The area of language and AI is particularly important for business leaders. Language runs through AI — in data, data labels, and language-specific applications like natural language processing (NLP). These systems are susceptible to the same harms that occur in human communication, including reflecting and reinforcing harmful biases. Yet, existing management strategies to tackle bias and advance language for equity and inclusion are insufficient. The guide is a launching point to address this gap.
Advancing language that supports equity and inclusion within AI and ML systems can promote positive norms and lead to a more inclusive product experience, while also better reflecting a company’s mission, ethical principles, responsible innovation commitments and stated product goals. This can enhance user trust and brand reputation, while mitigating risk — both reputational and regulatory. Responsible AI leadership is a competitive advantage that can serve as a driver for the company, including through being a business of choice for local and national governments (a large customer for AI technology). As investors are increasingly seeking to incorporate ESG framings into investment decisions, centering equity and inclusion as core drivers for AI products can set companies apart.
Business leaders have a central role to play, while bearing responsibilities to connect the mission and values of the company to the products and services it develops.