Using Artificial Intelligence (AI) in Diversity, Equity, and Inclusion (DEI) initiatives is now more about “what now” than “what if”. Though not a recent idea, AI has caused a significant change in the field of employment. Recent events have illustrated that our governmental, non-profit, and for-profit organisations still need to be prepared to allocate dedicated human resources to DEI initiatives.
As per a McKinsey report, artificial intelligence refers to a machine’s capacity to carry out cognitive tasks typically linked with human intelligence, while generative AI includes algorithms like ChatGPT that can generate various forms of content such as audio, code, images, text, simulations, and videos.
DEI’s Quick Ascent and Descent in Organisational Importance
In the last four years, we have witnessed a regrettable decline in the prioritisation of DEI initiatives. In a survey conducted by McLean & Company, global organisations ranked DEI as the seventh top priority for HR organisations in 2024, down from fourth in 2021.
However, we are aware that DEI is still important. The effectiveness and importance of DEI efforts have been consistently demonstrated, as organisations that focus on diversity and inclusion outperform those that do not. Although numerous studies show the benefits of focusing on DEI, these challenges persist and have yet to be tackled.
Although some organisations have advanced in this area, most still have a significant distance to cover to achieve their inclusivity objectives, such as pay parity, diverse leadership, and other goals.
Why is the priority of DEI decreasing? A positive response is that companies are integrating it into their daily operations. This would reduce its status as an individual priority since it has become ingrained in the culture. This is true for certain organisations.
However, a more negative possibility could be true for many people: decreasing dedication from leaders and concerns about potential repercussions in a more chaotic setting are directing them to prioritise less in DEI.
How AI Promises to Enhance DEI Initiatives?
Generative AI in HR has the great potential to transform DEI initiatives in different fields. This is how AI and DEI come together to create impactful transformations.
1. Revealing Prejudice in the Hiring Process
Conventional recruitment procedures may contain subconscious prejudice, influencing choices influenced by elements such as race, gender, or age. AI-powered recruitment tools can even out the competition by anonymising applications and using data-driven criteria to identify the top candidates.
This could enable the screening of resumes solely based on skills and experience, rather than names or photos, guaranteeing a just and impartial selection process.
2. Strategies for DEI based on Data
AI can examine large data sets to pinpoint patterns and discrepancies within DEI initiatives. Organisations can use these insights to develop better policies and initiatives that meet the unique needs of their employees and local community.
3. Tailored Learning for Everyone
AI has the potential to customise educational materials and training courses according to each person’s requirements. AI systems can tailor learning pathways based on individual learning styles and progress, providing equal opportunities for success regardless of background. This improves learning results and ensures each learner feels appreciated and cared for.
Challenges to Consider in AI and DEI
Even though AI presents promising opportunities, implementing it for DEI efforts is not without difficulties.
1. Lack of Interaction between people
Effective diversity, equity, and inclusion training is founded on intricate societal and cultural dynamics. During in-person training sessions, DEI trainers observe and address the responses and feelings of participants in the classroom.
They understand the specific details of the local area and connect with students’ various backgrounds by showing empathy, and curiosity, and engaging in open conversations. It goes further than just human interaction, delving into behaviour, context, and organisational maturity that AI systems might find challenging.
2. Mistranslated Interpretation
AI translation systems have advanced greatly, but they still face challenges in accurately understanding nuances in language, cultural references, and sensitivities. In certain languages, such as Japanese, there are words in the DEI vocabulary that do not have direct translations, like inclusion.
Certain Dutch terms offend German speakers due to the different meanings of some words in various languages. While AI translations can provide a fundamental understanding of the material, they may not consistently convey the desired message or convey it in a culturally suitable manner.
3. Programming bias
AI algorithms are only as unbiased as the data they are trained on. Currently, tools such as ChatGPT and Google Bard receive data from various online resources including public and private organisations, government reports, research publications, surveys, and studies. This means that, although this encompasses a wide range of diverse viewpoints, it can also include biased, discriminatory, or offensive content.
What Does the Expert Say?
Partly to blame is historical precedence. AI-supported recruiting showed anti-Black bias, according to a 2019 Harvard Business Review research, while a major online retailer’s hiring algorithm was shown to discriminate against women in 2018.
Although it’s too soon to tell how well the current batch of AI models will perform, experts warn that the spectrum of human prejudice included in the data used to train the AI has historically led to misalignments. Chief Diversity Officer at Korn Ferry JT Saunders states, “By having a bias in the system, you get biased results.”
According to Saunders, the problem arises from the engineers who create AI consistently needing more diversity. According to him, “We reinforce systemic biases based on those who built them.” In fact, according to reports from last year, women made up just 2 out of 10 software engineers in the US, and less than 1 out of 10 coders identified as Black or Latinx.
In addition to suggesting more inclusive employment practices, Saunders is the latest in academics and corporate executives to advocate for a pause in AI research until the risks are fully understood. “Your scientists were so preoccupied with whether they could, they didn’t stop to think if they should,” as the renowned Jurassic Park character Malcolm cautioned.
Strategies for Building Inclusivity with DEI and AI
Using a proactive and inclusive strategy is crucial for maximising the benefits of AI and reducing its potential risks. Below are a few tactics:
✅ It is essential to ensure that AI systems are trained on varied datasets that contain a broad spectrum of experiences and viewpoints. This aids in reducing bias and enhancing the equity of AI results.
✅ Forming teams with various backgrounds to create AI systems can aid in detecting and resolving potential biases in the early stages of HR software development. Viewpoints can result in increased creative and fair solutions, advancing AI and DEI.
✅ It is crucial to create explicit ethical guidelines and standards for the development and deployment of AI. These frameworks must place importance on equity, openness, and responsibility to ensure AI technologies adhere to DEI principles.
Conclusion
AI will exceed expectations if we can balance automation and diversity, equity, and inclusion (DEI). People will steer clear of systems that support bias and discrimination, or that lead to further divisions and disparities in society. While AI will transform the work environment, it will do so in a way that makes DEI a crucial foundation and essential requirement for companies seeking long-term growth.
Businesses should focus on developing and constructing AI systems that empower all users, whether direct or indirect, to achieve more enjoyable, fulfilling, and impactful lifestyles. Ultimately, only an AI revolution that prioritises people will achieve success.
