Advancing Women of Color in STEM: Harnessing AI for a More Inclusive Future
November 2019
Advancing Women of Color in STEM: Harnessing AI for a More Inclusive Future
In today’s rapidly evolving world, the push for diversity, equity, and inclusion (DEI) in STEM fields has never been more urgent. Within the broad category of science, technology, engineering, and mathematics (STEM), women of color remain significantly underrepresented.
However, emerging technologies like artificial intelligence have the potential to not only accelerate this progress but also introduce new layers of complexity that can either help or hinder the mission of advancing diversity.
How can AI boost diversity hiring in engineering?
AI holds significant promise for promoting diversity in the hiring process. When harnessed properly, AI can help organizations reduce unconscious biases that have historically favored certain groups over others. Here are a few ways AI can boost diversity hiring in engineering and infrastructure:
Traditional resume screening can often perpetuate bias, especially when recruiters inadvertently favor candidates from similar backgrounds or educational experiences. AI tools, however, can be trained to focus on the key skills, experience and qualifications necessary for a role, rather than irrelevant factors such as gender or ethnicity.
One of the biggest challenges in attracting women of color to engineering roles is ensuring that job descriptions are inclusive. AI can be used to analyze job postings and highlight any gendered or exclusionary language that might deter candidates from applying.
AI can also assist in identifying potential leadership talent among women of color within an organization. By analyzing career progression patterns, AI-driven tools can help HR departments develop tailored career development programs for underrepresented employees, setting them on a path toward leadership roles.
How can AI further hiring bias?
Despite its potential, AI is not a cure for all diversity issues. If not designed and implemented thoughtfully, AI tools can unintentionally perpetuate the same biases they aim to eliminate.
AI systems learn by analyzing large datasets. If the data used to train AI models is inherently biased, reflecting historical inequalities, then the AI will simply reinforce those biases.
Another challenge in AI-driven hiring processes is the lack of transparency and accountability. Many AI tools operate as "black boxes," meaning their decision-making processes are not easily understood or audited. This lack of transparency makes it difficult to ensure that the AI is acting fairly and equitably, and can lead to situations where biases are being reinforced without any visible mechanism for addressing them.
Some AI algorithms evaluate candidates based on how well they fit into an organization’s existing "culture." While this can be useful in some cases, women of color, who often have different experiences, may not fit into these traditional molds, leading to their exclusion from opportunities, even if they are highly qualified.
AI can be a key tool in ensuring that women of color have the opportunity to contribute to and thrive in energy and infrastructure. However, it will take conscious effort, continuous improvement and collaboration across industries to ensure that technology serves as a force for inclusion, not exclusion.