Technology promises a wide range of efficiencies; one of the most promising and yet elusive is the removal of human bias from decision making. A recent paper published by New York University (NYU) researchers presents a glimmer of hope for a less biased future. The study found that during Covid-19, “the majority of Black borrowers who received aid from the $800 billion relief program got their loan from a financial technology company, not a bank.” The study explains that fin-tech companies and large banks that employed algorithms and automation demonstrated higher rates of loan approvals for Black borrowers compared to small banks not using the technology.
In the study, the research showed that this distinction was even more pronounced in areas with higher instances of racial animus and that when companies switched to an automated process midway through the funding program, “the share of loans to Black-owned firms noticeably increased after the switch.”
This glimmer of hope comes at a time when there is widespread criticism of biased algorithms, and while the failures of biased algorithms have led to painful repercussions, these errors are hopefully part of the solution. Unlike humans, algorithms and automation are easily changed and adjusted when issues are identified. Dr. Howell, a member of the research team at NYU writes,
You can constrain an algorithm to meet fair-lending standards, and you can ensure the data it trains on isn’t biased. That may be hard to do, but it’s a clear and objective possibility. Whereas when you have a human loan officer who is in front of someone and making a decision, you can never do that.
As Dr. Howell points out, there are ways to reduce and eliminate bias in algorithms, although the trick is that these involve human intervention which is subject to bias.
That being said, before assessing the data or algorithm, it is likely beneficial to investigate our own biases. By recognizing our own potential biases, we can be on alert as to whether they impact our work and if they do appear, we can course correct.
Here are a few other ways to prevent biased algorithms:
There is reason to hope that technology will play an influential role in creating a less biased future society but in order to get there, there is still a lot of work to be done.