The MEI vs DEI debate: Is this a step back for inclusion

‘Scale is a meritocracy and we must always remain one.’ 

Tech entrepreneur Alexandr Wang posted on X last week saying his company Scale AI was replacing DEI (diversity, equity, inclusion) with ‘MEI.’ 

MEI, Wang added, stands for merit, excellence, intelligence. 

‘Talent is our #1 input metric,’ the founder said. 

Unsurprisingly, Tesla CEO Elon Musk applauded Wang on the move. “Great!” Musk responded, in a reply to Wang. 

Musk wasn’t alone in the list of company heads congratulating Wang on the move; Sequoia partner Shaun Maguire and Coinbase CEO Brian Armstrong hopped onto the appreciation bandwagon as well. 

Musk and plenty other leaders have been more than vocal about their dislike for DEI. In a post on X earlier, Musk had said: “DEI is just another word for racism. Shame on anyone who uses it.” 

Alarmingly, there’s a growing community of people actively in support of MEI over DEI. 

What this community gets wrong is that DEI does not mean prioritising diversity while ignoring other qualifications. Ironically, Wang’s post ends up lending credence to the very cornerstone of DEI hiring. 

“There are a lot of things in this post that are actually, perhaps surprisingly, aligned with the goals of a lot of DEI practitioners,” said Natalie Johnson, cofounder and managing director of strategy at DEI consulting firm Paradigm, in this story on Fortune. “I think for many, many years now, we have failed to recognize that and have failed to make that connection that, oftentimes, we have the same values, the same principles, that we’re building off of.” 

Speaking of diversity, in the AI industry alone, women make up just 30% of global roles. This is especially concerning considering how many Large Learning Models (LLMs) have biases in-built because of the lack of diversity within the teams working on them. 

Ranking high among AI’s biases is gender bias. The Artificial Intelligence and Gender Equality report by UN Women has identified a clear gender gap in access to the Internet, which manifests in the gender bias in AI. 

This study by the Berkeley Haas Center for Equity, Gender and Leadership analysed 133 AI systems across different industries and found that 44 per cent of them showed gender bias and 25 per cent both gender and racial bias. 

If AI is trained on data that is biassed, a natural consequence is it will learn and internalise that bias and incorporate it in the results it throws up. 

And gender bias is not the only kind AI perpetuates. Recently AI also came under fire for discriminating against people with disabilities by ranking CVs of disabled people lower than able-bodied people. 

DEI policies are put in place not only to ensure the right representation among the workforce but also to make sure every voice is heard, every person has a level playing field, and decision-making is fair and just. 

As Lisa Simon, chief economist at analytics platform Revelio Labs rightly puts it: “...as soon as you remove (DEI policies), people go back to hiring people that look like them.” 

Bias and discrimination are the prime reasons DEI came into being in the 1960s. Since then, companies have made significant strides in diversifying their workforce through internal policy reforms and structural changes in their hiring & talent development processes. 

Lots of data has emerged over time that shows how effective DEI boosts innovation, leads to higher revenues and results in greater employee satisfaction. 

There is also ample data to show that a large percentage of employees who are dissatisfied by ‘non-inclusive workplaces’ plan to quit within 12 months. 

And that is the fundamental issue with placing MEI over DEI - a less diverse team means a more homogenous outlook of the world, which severely limits the potential of both artificial intelligence as well as human teams, both of which are crucial in ensuring organisational success in today’s day and age. 

Not to mention rallying against DEI invisibilises the lived experiences of people who have been systemically marginalised, and denies them the opportunity to present their perspectives to the world. 

It ensures that systemic oppression is never weeded out. 

If the pushback against DEI goes on, AI-powered technology and services will evolve to be severely deficient of diverse perspectives. The teams in our workplaces will homogenise and innovation might come to a complete standstill. 

The gap will consequently result in a lower quality of services as well as biassed decisions about jobs, credit, health care and more. 

What are your thoughts about the DEI vs MEI debate?