Everybody has bias. It’s a universal phenomenon. That’s why confronting biases in AI models and predictions is critically important now. Avoiding the topic can be more detrimental than dealing with it head-on. Recognizing decision bias helps us, as the most harmful biases are often overlooked.

A critical aspect of bias lies in blind spots, particularly in predicting access to resources like loans or insurance, as examples. In these situations, it’s important to not shy away from information that may carry bias but to seek it out actively. By acknowledging the presence of biased data, we can measure and demonstrate that specific traits, such as ethnicity, are present but not significant in driving predictions. Attempting to exclude biased data only perpetuates a “see no evil” mentality, whereas measuring and including it can mitigate harmful biases effectively.

There are some scenarios where hidden signals within seemingly innocuous data can be ethically applied, such as determining suitability for financial aid or scholarships. At Wrench.ai, we use a client-driven content creation process to ensure that content aligns with brand goals while addressing diversity, equity, and inclusion concerns. In this scenario, we rely on collaborative efforts to incorporate the client’s input, brand insights, customer profiles, regulations, and goals to shape the content generated by our AI platform. This collaborative approach ensures that what we generate not only resonates with a client’s brand but also promotes diversity and inclusivity.

While bias can exist in AI models, there’s no one-size-fits-all solution due to our client’s diverse business needs. That said, we’re very much committed to minimizing and addressing harmful biases through transparency, continual monitoring, review, and ethical decision-making. We recognize that bias can unintentionally occur, and that’s why we strive to be aware of our biases and blind spots during our AI model development. Our dedication to ongoing training and retraining of AI models ensures that biases are kept in check and do not become more pronounced over time.