How PushFar’s Mentor Matching Algorithm is Removing Unconscious Bias

Supporting Inclusive and Scalable Mentoring at PushFar

At PushFar, we’re fortunate enough to support hundreds of organisations and tens of thousands of mentors and mentees along the way in their mentoring journey.

With more and more mentoring programs shifting towards matching and automation, we explore how our mentor matching algorithms are removing unconscious bias and opening mentoring to more and more participants, every day.

Why Manual Matching Can Be Problematic

Matching mentors to mentees has long been carried out by learning and development managers, community managers and HR directors. This process takes place across organisations and their mentoring programs, both external and internal.

In the last few years, with more organisations looking at scaling their mentoring offering to more employees and participants, manual mentor matching has become harder and more labour intensive. Technology has become a crucial support mechanism and with the advent of mentoring software, like PushFar, the matching has been far more seamless.

What are the Disadvantages of Manual Matching?

Manual mentor matching has been the norm for many organisations, but as mentoring programmes grow, the cracks in this approach become more evident. Let’s explore the key issues that arise when mentor-mentee pairings are managed manually:

It’s Time-Consuming and Hard to Scale

One of the most obvious disadvantages is the amount of work required. When thinking about a program with 20 participants, it seems pretty straightforward but expand it to 50 participants and the workload becomes a little more complex.

Now, consider an organisation offering mentoring to 500 or 1,000 employees – that is a lot of application forms to review. Several of our clients have tens of thousands of employees, such as Savills, UiPath and Zain. Without software, not only is it extremely resource-heavy but there are other issues that begin to occur too.

It Introduces Unconscious Bias

Another major concern is unconscious bias. For those of you who haven’t come across unconscious bias before, it is described as a bias or prejudice in favour of, or against, one thing, person, or group compared with another—usually in a way that’s considered to be unfair.

Unconscious biases are most frequently associated with social stereotypes about certain groups of people that individuals form outside their own conscious awareness. In the case of mentor matching, we may well see two participants matched with unconscious bias behind the matches.

It Can Feel Invasive for Participants

The final problem with manual matching is that it often requires participants to share details about their mentoring requirements and goals with a program manager. This can be off-putting to have to put your name forward, knowing it will be read by the program manager and judged accordingly.

Some may hesitate to share their true ambitions or development needs, limiting the effectiveness and inclusivity of the programme from the outset.

How Does PushFar’s Mentor Matching Algorithm Set a Level Playing Field?

So, how can we remove unconscious biases from mentor matching?

PushFar’s mentoring software has a number of significant features built-in to ensure that there is both equity and equality factored into matches.

No Demographic Data by Default

One of the most powerful features of our Mentor Matching Algorithm is what it doesn’t consider. By default, our platform never looks at personality traits or characteristics such as gender, sexuality or age. In fact, we don’t even ask for that information by default.

The reason we say ‘by default’ is that each of our clients can, of course, add these questions in, should they wish to. In some cases, these become far more relevant. Our matching algorithm first looks at those available mentors and mentees, screening out those without capacity.

Matching Based on Skills and Focus Areas

Instead of relying on personal characteristics, our algorithm focuses on what really matters: mentoring goals.

An example of this could be a mentee signing up looking for mentoring in Industry Insights or Negotiation Skills. We then look for mentors who have said that they would be happy to help mentor people in those areas.

Naturally, there are likely to be several mentors available here, so we always then randomise the suggested matches to ensure that each participant is seen and visible equally as a suggested match.

Empowering Individuals and Reducing Bias

Crucially, we take the match decision out of the hands of a single administrator or panel. This reduces the risk of both conscious and unconscious bias and gives participants more autonomy.

Whether individuals are choosing their own mentors or administrators are using automation, the process is transparent and equitable. This is made possible by our purpose-built Mentor Matching Algorithm.

What Makes a Good Match?

A question we’re often asked is whether matches who are similar or different are the best. It’s a tough one to answer, and a survey that we recently ran with more than 500 professionals proactively engaged in mentoring at PushFar highlights that.

• 63% of participants said that finding a mentor from a similar background was one of two of the most important things they look for (out of four options), with 25% saying it was the most important thing.
• 36% of respondents said that someone with a different background was one of the two most important things, with 13% saying it was the most important thing.

The fact is that different people want different things. Some people do feel naturally more comfortable being mentored by others they identify with, yet we know that those mentored by someone from a different background are probably, in most cases, more likely to learn more and have more to offer one another.

The Bottom Line

The reality is that mentor matching is always a tricky thing and there are lots of considerations to make. Yet, if you want to remove unconscious bias with a reliable, inclusive and scalable mentor matching algorithm, PushFar’s mentoring platform is a great place to start.

Request a demo today.