December 22, 2023

Partner with ethicists and antiracism experts in developing, training, testing, and implementing models. Recruit diverse and representative populations in training samples.

December 18, 2023

Data science teams should be as diverse as the populations that the built AI systems will affect. Product teams leading and working on AI projects should be diverse and representative of impacted user cohorts. Diversity, equity, and inclusion in the composition of teams training, testing and deploying AI systems should be prioritized as the diversity of experience, expertise, and backgrounds is both a critical risk mitigant and a method of broadening AI system designers’ and engineers’ perspectives. For example, female-identifying role models should be fostered in AI projects. Diversity and inclusion employment targets and strategies should be regularly monitored and adjusted if necessary. The WEF Blueprint recommends four levers. First, widening career paths by employing people from non-traditional AI backgrounds, embedding this goal in strategic workplace planning. 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.

December 18, 2023

An ‘AI-ready’ person is someone who knows enough to decide how, when and if they want to engage with AI. Critical AI literacy is the pathway to such agency. Consequently, governments should drive the equitable development of AI-related skills to everyone from the earliest years via formal, informal, and extracurricular education programs covering technical and soft skills, along with awareness of digital safety and privacy issues. Governments and civil society organisations should create, and fund grant schemes aimed at enhancing the enrolment of women in AI education. Organizations also can play a critical role via paid internships and promoting community visits, talks, workshops, and engagement with AI practitioners. To harness the potential of increasing diversity and inclusion in the global AI ecosystem, such opportunities should prioritise participation (as facilitators and participants) of people with diverse attributes (including cultural, ethnic, age, gender identification, cognitive, professional, etcetera).