The 3 Principals of Building Anti-Bias AI
In April of 2021, the U.S. Federal Trade Commission — in its “Aiming for truth, fairness, and equity in your company’s use of AI” report — issued a clear warning to tech industry players employing : “Hold yourself accountable, or be ready for the FTC to do it for you.” Likewise, the European Commission has proposed new AI rules to protect citizens from AI-based discrimination. These warnings, and impending regulations, are warranted.
Machine learning (ML), a common type of AI, mimics patterns, attitudes and behaviors that exist in our imperfect world, and as a result, it often codifies inherent and systemic racism. Unconscious biases are particularly difficult to overcome, because they, by definition, exist without human awareness. However, AI also has the power to do precisely the opposite: remove inherent human bias and introduce greater fairness, equity and economic opportunity to individuals on a global scale. Put simply, AI has the potential to truly democratize the world.
Just as a child observes surroundings, sees patterns and behaviors and mimics them, AI is susceptible to mirroring human biases. So, tech companies, like parents, carry the weighty responsibility of ensuring that racist, sexist and otherwise prejudiced thinking isn’t perpetuated through AI applications.
Unfortunately, AI’s unsavory reputation in that respect has been rightly earned. For example, in January of 2021, the entire Dutch government resigned after it was revealed that it used a biased algorithm to predict which citizens would be most likely to wrongly claim child benefits, which forced 26,000 parents (many selected due to their dual nationalities) to pay back benefits to the tax authority without the right to appeal.
Other research, conducted by the Gender Shades Project (a non-profit focused on gender discrimination), found that facial recognition machine learning is notoriously bad at accurately identifying people of color. Some of the impacts of associated applications have been catastrophic, such as identifying an innocent person as a criminal in a virtual line-up. In the summer of 2020, Detroit’s police chief confessed that the facial recognition his department used misidentified roughly 96% of suspects, leading to innocent people being identified as potential criminals. More recently, Amazon extended its ban on using its facial recognition software in policing due to concerns around racial misidentification.
But it doesn’t have to be this way. AI can be created not only to be unbiased, but also to fight back against inequities relating to race, gender, etc., and ironically, the only true solution to the biases in AI is human intervention.
