The advent of AI was a big deal. It’s come a long way, but it’s far from perfect.

Almost every major industry and sector already uses some sort of AI tool, but these systems still struggle with bias and diversity challenges. So, what if we could do more than just address inclusivity challenges within AI systems—and actually foster diversity in the real world?

Bias in AI

Bias in AI systems is the unfair and systematic discrimination against certain groups of people, including women and minorities.

But biases aren’t inherently part of AI. Skewed data, flawed algorithms, and human bias introduce them into the technology. Hiring algorithms that favor male candidates and facial recognition software that misidentifies people of color are just two examples of how biases affect everyday AI use. They perpetuate inequality by disproportionately affecting marginalized communities.

A recent Stanford University study showed that AI bias negatively affected non-native English speakers by flagging their work as AI-generated. Another study by Harvard found that young, black females between 18 and 30 years old had error rates up to 34% higher than lighter-skinned males. Addressing the relationship between AI and diversity is one of the most important parts of creating fair and inclusive AI models.

Diversity in tech

When tech companies promote diversity, they increase productivity, problem-solving and innovation by bringing different perspectives, backgrounds and experiences to the table. Diverse teams, like those with women leaders, people of color and those with many backgrounds, are more efficient and creative. Ultimately, this leads to better outcomes.

Studies show that companies in the top 25% for gender diversity are 21% more likely to raise and achieve higher profitability rates. Higher racial and ethnic diversity within teams also leads to 35% higher financial returns above industry medians.

Identifying and mitigating bias in AI

To use truly fair and effective AI tools, teams need to identify bias in their AI systems through algorithm and dataset audits to detect discrepancies. Starting with diverse datasets is key to reducing bias because that means more representation. That’s why transparency in AI algorithms is important so we can continuously assess and improve. Luckily, there are tools to help teams audit their AI. Google’s What-If Tool, IBM’s AI Fairness 360 and other tools help detect unfairness or bias through automated detection and fairness-aware algorithms. Using automated tools alongside human audits creates the most unbiased AI possible.

Diverse datasets

Using diverse datasets means any resulting AI algorithms will represent various populations. Creating these datasets requires collecting and curating critical information from diverse sources across multiple regions, cultures and scenarios.

Many tech companies have strict requirements to diversify their training datasets for AI technologies. Google, for instance, offers resources for responsible AI practices to help the global community use AI fairly and non-biasedly.

Transparency in AI algorithms

Keeping algorithms transparent allows for external auditing while increasing user confidence and accountability. Algorithmic transparency means sharing openly how an AI algorithm works and its data sources, decision making process and any known or potential biases.

Microsoft provides detailed insights into its AI systems through its Transparency Notes to increase user trust and mitigate bias. Meta researches how to create and distribute more diverse datasets based on the company’s core values. They also developed a new use of machine learning to distribute ads more fairly across apps.

Tools and frameworks

Many other popular tools and frameworks are available to help with bias detection and mitigation. TensorFlow’s Fairness Indicators help assess machine learning models against fairness metrics, Microsoft’s Fairlearn provides bias mitigation through visualizations and algorithms. The University of Chicago released a notable tool called Aequitas to audit machine learning models for fairness. Using these types of tools and frameworks ensures AI models are fair and transparent by promoting ethical AI and actively reducing biases.

AI for diversity case studies

Computing giant Intel uses AI in its Inclusion Index to measure in-house company culture inclusivity. By tracking and benchmarking diversity and inclusion programs the Index provides a full picture of inclusivity by collecting and analyzing datapoints like employee sentiment, promotion, recruitment and retention.

Lenovo uses AI to promote diversity and inclusion initiatives. The company emphasizes inclusive practices alongside AI algorithms to ensure not only a fair hiring process but equal opportunities for all employees. Lenovo’s Product Diversity Office works to embed diversity into the company’s product design and development work.SAP uses generative AI in its diversity and inclusion tool, SAP SuccessFactors. This system automates HR processes, improves talent management with advanced insights and provides personalized employee experiences. It also uses generative AI for learning and recruiting, talent intelligence and total workforce management.

Challenges and ethical considerations

Using AI to promote diversity is not a silver bullet and still presents some unresolved problems. Some of the biggest challenges are ensuring algorithms/systems are free of existing biases, preventing misuse and transparency in AI decision making. Balancing AI-driven decision making with human judgment is also key to mitigate unintended consequences.

To ensure ethical use of AI tools humans and technology must work together by involving diverse teams in AI design and deployment. These systems require rigorous bias detection and mitigation frameworks. Whatever the use case or application teams must foster a culture of transparency through open AI development. Continuous monitoring and accountability will help uphold ethical standards within AI applications especially in diversity and inclusion.

Ethical considerations go beyond fair use and responsible use of AI to prevent harm and promote equity. To do this, AI developers and users need comprehensive guidelines and continuous ethical training.

The future of AI and diversity in tech

The not-so-distant future of diversity and AI in tech will bring more technological advancements to promote inclusivity and diversity. This could include more sophisticated bias detection and automated correction tools. New and emerging trends include AI systems that can learn to identify and eliminate new biases in real-time and highly inclusive AI platforms that cater to diverse needs.

One major advancement and current research project is explainable AI (XAI). XAI aims to promote fairness by making the AI decision-making process not only transparent but understandable. Combining AI with intersectional approaches to diversity, equity and inclusion will also help address the unique challenges of individuals with intersecting identities and broader inclusion goals.

Conclusion

A truly revolutionary technology, AI can foster diversity and equity in the tech industry. By detecting and mitigating biases, AI helps companies promote inclusive hiring practices and fair opportunities and innovation. Many of the biggest names in tech including Intel, Lenovo and SAP already use such algorithms in their inclusivity efforts. To do this, leaders and stakeholders in the tech industry must continue to research and have the hard conversations to build a more innovative, inclusive and equitable future for technology.

FAQ

What is AI bias?

An AI bias is the output of biased results from flawed or skewed data or algorithms. Biases show up in AI systems in many ways, e.g., hiring algorithms favoring certain genders or facial recognition software misidentifying minorities.

How can AI promote diversity?

AI promotes diversity by reducing biases in hiring processes (including in job descriptions), providing inclusivity insights, equal representation and more. XStereotype’s AI-powered “insight scorecard” and SAP’s SuccessFactors are two examples of AI hiring programs.

What are the challenges of using AI to promote diversity?

Challenges of using AI to promote diversity include lack of transparency in usage, biased training data or datasets, and insufficient varied representation in training. To address these issues teams must use rigorous bias detection, inclusive datasets and transparent algorithms.

Why is diversity important in the tech industry?

Diversity in the tech industry leads to innovation, problem-solving and productivity due to different perspectives, experiences and beliefs among employees. Companies in the top quartile for ethnic and racial diversity are 35% more likely to have above average financial returns and 19% higher innovation revenue.

What are some tools to detect and mitigate bias in AI?

Tools to detect and mitigate bias in AI include Google’s What-If Tool, IBM’s AI Fairness 360 and Microsoft’s Fairlern. These tools are gaining traction in the tech industry and beyond to make things fairer.