While some may not realize it, the issues of technology and supporting human equality are closely intertwined. Since the advent of the internet and social media, connecting people across all levels of society, Artificial Intelligence (AI) has become another scientific field capable of promoting equality. This is especially relevant in an era where developers emphasize diverse thinking and sustainability.

Challenges of AI and Diversity

As AI becomes more widely used, various issues become increasingly apparent, particularly in biased data processing. The problem often originates from biased data used during AI training, which tends to represent a small and specific group of people. Consequently, when the system is applied on a broader scale, numerous groups of people may be marginalized. The biases in AI can manifest in various forms, such as:

• Facial Recognition AI : Early facial recognition AI systems faced challenges in analyzing the faces of the elderly or individuals with different skin tones. This was due to the predominantly white male data used for training.
• Criminal Justice AI : Experiments with AI systems for analyzing and deciding on criminal behavior tended to show a bias towards certain racial or ethnic groups. This bias stemmed from initial data that was not representative and unbiased.
• Natural Language Processing : Language is a key aspect that limits AI from reaching diverse groups, as it often relies on English or alphabetic characters. This limitation hinders effective use for those who cannot use English proficiently.

Developing AI to Address DEIB

As AI becomes more prevalent in people-related fields such as HR and marketing, attention must be given to Diversity, Equity, Inclusion, and Belonging (DEIB) – the four pillars of equality. Companies that prioritize and create diverse workplaces open to new ideas from various groups tend to be more profitable. According to diversio statistics in 2023, such companies experience a 43% increase in return on investment (ROI) and over 20% higher ROI.

AI’s role should be to eliminate ‘unconscious bias’ in the workplace, which creates divisive and toxic work environments. By using AI to assess employee performance, it can potentially mitigate biases related to ethnicity, skin color, or gender while measuring efficiency and the true potential of individuals.

Use Case: Uber Enhancing Workplace Equality with AI

Uber, a major transportation company operating in North America and Europe with over 3.5 million drivers, faces the challenge of analyzing the work performance of a vast workforce. To avoid biases in human analysis, Uber employs AI algorithms to assess each driver, considering factors like passenger ratings, job diversity, accident rates, and driver earnings. These factors are directly related to overall job performance and human potential.

Use Case: Diversio – AI Emphasizing Diversity

Diversio, a pioneer in Diversity, Equity, Inclusion, and Belonging within the AI industry, connects to the HR systems of various companies. Its AI analyzes employee diversity, providing easily accessible data. It then suggests actions to increase workplace diversity, using a database from over 20,000 leading companies. Major corporations like Morgan Stanley, Honda, Amazon, and others have implemented Diversio successfully.

Developers Must be Diverse and Equal

In conclusion, the perspective of AI developers is crucial in creating systems that embody DEIB principles. To enhance AI intelligence and understanding of diverse populations, developers should:

• Include Perspectives of Minority Groups : To avoid reflecting the biases of developers, AI should incorporate information from diverse sources, promoting a more varied analysis.
• Adopt User-Centric Design : Quality AI should prioritize people, not replace them, and focus on user experience (UX/UI). This inclusivity can range from reaching a larger audience to making AI accessible to individuals with varying levels of technical expertise.
• Integrate Culture into AI : Gathering practices from different cultures can lead to a better understanding of diverse populations, making AI more intelligent and socially aware.

Ultimately, it is essential not to see AI as inherently good or evil. Its impact depends on how the system is used within the correct context. Both individuals and companies must establish an inclusive culture before implementing various technologies.