Over the past two years, AI technology has rapidly advanced, with ChatGPT leading the way. This has resulted in a surge of generative AI applications and tools, known as the “Cambrian explosion.” While the potential of this technology to positively impact our lives is undeniable, there is also a growing concern about the presence of pervasive bias within these models.
AI has transitioned from supporting everyday tasks like ordering rideshares and making product recommendations to more significant activities such as arbitrating insurance, housing, credit, and welfare claims. Previously, bias in these models may have been seen as annoying or humorous, such as recommending glue to make cheese stick to a pizza. However, when these biased models become the gatekeepers for crucial services that affect our livelihoods, the bias is no longer defensible.
The question arises: how can we proactively address AI bias and develop less harmful models if the data used to train them is inherently biased? Is it even possible when those who create the models lack awareness of bias and its unintended consequences in all its forms?
The solution lies in increasing diversity within the AI talent pool. By having more women, minorities, seniors, and individuals from different backgrounds involved in AI, we can work towards mitigating bias. However, achieving this diversity requires comprehensive strategies that make STEM fields more attractive and accessible to underrepresented groups. This effort should start in the classroom, with early education and exposure to STEM for all students.
Currently, there is a significant lack of diversity within the STEM workforce. Women make up less than a third of all STEM workers worldwide, while black professionals in math and computer science account for only 9%. These statistics have remained relatively stagnant for the past 20 years. To address this issue, it is crucial to create equal opportunities for exploration and exposure to STEM subjects. Non-profit organizations like Data Science for All and the Mark Cuban Foundation’s AI bootcamps play a vital role in providing additional resources and support to underrepresented groups.
Representation plays a crucial role in shaping perception. By celebrating and amplifying the stories of women who have made significant contributions to the field of AI, we can inspire and motivate young girls to pursue STEM careers. CEOs like Lisa Su, CTOs like Mira Murati, and pioneers like Joy Buolamwini serve as role models for young girls, showing that women can excel in the STEM field.
It is essential to recognize and acknowledge the existence of bias to effectively mitigate it. Bias can infiltrate AI models through the vast data sets on which they are trained and through the personal logic or judgments of the model creators. For example, popular image generators like MidJourney and DALL-E showcased a lack of representation in body types, cultural features, and skin tones when asked to depict a “beautiful woman.” This highlights the need for more diverse perspectives and inputs during the model creation process.
However, bias can also be more subtle. For instance, if a model is trained on historical credit data, it may not accurately represent women in some regions due to cultural or legal barriers that limit their access to credit. Maternity leave and childcare responsibilities can also create gaps in employment or credit history. To address these issues, developers and data professionals need to be aware of potential discrepancies and find ways to compensate for them, such as using synthetic data enabled by gen AI.
To ensure a diverse range of voices is involved in creating, training, and overseeing AI models, it is crucial to have a diverse representation of women in the field. This cannot be left to happenstance or be solely dependent on the moral and ethical standards of a select group of technologists. With more diversity, we can create more accurate and inclusive models that benefit everyone.
Although it may be challenging to completely eliminate bias from AI innovation, we must not ignore the issue. Taking action to increase diversity in STEM fields and involving diverse talent in the AI process is crucial for producing more equitable and fair AI models. By doing so, we can work towards a future where AI technology reflects the diversity and richness of our global population.
In conclusion, addressing AI bias requires a multifaceted approach that includes increasing diversity in the AI talent pool, starting with early education and exposure to STEM subjects. It also requires acknowledging the presence of bias and actively working to mitigate it by involving a diverse range of perspectives in the model creation process. By taking these steps, we can strive for more accurate, inclusive AI models that have a positive impact on society as a whole.
Source link
