Artificial intelligence (AI) is becoming increasingly prevalent in our lives. From facial recognition to predictive text, AI is automating tasks and shaping how we interact with technology. However, there are growing concerns about bias in AI systems that can lead to discriminatory and unethical outcomes. One of the root causes of bias in AI is a lack of diversity among AI developers. The current field of AI experts is relatively homogenous, dominated by white men who make up the majority of leading researchers and engineers at prominent technology companies. This lack of diverse perspectives results in AI systems that reflect existing societal biases and fail to be inclusive. To build more equitable, ethical, and unbiased AI, we need to have more diverse teams involved in developing, auditing, and governing these technologies. Recruiting people from different backgrounds and walks of life will lead to AI that better serves everyone.
The Importance of Diversity in AI
Diversity provides tangible benefits when building AI systems:
- Broader perspectives – People with different backgrounds bring diverse viewpoints, helping illuminate blindspots. This leads to recognizing excluded groups or use cases.
- Avoiding biased data – Homogenous teams can overlook biases in training data that diverse teams would catch. Diversity helps ensure data is representative.
- Inclusive development – Teams with wide-ranging identities and experiences build inclusive products catering to diverse users, not just the majority.
- Building trust – AI designed by representative teams is more likely to be accepted by the public and seen as ethical. Diversity is key for trust.
- Better innovation – Different perspectives spark creativity. Research shows diverse teams are more innovative, helping generate new ideas and solutions.
In short, diversity enables building AI that is fairer, more inclusive, more creative, and more trusted. Homogenous teams lack the range of voices needed to address AI’s ethical challenges.
Current Lack of Diversity in AI
While diversity is critical for ethical AI, the current field of AI experts is far from diverse:
- A survey by Element AI found only 12% of European AI professionals were women. For comparison, women make up nearly half the overall workforce.
- In the US, only 2.5% of Google’s workforce is Black, with Black employees representing only 1.4% of tech roles. Hispanics make up just 3.7% of the tech workforce.
- Leadership roles are even less diverse – only 3% of AI research staff at Facebook and Google are women.
- A study by NYU found facial analysis AI has error rates up to 35% higher for darker skinned women compared to lighter skinned men due to lack of diverse training data.
This homogeneity stems from several factors:
- Pipeline issues – Fewer women and minorities pursue tech careers due to lack of exposure and discouraging stereotypes.
- Education gaps – Women and minorities are underrepresented in AI-related fields like computer science, engineering, and mathematics.
- Lack of inclusion – AI workplaces can have cultures that alienate diverse talent, causing high turnover.
- Implicit biases – Even well-intentioned hiring managers can have subconscious biases influencing recruiting and promotions.
These systemic issues result in a homogeneous AI expert community. Without diversity, we risk building AI that is neither equitable nor ethical.
Steps to Recruit Diverse AI Talent
To build more diverse AI teams, organizations need proactive strategies targeting the following areas:
1. Improving Candidate Pipelines
- Partner with organizations that promote STEM education among women and minorities to expand the pool of candidates.
- Offer AI scholarships/grants and mentorships aimed at students from underrepresented backgrounds.
- Host events like hackathons focused on inclusion to identify promising talent early on.
- Seek diverse interns and entry-level candidates from a wide range of academic programs to build an inclusive pipeline.
2. Mitigating Biases in Hiring
- Provide bias mitigation training for recruiters and hiring managers.
- Use clear, standardized criteria for evaluating candidates to minimize bias.
- Have diverse panels conduct interviews and technical assessments.
- Blind resumes and use skills-based assessments to reduce influence of demographics.
3. Creating Inclusive Cultures
- Ensure leadership demonstrates commitment to diversity, equity and inclusion.
- Provide employee resource groups, mentoring programs, and networking initiatives for underrepresented groups.
- Offer training on avoiding microaggressions and being an ally.
- Establish and enforce zero tolerance policies for discrimination and harassment.
4. Setting Diversity Goals
- Set specific, measurable goals for improving representation, not just vague commitments.
- Track diversity metrics across the employee lifecycle – from hiring and retention to promotions.
- Tie executive compensation to success on diversity goals.
- Report diversity data and progress regularly to stakeholders.
5. Widening Recruiting Reach
- Advertise positions beyond the “usual” tech job boards to reach diverse candidates.
- Build relationships with professional organizations for women and minority tech workers.
- Attend conferences and events serving a diverse audience.
- Engage staff from underrepresented groups to promote openings in their networks.
Implementing an Inclusive Recruitment Strategy
With those focus areas in mind, here are some best practices companies can adopt for recruiting diverse AI talent:
Attracting Candidates
- Highlight diversity and inclusion programs in job posts and on careers pages.
- Ensure job descriptions use inclusive language and don’t create unnecessary barriers.
- Advertise open roles in diverse communities and media outlets.
Reviewing Applications
- Use blind resume review to hide demographic info like name and gender.
- Leverage skills-based assessments and structured interviews to minimize bias.
- Form diverse screening committees representing different backgrounds.
Interviewing
- Provide bias mitigation training for interviewers.
- Ask the same core job-related questions to each candidate.
- Standardize the scoring process rather than relying on intuition.
- Have a diverse panel conduct interviews to bring multiple perspectives.
Selection
- Compare candidates based on the criteria outlined in the job description.
- Keep the hiring manager accountable to an inclusive process.
- Extend offers to multiple qualified candidates from underrepresented groups.
Onboarding
- Share the organization’s commitment to diversity during onboarding.
- Set new hires up with mentors that share their identity.
- Connect new employees to any existing inclusion programs or groups.
Achieving Equitable and Ethical AI
While work remains to be done, diversity in AI is beginning to gain more attention. For example:
- Congress established a national AI advisory committee requiring representatives from minority communities.
- Major AI conferences like NeurIPS now require diversity and inclusion plans.
- Research initiatives like Black in AI and LatinX in AI promote participation of underrepresented groups.
- Companies like Microsoft and IBM have diversity-specific roles focused on AI ethics.
To fully realize the benefits of diversity, organizations must commit to lasting culture change. Recruiting diverse talent is only the first step. Fostering inclusion helps retain diverse employees and creates an environment where different voices are heard and valued. While AI carries risks, diverse teams have the potential to develop AI that is ethical, unbiased, and benefits all groups in society. By recruiting people with a broad range of backgrounds, experiences, and perspectives, we can build AI that is both equitable and innovative.
Conclusion
AI has incredible potential to improve lives, but also the power to cause great harm if developed without diverse perspectives. Homogenous teams of AI experts risk building biased and discriminatory systems. Organizations have a responsibility to recruit people of different races, genders, sexual orientations, abilities, and socioeconomic backgrounds to develop ethical AI. This requires expanding pipelines, removing hiring biases, building inclusive cultures, setting diversity goals, and broadening recruiting reach. With more equitable and diverse AI development, we can create innovative systems that reflect the diversity of users and avoid perpetuating historical patterns of discrimination. The path to ethical AI begins with diverse teams.
