In your latest book, you propose a paradigm shift to make work fair. What does this entail in practical terms?

Anyone can make work more fair by improving how they do things they are already doing daily in their job. If you hire people, employ practices that have been proven to give everyone a fair shot. If you create presentations, ensure the images you select are representative of your employees, customers and the communities you serve. And if you chair a meeting, run it so that you can benefit from everyone’s wisdom.

Making work fair means designing workplaces where everyone can thrive and perform at their best. This means giving all people — regardless of gender, race, ethnicity, sexual orientation or any other aspect of their identity or background — access to a playing field where some people are not unfairly advantaged in a way they didn’t earn. Very few people are against fairness, but we don’t all agree on what it entails. My definition is simple: true equal access and opportunity to thrive.

Individuals can take steps to make work more fair for themselves, as well. How?

Think of the typical résumé. Normally, you describe your work experience and include the dates that you have held various positions over the years. It might say, “from March 2017 through February 2019, I was assistant manager of operations at Company X.” Then, there might be a noticeable career break, before the dates indicate that the role resumed from January 2022 through December 2024. We don’t think much about the effect of such gaps, but my colleagues Oliver Hauser and Ariella Kristal and their co-authors tested the impact of describing one’s work experience this way.

They ran an experiment with about 9,000 companies in the UK where they sent out résumés for the same person in two different ways — with and without dates indicating a career break. For the second version, instead of including dates, they focused on the number of years or months that someone had been in a particular job. So it might have said, “assistant manager operations, two years;” and then “manager operations, three years.

Their question was, what impact would this have on whether the individual was contacted for an interview?

The result: not including specific dates increased the chances of being contacted for everyone — women and men. So, with the current tendency to include dates, men are likely to benefit because they tend to have fewer career breaks. If the simple practice of not including dates of employment became widespread, we would move toward equalizing the playing field.

In your view, why are diversity, equity and inclusion (DEI) initiatives experiencing so much backlash of late?

There are multiple reasons. Particularly in the U.S., people differ in their appreciation for equity, and we just have to face that. Not everyone values equal opportunity to the same degree, and we’re experiencing backlash from those people and the organizations they lead. Having said that, I also think those of us who care deeply about equity have to ask ourselves whether the things we’ve been doing for the past few years have actually worked. Based on the evidence, they have not. That is another reason for the backlash, because the returns on investment are just not there.

To give you an example, when I wrote my first book back in 2016, I discussed evidence that diversity training might be able to raise some awareness, but that there was no evidence that it would change long-term behaviours. And that is what we’re after here. We want people to behave differently toward each other and to work differently.

After the murder of George Floyd and the other horrible things that came to the fore in 2020, many leaders made big statements. Promises were made, training was introduced and events were held, but without any real measurement of what works and what doesn’t. Collectively, we’ve spent a lot of money on things that haven’t worked.

Nevertheless, you believe that making work fair for everyone can still be a game-changing opportunity for organizations. How so?

We need to stop doing the wrong things. That’s the key takeaway here. I’m very concerned about the backlash we’re seeing and I’m nervous that organizations will just throw their hands in the air, thinking there’s nothing they can do to create equal opportunities. That mindset is going to cost these organizations dearly, because they won’t be able to benefit from 100 per cent of the talent pool.

The second issue is, they won’t be able to access new markets. To give you an example, when Rihanna launched her beauty line, Fenty, a few years ago, she immediately offered 40 different shades of foundation. Today, there are more than 50. As you might imagine, this resonated deeply with people with different skin tones. That’s how she accessed a new market. If you don’t think about 100 per cent of your potential customers, you will never come up with a product like that. The third reason — which is the most important one for me — is that making work fair is just the right thing to do.

Apart from Fenty, are there other companies that are doing this really well?

I don’t think anyone has completely figured it out, but there are bright spots where learning organizations are trying things and improving. One example is Harvey Mudd College, a STEM [Science, Technology, Engineering, Mathematics] college in California. Maria Clave — a former Princeton Physics professor — became president of the college in 2006. When she joined, it had only 10 per cent female students in its computer science program. Under her leadership, by 2018 they reached 56 per cent, and they are roughly at an equal share now. Their goal was both simple and fair: to have the student body represent the world we live in. She and her team asked, “Why are young women choosing not to study CS?” That approach was key. Whenever I work with any organization, we start with the diagnosis: “Why is X not happening?”

Her team found a number of reasons for it. One was that the substance of the curriculum didn’t resonate with female students. They were more interested in understanding the why — why does it matter? — and also the how — how could I use this tool, for example, to improve healthcare? That was the first important insight. They needed to redesign the courses to show the breadth of what you can actually do with the tools.

Another key reason for low enrollment was the lack of female role models. So, they also focused on diversifying their faculty and sent students to conferences such as the Grace Hopper Celebration of Women in Computing, which focuses on women in STEM, so they would be exposed to many role models.

In the book, you debunk seven myths about workplace fairness, the first of which is that "we need to de-bias individuals." Why is that a myth?

It is actually incredibly difficult to de-bias an individual. In Behavioural Science, we’ve had limited success trying to change mindsets. More than 200 biases have been identified, and we can definitely make things a bit better by raising awareness about the common ones, but it’s not going to move the needle in terms of unfair behaviour. That’s why our book is about changing behaviours, not attitudes. The approach we use, behavioural design, is rooted in insights into how our minds work.

What we can do is de-bias the organizational processes, environments and policies that we have created and that contain built-in bias. Consider the commercial facial recognition software that can accurately identify the gender of a white man in a photo, getting it wrong only one per cent of the time, but that fails to recognize darker-skinned women up to 35 per cent of the time. This software was built and tested by mostly white, male engineers at several of the world’s leading tech companies — but it can be reprogrammed to work better for all humans. Or think of the policy that gives women 26 times more parental leave than men to take care of their child, reinforcing gender stereotypes of women as carers and men as earners. That policy was created by legislators in the UK — but it, too, can be changed to give everyone an equal opportunity to contribute both at work and at home.

How do you see AI fitting into this already massive challenge? Do you think it will help or hinder it?

The truth is, I don’t know — but I do believe we all need to understand the benefits and risks of AI. There are clear benefits. If you have, say, a screening algorithm looking at incoming résumés, you can add a step to examine the results. You can look at who is making it through, based on, as discussed earlier, whether or not they have career gaps. Of course, you can look at gender, race and other information.

And then, if you notice something is off — you can fix it. This could be transformational. It means we can actually change the outcome for many people overnight — if we want to.

One big downside is that AI is, by definition, backward looking because it is built on data that has been generated over many years, often in the Northern Western Hemisphere. This means we can easily make the mistake of generalizing to other parts of the world, where cultural norms are different, and if things weren’t equal in the past, they won’t be equal in the future. The fact is, any time you get an algorithm wrong, it can impact millions of people in a negative way. So the stakes are very high for getting it right.

We need to do a much better job at testing algorithms before they are unleashed. The City of New York passed a law recently mandating that screening algorithms used in people decisions must be tested before they are shared with the world. I do think that is the way to go, because as indicated, algorithms have the potential to make things better for people. They don’t fall prey to some of the issues that human minds do.

For example, we know that ‘order effects’ are important when human screeners are looking at résumés: The first one gets much more scrutiny than the 125th, when the screener is totally exhausted. But algorithms can look at 125 — or 5,000 — files in the exact same way. AI definitely offers many advantages, but we need to test it beforehand to make sure the results are predictive of what we actually want to see happen in the world.

You use a running analogy to define what we need to do in the workplace. Please share it.

Although empowering women (and all people) to reach their full potential is certainly a goal we endorse, such empowerment can only go so far. Giving a sprinter a better pair of sneakers might make them marginally faster — but if their starting line is, say, 20 metres behind their competitor’s, they will still finish second.

Efforts to fix individuals are akin to giving them better sneakers without considering the starting line. We will be better off moving everyone to the same starting line and allowing them to compete fairly. In short, change the playing field, not the players.

This article originally appeared in the Spring 2025 issue of Rotman Management magazine. If you enjoyed this article, consider subscribing to the magazine or to the Rotman Insights Hub bi-weekly newsletter.