Diversity and Inclusion in Artificial Intelligence

For instance, backgrounds in marketing, social media marketing, social work, education, public health, and journalism can contribute fresh perspectives and expertise. Second, diversity and inclusion should be covered in training and development programs via mentorships, job shadowing, simulation exercises, and contact with diverse end user panels. Third, partnerships with academic, civil society and public sector institutions should be established to contribute to holistic and pan-disciplinary reviews of AI systems, diversity and inclusion audits, and assessment of social impacts. Fourth, a workplace culture of belonging should be created and periodically assessed via both open and confidential feedback mechanisms which include diversity markers.