How Implicit Biases Compound for Aspiring Executives
Affinity bias, confirmation bias, and negativity bias build upon each other. Minute, almost imperceptible differences add up over years and make the difference between Director and C-level executive.
(Teddy the Corgi; Orinda, California)
The Trifecta of Affinity, Confirmation, and Negativity Biases
The three types of bias that stand out in terms of the highest impact on minorities trying to advance their career are affinity bias, confirmation bias, and negativity bias. These three combined create a self-fulfilling loop that promotes more of the in-group (e.g., white males with roughly 80 percent representation) and further excludes the out-group (e.g., non-white males or females).
Affinity bias is the very human tendency to trust and favor people whom you view as more similar, often based on common experiences, backgrounds, or interests. Affinity bias leads executives to give their “mini-me” more opportunities, less critical feedback, or more benefit of the doubt when it comes to mistakes.
It was in my first job out of college that I saw the affinity bias based on gender and race at play in the workplace at play in the workplace. I landed a business analyst role at McKinsey & Company, where I was the only person not from an Ivy League school to be accepted that year. Shortly after starting, I noticed that although half of my interviewers had been women, most of the partners at the firm were white men. Often, a white male partner would ask a white male associate to take on a part of the presentation and introduce them to the clients. They would go off and plan for future meetings, leaving the rest of the team to do follow-up work. Over time, I noticed that non-minority managers were more frequently given opportunities to work closely with partners and that the senior non-white-male managers would have to explicitly ask for similar opportunities. This is what affinity bias looks like.
The second is confirmation bias, which is the tendency to interpret new evidence as confirmation of one’s existing beliefs or theories. This means that when we receive new information that does not fit our current beliefs, rather than altering our beliefs to incorporate the new information, we do the opposite. We alternate our interpretation of the information to fit our beliefs. This is why two very smart people looking at the same set of data or observing the same set of events can come to two very different conclusions. From a career perspective, this shows up in many different places. When hiring, a CEO may interpret interview results in a way that confirms their preexisting beliefs (subject to affinity bias) about a candidate. During performance reviews, managers may subconsciously inflate the performance rating of those who are more like them or be more willing to overlook mistakes. In decision-making, leaders may prefer to hear, and pay more attention to, information or perspectives that match their own. It’s easy to see how this leads to a disadvantage for minorities, who are more likely to have a different perspective and a different approach than their (usually) white male leaders.
Third is negativity bias. This is where executives over-index on negative actions of people who are unlike them and more readily perceive a mistake as a repeat pattern. For example, I would often receive feedback for being “intimidating” or “aggressive” if I spoke harshly in just one meeting, while my male peers were considered “persistent” and “confident” in similar scenarios. If I missed a launch date once, it was considered a pattern of missing timelines; whereas, for my white male peer, it was viewed as an exception for someone who generally delivered on time. This tendency to over-index on negative action was the reason one small failure of the team would damage my performance review but not that of my white male peer.
Implicit Bias Shows Up in Actions Not Taken
Sometimes bias is explicit and easy to recognize, but it also often presents itself in actions not taken or skills not taught. It may not be an action someone takes against you or something someone tells you directly. Rather, it shows up as opportunities given to someone else, an implicit discount on your work impact, or a lack of progress over time. Rarely will someone tell you that they gave an assignment to another person because he is a white male. This makes the bias more difficult to identify for both the person with the bias and the recipient.
Here is a situation I have seen and heard about often: There’s a large group meeting, and the organizer, a white male, is trying to limit the number of attendees. He leaves a minority manager off the invite to “save her time.” At the same time, he invites another white male manager who is his friend and is a go-getter, because he knows his friend would appreciate being in the room. The minority manager will likely not notice that she was left out of the meeting. If she does notice, the organizer could easily explain it away by saying that they had to keep the invite list to the most relevant people. This decision of whom to invite took probably 10 seconds and didn’t even truly register in the organizer’s mind as a bias. However, these types of reflexive biases add up over a person’s career to meaningful differences in opportunity and rate of promotion.
I experienced this unintentional aggregation of implicit biases firsthand after business school. I joined as a product manager at Thumbtack, a 12-person Sequoia Capital–backed startup working out of a garage in San Francisco’s Mission District. Coming from the business school bubble, I was unprepared to walk into an office of almost all white men on my first day. The space was small, and everyone sat in an open floor plan. Everyone there, except the office manager who greeted me, was a man. I had heard stories about the male-dominated startups in the valley, but this was the first time I experienced it.
When the company was small, the culture felt closer to a meritocracy. However, as the team grew, it became more and more opaque as to why certain people were promoted or let go and how key company decisions were made. While the leaders made solid efforts to promote diversity in the company, most of the senior leadership were white men. I had proved consistently that I could ship products that impacted business metrics, growing our suppliers in the marketplace more than tenfold. The team grew from 12 to 100 people and raised hundreds of millions in venture funding. I wanted to be (and felt that I deserved to be) one of the senior leaders at the company.
Unfortunately, no matter how explicit I was about my career goal to be a VP, I was stuck at the “head of” level. It was much later that I realized I had let a combination of affinity bias, confirmation bias, and negativity bias hinder my leadership trajectory. Often, my work and impact were more readily discounted, attributed to the task being “easier than expected” or “overall company growth.” Not only did Thumbtack’s leadership not recognize the implicit biases, but I also didn’t fully recognize them myself. We all let its effect compound over time. It took working with an external career coach to help me realize the tremendous value I had brought to the company and learn how to articulate and advocate for recognition of my impact. Hopefully, the examples from my experience in this book can help future aspiring leaders to learn from my mistakes, recognize and tackle bias early, and get ahead in their careers.
Small Biases Compound
Schelling’s model of segregation, developed in 1971, analyzes the creation of racially segregated neighborhoods. It shows that very minor preferences for same-group people in a neighborhood can lead in the aggregate to distinct segregation over time.[i] For example, even though white people in a neighborhood prefer some level of diversity with Black people at a roughly 80/20 split, the lack of desire for a 50/50 split leads a neighborhood to become polarized over time.[ii]
Similarly, small, often implicit biases can lead to an executive team that lacks any diversity. In most cases, no one is intentionally trying to hold minorities back. Most people hiring executives are simply looking for the best person for the role and the business. However, the further you climb up the career ladder, the more competitive it becomes. At the director level and above, most of your peers are well-spoken, intelligent, and ambitious. This high level of competition means that tiny biases that were previously easy to ignore now have an outsize impact on who can break through and get promoted.
Interestingly, hiring at the senior levels also tends to be more subjective, further allowing bias to influence the race for a heavily sought-after role. The more subjective hiring decisions are often masked as looking for cultural fit. But in practice, it leads to increased personal bias and subjectivity. Over time, with increased similarity of people at the executive level (e.g., more white males), it becomes increasingly difficult for someone of a different gender or race to break in. The effects of biases compound upon themselves, role after role, until there is no diversity in critical decision-making roles at the executive level.
This is why it is so critical for both executives and aspiring executives to be aware of these biases and actively combat them. Competition for roles at the executive level is fierce, so it is more important than ever to be intentional and transparent with everyone about how hiring or promotion decisions are made and what merits or skill sets are being rewarded in the process.
For a strong performer, facing these biases can be daunting and frustrating. After all, implicit bias is very difficult to abolish. For me, it felt like I was constantly pushing against a bouncy shapeless blob blocking my path to the executive level. It would yield a little bit when I asserted my strength but would bounce me back to where I had been every time.
Next up, we’ll talk about how to combat and overcome these biases! Subscribed to stay tuned.
[i] W. A. V. Clark, “Residential Preferences and Neighborhood Racial Segregation: A Test of the Schelling Segregation Model.” Demography 28, no. 1 (1991): 1–19, https://doi.org/10.2307/2061333.
[ii] Clark, “Residential Preferences.”