We Hire The Best

“We hire the best.”

It’s a slogan we can rally around in company meetings, a tagline to put on the jobs page… a shoring up of identity bound too much in employment.

It’s also manifestly untrue. A more accurate way of putting it might be:

Among people we know, we hire “the best” (as determined by our subjective process), who are willing and able to work in a specific place (or remotely), and who accept our offer.

Sadly, that’s not as catchy.

People We Know

Who you hire is profoundly limited by your company’s network, so when looking to diversify your workforce, it’s worthwhile to broaden your network. It is unlikely that everyone has heard of you, and of those that have, many may not know that you are hiring. Among the people who do know: are they aware that you hire people with their skills and/or backgrounds? A historically elitist hiring process may well discourage people from non-traditional backgrounds from applying, even if you later change it. Given the prevalence of team pages filled with white men, it’s possible for someone to hear about your company, look at the team page, and determine it’s not worthwhile to apply. The later diversity in hiring is considered, the bigger problem it becomes.

If you aim to hire mostly through referrals, those referrals are likely to reflect your current demographic — unless you work hard to change that. For example, women are often shut out of social gatherings (casual drink ups), or assumed to be service staff or partners of attendees at events where career connections are formed, and the typical social network of a white American is 1% black. Meanwhile, companies spend large amounts of time (or delegate to external recruiting firms at enormous expense) in order to find “passive” candidates – those who aren’t applying, but could be convinced to interview. This strategy is subject to serious bias, such as preferencing alums of pedigree universities and well-known companies. And recruiters often pitch minorities on too-junior roles — due to sexist and racist stereotypes, women are deemed less competent and black people are perceived to show less leadership. Such stereotypes and biases can play a huge role in if and how candidates are approached, and for what positions.

…As Determined By Our Subjective Process

All hiring processes are subjective which means: open to bias and flawed.

In Shaft’s article Thoughts On Diversity Part 1 (tackling the Meritocracy Myth), he observed:

“Yahoo > Google > LinkedIn > FaceBook > Twitter. After Yahoo each of these companies’ diversity numbers have been worse than the company that followed them. I believe this is because Google recruited from Yahoo, LinkedIn from Google, and so on. Each subsequent company becomes less diverse due to the sub-conscious amplification of educational, cultural and work history biases.”

These biases are particularly evident in the educational pipeline leading to the tech industry. Just looking at the Ivy League, we see disproportionate representation of “legacy” enrollment: between 10 and 15% of students are children of alums. A 2011 study found that when a parent had attended that same university, students’ chance of admission went up 45.1%. In Dartmouth’s 2015 class, legacy admissions comprise 8.5% of the student body. Princeton legacies have a 33% admission rate, compared to 8.5% general admission rate, and Yale 20-25% compared to 6.7% (2013). And legacies make up 12-13% of Harvard undergraduates, with a 30% admission rate (2013).

Photo CC-BY mararie, filtered.

Meanwhile, racial diversity in the Ivy League is dismal. As of 2012, the highest enrollment rate of black students in an Ivy League school was 7.7%, compared to a US population that is 13.1% black. And the highest rate of enrollment of Hispanic students was 13.2%, compared to their 16.7% overall representation. Socio-economic status and parental income is also a significant factor in admissions to Ivy League universities. 69% of Yale undergrads come from families with incomes over $120,000, with just 15% of students coming from families with incomes below $65,000 (2014). Meanwhile, the US mean household income is $60,528, and is lower for black and Hispanic households.

Further, admission rates are distinct from graduation rates, where we also see systemic inequality. For example, at University of California (Berkeley), white students have a graduation rate of 92%, while Hispanic and black students have a graduation rate of 81% and 71%, respectively. (For further reading, Whistling Vivaldi contains some good discussion). And while this data looks at a particular school system, it is a great illustration of how filtering resumes on pedigree companies and universities builds on the flawed, biased processes that have come before.

Interviewing

While much inequality enters the hiring process even before an interview, this is yet another place where we see significant bias enter the equation. Interviews range from unstructured conversations based around assessing “culture fit”, a vague practise that opens up almost unbounded opportunities for discrimination, to extremely structured interview questions which may or may not cover topics that are even relevant to the job.

For example, the prevalent interview system in the Valley means assessing a candidate’s grasp of 2nd year Computer Science Algorithms and Data Structures courses through contrived problems. This is commonplace at companies including Microsoft, Google, Amazon, Facebook and Palantir. These interviews mainly test people’s abilities to remember, revise, or learn the algorithms and data structures, and to respond appropriately to artificial problems (pro-tip: the answer is usually a Hashtable). It is problematic in hiring people from non-academic backgrounds (who may not have taken 2nd year Algorithms and Data Structures), or who went to universities which do not cover that material to the necessary standard.

Photo CC-BY Chris Sammis, filtered.

This is also challenging for specialists; for example, much of this content has little bearing on front-end Javascript, and often the most challenging problems in mobile have less to do with algorithms than physical concerns like networking and battery life. In this subjective process, a tangentially related thing is used to assess someone’s ability to do a job that comprises 99.9% other things. In fact, Google’s own data from using this process has revealed interview performance has little bearing on job performance.

There are numerous other issues with the design of interviews — many of which have been discussed elsewhere — including:

1. Trick questions. May be based on knowing some esoteric factoid about the JVM, or that require an “aha” moment or are otherwise impossible to solve.
2. Other kinds of bad questions: for example, questions that are too large for anyone to get through during the interview process, and so results are biased by which part of the question the interviewee focused on.
3. Bad interviewers: interviewers who are not clear in their questions, who manage time poorly, etc.

Finally, interview structure aside, feedback on applicant performance is riddled with “unconscious bias”. Having read much of the research around stereotypes and bias, when working at a major tech company I would spend around 30 minutes combing through my feedback for bias after interviewing minorities. Consistently, I found small comments and phrases that I would remove or rephrase to reduce bias. This doesn’t mean I invariably supported the hiring of minorities who I interviewed; I did not. Merely, I tried not to further stack the odds against them through my feedback and picking on things I would not have highlighted had the person I interviewed been a white male. This also does not mean that my feedback was without bias: bias runs too deep in all of us. It just means that I did my best to be conscious of my bias and work to combat it.

It is unlikely that many, or even any of your interviewers are doing this: they lack the domain knowledge, and will not take the time. And the reward for employees who do do this kind of extra work is often being asked to do more of it: recruiters are incentivized to hire and learn which interviewers care and are quick and painstaking in their feedback. Relatedly, programs to improve the experience for women candidates often create additional work for the women who are already there. Well-intentioned companies interviewing women often aim to get a woman on each interview slate, which can be problematic and causes the work of interviewing to disproportionately fall on women. These extra tasks around hiring and outreach are something that is for the good of the collective, and are exactly the kind of task that women receive little or no recognition for but are penalized for not agreeing to (see Women Don’t Ask).

Plus, it often doesn’t make for a good experience for women-interviewees, either – yes, we notice when we come in and don’t meet any women, but we also notice when we get a female interviewer who is clearly in the wrong domain, or inappropriately junior.

Reviewing

At this point in the hiring process, feedback is collected and another person or group of people review it and make a decision. Many of the filtering and interviewing biases are amplified here as more people get involved and look for adverse signals (like “shyness” or lack of technical credibility, or an unusual background), unless a serious and concerted effort is made to remove them.

It’s also in this stage that we commonly see reference checks: another possible source of bias. As part of some volunteer work, I ran a committee assessing female CS students for scholarships. As part of the process, we reviewed recommendation letters from their (mostly male) professors: the prevalence of gendered feedback in these letters was frankly appalling.

Are Willing to Work [In a specific place] / [remotely]

Photo CC-BY Revol Web, filtered.

The phrase “we hire the best” irks me everywhere, but most of all in the Valley. One would think that the competition and amount of movement between companies in the valley precludes this, but there are incredible people around the world who who do not wish to move there, and with good reason. There are the US issues, such as the difficulty of procuring visas, poor social policies around childcare and maternity leave, and the localized issues of high rents. In addition, professional women are more likely to face the “two body problem”, having a spouse with a job that is hard to move, or to be the primary carer for a relative that limits their geographic mobility.

Remote work is one way of getting around this, however some people, particularly junior people, may not wish to work remotely, and want the benefit of in-person interaction and mentorship. Some may just find it too lonely, or not a fit for their working style. Remote work is not suited to everyone’s temperament.

Those Who Accept

In the end, hiring “the best” is dependent on “the best” accepting your offer in the first place.

One of my friends has been interviewing recently and it’s fascinating how poorly some of the companies have treated her over the course of extending her an offer. One company lowballed her salary, was late on every deadline, and generally did not respect her time. Perhaps they think anyone who makes it through their arduous and time-consuming process must be committed to saying yes, or perhaps they’re just relying on their brand.

During the process they used to determine she was a good fit for them, she figured out they were not going to be the best fit for her. She’ll go somewhere where they respect her time, and offer her fair compensation up front. Somewhere that communicates with her in a timely manner. As for startups she spoke to who treated her really well, there’s every chance that she’ll consider them again in 2 years or so, when she’s thinking about making a move again.

There’s massive competition to hire engineers, and engineers talk amongst themselves. We know that Glassdoor and the like are full of bitter animadversions from people who didn’t get hired, but it is much more damning when someone gets an offer and chooses not to, based on their experiences in the process. Most people consider multiple options when making their next career move, and they will weigh things that you may have no control over: their commute time, their desire to start their own company. But they will also weigh up things you can control. Their compensation. Their impression of the company. And how recruiters and interviewers treated them.

No Such Thing

The tech industry prides itself on its rationality and yet is filled with trite slogans, like “hire the best,” that are demonstrably untrue… and further, harmful. White men declare loudly and proudly that the issue is “unconscious bias” and ignore the depressing data on sexual harassment. They claim to be trying to build more diverse companies and yet all effort focuses on the pipeline, children and university students, while the 56% of women who drop out by mid-career are ignored.

“We hire the best” they cry. But they don’t. They never will. This mentality justifies the homogenous workplaces, where much is invested in maintaining this facade and propping up the status quo. The system works, and therefore I am here. I am here, and therefore the system works. It is used to justify lengthy processes of interviewing, of unpaid or poorly paid projects. The new trend of unpaid labour in less prestigious roles such as customer support reflects previous trends of demanding Open Source contributions, which is problematic and ineffective. Candidates must prove that they want it enough, prove that they are “the best”, where “the best” sometimes just means the most willing and able to work for free.

The truth is, that there is no such thing as “the best,” except for the most arbitrary of metrics.

Perhaps if we could admit that we all hire flawed humans, then we could do the work to create environments where those flawed humans treat each other well. We might not be able to claim “the best,” but we would all be better off.

Thanks to Julia Evans for reviewing and giving feedback on this article, and Alex Wilson for helping with research.