The expanding use of AI is attracting new attention to the importance of workforce diversity. Data from the U.S. Bureau of Labor Statistics shows that, although technology companies have increased efforts to recruit women and minorities, computer and software professionals who write artificial intelligence (AI) programs remain largely white and male. A byproduct of this lack of diversity is that datasets often lack adequate representation of women or minority groups. For example, one widely used dataset is more than 74% male and 83% white, meaning algorithms based on this data could have blind spots or biases built in. Biases in algorithms can skew decision-making, and many companies have realized that eliminating bias upfront among those who write code is essential.

The expanding use of AI is attracting new attention to the importance of workforce diversity. Although tech companies have stepped up efforts to recruit women and minorities, computer and software professionals who write AI programs are still largely white and male, Bureau of Labor Statistics data show. Developers testing their products often rely on data sets that lack adequate representation of women or minority groups. One widely used data set is more than 74% male and 83% white, research shows. Thus, when engineers test algorithms on these databases with high numbers of people like themselves, they may work fine.