Like most things in 2019, some of our old inundated business standards are being upgraded to a higher level efficiency and cultural propensity.
One of these standards is hiring qualified talent, and understanding what truly makes some “qualified.” Or rather, asks: How can we limit biases in the process in order to get the best result? That is where Genevieve Jurvetson – co-founder and CMO of Fetcher –comes in, and why I believe her most recent talk at the 2019 Elevate tech conference was one of the best.
I spoke briefly with Genevieve over coffee prior to her presentation, and here were some of the more fascinating points that we touched upon:
Inherit Diversity vs. Acquired Diversity
In today’s business landscape, the need for a diverse workforce is paramount due to our societal changes and values. However, there is quite the positive data coming out to prove that this shift is a positive one from a corporate earnings standpoint as well.
In Genevieve’s presentation, she brought up some great facts to help prove this further:
Employees of firms with two dimensional diversity are 70% likelier to report the firm captured a new market – Harvard Business Review, 2013
Companies in the top-quartile for gender diversity on executive teams were 21% more likely to outperform on profitability and 27% more likely to have superior value creation – Mckinsey, 2018
Diverse management teams produce 19% more revenue due to innovation – BCG, 2018
She also brought up the difference between Inherent Diversity and Acquired Diversity, which is quite the fascinating topic. Inherent Diversity is the factors that make up someone – such as age, gender, ethnicity, socio-economic background etc. On the other hand, Acquired Diversity is the instinctual factors that make up someone – such as mindsets, beliefs, and experiences to name a few. What makes this topic so fascinating is that a generalized viewpoint of what makes up diversity, is not truly ‘diverse’ in nature.
In our society today, diversity is mass generalized as inherent. When in reality, it is both inherent and acquired. This can lead to a very narrow viewpoint as to how to approach such an issue, but also begs the question as to which type of diversity is more important to building an effective team – or corporate culture. In Genevieve’s approach at Fetcher, the answer is both; but the aggregated combination might be different based off of corporate need, wants, and of all things, diverse make up of the business. This is why proper recruitment of talent is imperative to corporate success, as this truly is the backbone of all successful ventures moving forward in our progressive social climate.
Biases, and what we can do about them
From speaking with Genevieve – and listening to her presentation – she brought up the immediate fact about unconscious biases. Specifically, how these biases effect the way we think and act; and how our final actions (and decisions) could be quite different if they were muted. In her Elevate presentation, Genevieve went more in-depth on three specific biases she encounters daily in the recruiting industry: Affinity Bias, Halo Bias, and Confirmation Bias.
Affinity Bias is essentially our ability to be drawn towards people who look/act/talk similar to ourselves – which if not addressed, can be quite the hindrance for corporate growth. And in reality, this is very true. Limiting one’s comfort zone to those that you have the most in-common with, can be quite restrictive from an idea creation standpoint. A good example of this in the business world would be the inverse dynamic of an outside-in (consumer viewpoint) vs. inside-out (industry focus) approach to problem solving.
Halo Bias effects one’s overall impression of someone, based off of their specific traits. Some refer to this as the “Ivy League Bias”, as we have the natural inference that those who attend Ivy League schools are more qualified for a specific task; based off of their knowledge background. While in some cases this might be true, a mass generalization of this can lead to an array of issues on a corporate talent acquisition level.
Confirmation Bias is – at its basic level – personal snap judgements about someone, and the search for signs and signals to confirm said judgements. A great example: If someone believes that left-handed people are actually more creative than right-handed people. If that person ends up meeting some people that are both left-handed and creative, they will use this as ‘proof’ to essentially build up their judgement; while looking for other examples to dismiss the latter (right-handed, creative people).
In most situations, this is very limiting from a data gathering standpoint; and also weighs heavily on our personal influences. And in terms of building up a strong diverse workforce, this can hamper true development on this front.
AI is a means to an end, not an end to our means
A natural question to the biases above would be how do we combat these from effecting our processes? Well, in Fetcher’s case, this is through the use of AI automation in the recruiting industry. During our chat, Genevieve stated that by effectively taking the human biases out of the initial screening process, we can create a more effective outreach program; that is also produces higher quality returns in terms of productive talent.
Having said that, the issue arises in terms of what the AI is programmed to do – and Genevieve isn’t remised of this fact. In her presentation, she referenced a Mckinsey report on AI automation in which it stated that:
“AI can help reduce bias, but it can also bake in & scale bias”
This is where our understanding of what we are looking for – from a talent gathering perspective – has to be as specific as possible, while also using “good” sources of data to help the algorithm work more effectively. For example, if I were to use biased data points from prior works to help with my talent search, wouldn’t the already biased data points be amplified by the AI algorithm in terms of its outreach? The answer is yes, and this is one of the major issues today, with major players jumping into AI automation in haste, without an overall perspective approach.
