How PushFar’s Mentoring Platform is Removing Unconscious Bias

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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 algorithms and matching processes are removing unconscious bias and opening mentoring to more and more participants, every day.

Matching mentors to mentees has long been carried out by learning and development managers, community managers and HR directors, in 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.

The Problems with Manual Matching

There are a number of significant problems with manually matching participants in mentoring relationships. One, as outlined briefly above, is the work required in order to do this. 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.

Unconscious bias is a significant problem with manual matching. For those of you who haven’t come across unconscious bias before, it is described as a bias 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 biased behind the matches.

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

Mentoring Software to Level the Playing Field

So, how can we remove unconscious biases from mentor matching? Well, PushFar’s mentoring software has a number of significant features built-in to ensure that there is both equity and equality factored in to matches. First and foremost, our default algorithm will never look 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.

Secondly, the matching algorithm will look at key focus areas of mentoring. 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 Industry Insights and Negotiation Skills. 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.

By no longer giving the matching control to one administrator or panel of administrators, and instead giving the control over to individuals, mentoring is opened to helping individuals to find the right match for them, and/or allowing administrators to automate the whole process, with the knowledge that there is no conscious or unconscious bias involved, other than our equity and equality mentoring algorithms.

Who Are the Best Matches?

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. However, 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 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 mentor matching, PushFar’s mentoring platform is a great place to start. Click here to request a demo today.