By Kavitha Prabhakar, Kristi Lamar, and Anjali Shaikh
The speed at which many companies churn out technology-based innovations is rapidly accelerating, despite the potential for unintended societal and business risks. The laws and regulations intended to protect the public from such risks often do not keep pace with this exponential rate of innovation. For instance, many companies consider AI, descriptive analytics, and data mining critical to corporate strategy, but these advances can also perpetuate stereotypes and biases hidden within data.
Organizations lacking divergent and collective viewpoints during the design and development process could spend vast amounts of time and money innovating products or services that unintentionally exclude customer groups, reflect assumptions or biases, generate adverse side effects, or otherwise undermine trust. “Diversity is more important than ever,” says Judith Spitz, founding program director of the Initiative for Women in Technology and Entrepreneurship at Cornell Tech. “A diverse workforce is the first line of defense against algorithmic bias.”