Recent controversies around biased outputs from AI systems like image generators and facial analysis tools have highlighted the urgent need for more diversity in AI development teams. Homogenous teams that lack representation from different genders, ethnicities, and socioeconomic backgrounds lead to blindspots that allow harmful biases and stereotypes to seep into AI algorithms and data easily. However, research consistently shows that diverse teams build better AI systems by bringing different perspectives. Assembling inclusive teams is a crucial strategy for identifying and mitigating sources of bias before products ever launch.
Here are some recommendations on how companies can build more diverse AI teams to counteract algorithmic bias:
The Case for Diversity
Many factors contribute to the current lack of diversity across the AI field, including:
- Historical gender, racial, and socioeconomic inequality in access to STEM education and technology careers.
- Ongoing biases in hiring, promotion, and advancement that favor majority demographics.
- Lack of belonging and unwelcoming culture causes marginalized groups to leave tech.
- Lack of access to capital and resources for minority entrepreneurs.
- These systemic disparities result in AI development being dominated by a homogenous set of perspectives – typically white, Asian, male, and socioeconomically privileged. While undoubtedly well-intentioned, these homogeneous teams suffer from distinct blind spots that prevent them from noticing potential harms in the AI systems they build. Some examples include: Not realizing training data needs to have more diversity or contain skewed representations.
- Failing to recognize cultural nuances outside one’s own background.
- Assuming benchmarks alone indicate an AI system is fair and unbiased.
- Lacking empathy for marginalized groups negatively impacted.
These blindspots underscore why diversity is not just a nice-to-have, but a necessity for ethical AI. Teams that bring together people from different ethnicities, genders, backgrounds, and experiences are better equipped to identify sources of unfair bias before products launch.
Building Inclusive Teams
Companies serious about mitigating algorithmic bias must focus on building more diverse and inclusive AI teams across multiple areas:
Recruiting and Hiring
- Proactively sourcing talent from minority demographics overlooked by typical recruiting channels.
- Removing biased requirements and language from job postings that deter diverse candidates.
- Using skills-based assessments rather than traditional resume screening.
- Having diverse panels conduct interviews with a focus on inclusion.
Retention and advancement
- Fostering belonging for minorities through employee resource groups, mentoring programs, and valuing diverse voices.
- Providing opportunities for growth and leadership development.
- Assessing and addressing reasons for attrition among underrepresented groups.
- Linking diversity metrics to executive compensation.
Community Input
- Forming advisory boards with external experts from diverse backgrounds.
- Conducting pre-launch audits to uncover biases in data, algorithms, and outputs.
- Continuously gathering feedback from marginalized groups post-launch.
- Partnering with nonprofits focused on algorithmic fairness.
Education and training
- Offering extensive bias mitigation training and resources for building empathy.
- Openly discussing real harms from AI failures in a non-punitive manner.
- Bringing in speakers from diverse groups to share experiences.
- Promoting cultural values of inclusion, ethics, and empowerment.
Achieving diversity requires a continuous focus spanning the entire machine learning pipeline – from data collection to model evaluation and post-launch monitoring. It cannot be an afterthought.
Challenges and Considerations
While diversity provides immense benefits, it is not a panacea, and challenges remain, including:
- Systemic educational, social, and economic disparities cannot be quickly reversed.
- Diversity alone does not guarantee better outcomes if systemic biases persist.
- Quantifying progress on diversity can be complex and reductionist if done poorly.
- Marginalized voices brought in to consult may feel exploited or undervalued.
- Majority groups may react negatively to diversity initiatives.
- Team cohesion and productivity may initially decrease in heterogeneous teams.
These are valid concerns that diversity advocates must seek to understand and address in good faith. But none of these criticisms imply pursuing diversity lacks merit. Instead, they highlight how variety must be paired with sustained commitment to inclusion, equity, and institutional change to have the most significant positive impact.
Recommendations for the Path Forward
- While progress takes time, companies should proactively build more diverse AI teams to mitigate algorithmic harms, guided by the following recommendations: Set specific, measurable diversity goals across all levels of the organization.
- Commit to sustained, long-term effort over the years, not quick fixes.
- Take a nuanced, intersectional view of diversity, not a reductive, limited one.
- Pair diversity efforts with reforms to address root causes of inequality.
- Listen to and empower marginalized voices; don’t just use them for validation.
Case Studies in Inclusive Teams and AI
While challenges exist, many AI labs and companies demonstrate that building inclusive teams is possible with sustained effort: Google’s AI residency program focuses on developing promising women, minorities, and other talent often overlooked by the industry.
- Microsoft’s LEAP apprenticeship brings nontraditional candidates into engineering roles through on-the-job training, mentoring, and support.
- Facebook partners with minority-serving schools and conferences to improve diverse recruiting. They also have employee resource groups.
- IBM’s AI Ethics Board comprises diverse social science, policy, and ethics experts who provide guidance.
- Startups like Fiddler Labs are building tools to audit models for bias by explaining model behavior during development.
- The Algorithmic Justice League publicizes model failures in facial analysis to motivate change.
- The Partnership on AI coalition brings nonprofits like the ACLU together with companies to develop best practices.
These examples demonstrate meaningful progress is possible when organizations commit to long-term, intentional efforts to build inclusive teams and AI systems. But work remains to make diverse teams the norm, not the exception in the industry.