AI. Working with hospital’s cross-functional team of physicians, administrators, and IT experts, Fairly can audit incoming data and algorithms to make sure AI systems are continuously calibrated to produce positive patient care outcomes.
Avoid Admitting Mistakes
Every year colleges and universities are inviting, and receiving, ever higher numbers of student applications. They’re incorporating AI systems into their admissions process to help deal with the tens of thousands of applicants and help inform decisions.
But if their AI algorithms are created from data with inherent bias, then those tainted systems will deliver biased and perhaps even discriminatory results to admissions officers. And that bias is likely to be unintentionally carried through in admissions decisions. As recent legal challenges to university admissions process have shown, no institution can afford to take that risk.
With Fairly, admissions offices can identify and root out bias in their AI systems, understand the causes of that bias, and determine how to eliminate it. As a result, colleges and universities can more easily and confidently filter through their many thousands of student applicants to create a class cohort with the ideal mix of talents, interests, and demographic representation — and untainted by bias.