A Blueprint for Equity and Inclusion in Artificial Intelligence

The report argues that AI's full potential requires the inclusion of diverse populations throughout its development to prevent bias and harm. It proposes a dual-level approach: building an inclusive ecosystem through equitable infrastructure, AI literacy, and diverse hiring, and implementing 'inclusion by design' across a seven-step technical life cycle. The document emphasizes continuous monitoring, stakeholder engagement, and the adoption of indigenous perspectives, such as Māori data sovereignty, to ensure AI serves collective well-being rather than just economic gain.

• An inclusive AI ecosystem requires equitable access to technical infrastructure, such as computing and data storage, to enable participation from citizens who cannot afford commercial cloud resources.
• AI literacy and education are essential prerequisites for equity, enabling individuals to be critical users who understand the risks and rights associated with AI-driven systems.
• Organizations can foster inclusive AI by diversifying their workforce through widened career paths for non-traditional backgrounds, mentorship, and a workplace culture of belonging.
• Inclusive AI requires 'inclusion by design,' which begins with clearly defining the problem from multiple perspectives to ensure AI is only deployed where it is necessary and desired by the community.
• Effective community engagement must avoid 'fly in, fly out' extractive practices and instead invest in long-term, reciprocal relationships with impacted communities, including providing compensation and ensuring safety.
• Inclusive model design involves managing complex trade-offs between predictive accuracy and fairness outcomes across different sub-populations and contexts.
• Data collection must prioritize the representation of marginalized groups and respect data sovereignty, which allows communities to have autonomy over the collection and use of their own information.
• To prevent the release of flawed tools, developers should use diverse beta groups and iterative testing to identify biases before full deployment.
• Post-deployment monitoring is critical because AI systems can evolve to make unfair decisions as the operating context or user populations change.
• The AI life cycle should be viewed as an evolving process—analogous to the Māori 'koru' spiral—rather than a closed circle, allowing for continuous expansion and adjustment based on lessons learned.

APA

World Economic Forum (2022). A Blueprint for Equity and Inclusion in Artificial Intelligence. https://www3.weforum.org/docs/WEF_A_Blueprint_for_Equity_and_Inclusion_in_Artificial_Intelligence_2022.pdf

Chicago

World Economic Forum. A Blueprint for Equity and Inclusion in Artificial Intelligence. 2022. https://www3.weforum.org/docs/WEF_A_Blueprint_for_Equity_and_Inclusion_in_Artificial_Intelligence_2022.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=A Blueprint for Equity and Inclusion in Artificial Intelligence |date=June 2022 |url=https://www3.weforum.org/docs/WEF_A_Blueprint_for_Equity_and_Inclusion_in_Artificial_Intelligence_2022.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2022blueprint, author = {{World Economic Forum}}, title = {{A Blueprint for Equity and Inclusion in Artificial Intelligence}}, institution = {World Economic Forum}, year = {2022}, month = jun, url = {https://www3.weforum.org/docs/WEF_A_Blueprint_for_Equity_and_Inclusion_in_Artificial_Intelligence_2022.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }