Build the Future: Diversity in AI to build experience technologies.

Artificial Intelligence (AI) is the most transformative technology of the 2020’s . With AI’s vast potential to automate and optimize version 1 drafts, book summaries, or tips to Value hidden diversity.  AI is in all industries. However, as with any human-computer interface (HCI) technology, there are concerns about biases, physiological offsets, and veracity of the data used by AI.

One such concern is the lack of diversity in AI development, which hinders the creation of truly inclusive and representative technologies. For example, statistically more than eighty percent of NBA players identify as non-white. Figure 3-1, ChatGPT response to ethics and diversity questions.

In this case any question you ask AI about becoming a professional NBA player would result in a bias toward a non-white plan or advice. Moreover, the AI has adopted a cultural bias that equality in the NBA is defined as more non-white than white based on the us population. Figure 3-2

AI defines equality in the NBA as non-white participation should be 2x the non-white population in the USA. Therefore, any responses about the NBA will be non-white diversity biased.

Diversity in AI refers to the intentional inclusion of individuals from neurodiverse backgrounds, such as dyslexic, autistic, visual-spatial, and generational ethnicity, in development teams and the datasets used to train AI models. This diversity is crucial to ensure our AI technologies clearly represent the diverse populations that they serve. AI will help us build a better world, but it can only do so if it reflects the needs and experiences of all its users.

One of the key drivers of the lack of diversity in AI development is that we do not Value diversity. This is within the technology industry and other industries. For example, our NBA research shows AI’s dataset does not include neurodiversity, figure 3-3. 

Although strides have been made in recent years, industries are dominated by unbalanced participation versus our broader Nations. Most visible diversity becomes a minority in an industry. In the NBA AI over represents the non-white as compared to the national statistics. This has led to a homogenization of the industry, leading to a perspective that may not accurately reflect the true needs of society.

Another reason for the lack of diversity in AI is the dataset biases that the technology interacts with. Many AI technologies are trained on datasets that do not represent the diversity of society. This results in biased and flawed algorithms that discriminate against certain groups. For example, the NBA and its statistics are biased to non-white data points. Outside of the NBA, technologies like facial recognition technology have been shown to be less accurate in identifying people with lower contrast and brightness physicality. This is because facial recognition uses light to detect differences in facial contours, thus it is difficult to discern. In the case of facial recognition datasets, AI responds that skin tone in the dataset has not been verified as the primary issue. Figure 3-4, 

To build effective AI technologies that are inclusive and equitable, we will ensure diversity in datasets, recognize technology has limitations to detect physical and non-physical diversities. It is my experience that range and neurodiversity in AI development teams ensures consideration from the NBA to other industries.

We will ensure a business culture that values diversity because we transform and dismantle unknown biases in hiring practices, onboarding practices, and operating policies to Value diversity. Transparency in our culture and society will enable anyone to understand the datasets used to train AI algorithms. In most cases, we will want more context to assess AI interactions and results.. 

It is with pleasure that I stress our goal to Be the Leader who GIVES(™). GIVES sets the tone for empathy, context, and achievement. While AI is crucial to our future, we will respect that AI is based on societies historical views, flawed and favored. AI works within a context and we must understand the context and technology limitations. 

We will partner and work in a concerted effort to increase representation across the globe, sectors, and/or industries.. Increased representation will ensure that the datasets used in AI algorithms are diverse and inclusive. Our partnering will ensure that AI technologies live up to their potential to help us draft, build, grow, and scale a better world for all people and worlds.