Words

17 Jan 2016

Despite being a nerd, I was never all that into reading science fiction and fantasy when I was growing up. Here’s a confession: I’ve never finished a single book by Tolkien or Asimov, though I tried many times in middle school and high school.

Then, when I was about 20, I read The Left Hand of Darkness by Ursula K. LeGuin. It not only changed what I thought science fiction books could be but what I thought books could be.

In the introduction to that book, LeGuin says something that I’ve never forgotten:

The artist deals with what cannot be said in words.

The artist whose medium is fiction does this in words. The novelist says in words what cannot be said in words.

I found that idea revelatory. It’s an elegant expression of, maybe, what literary criticism and to some degree history and every other liberal art is – the art of uncovering things that can’t be spoken of directly; the oblique approach when the direct one fails not despite but because of its directness.

After being actively involved in interviewing for a few years, I realized that deriving answers to unasked questions is at the heart of what great interviewers do. I also realized that questions that are anything other than surface-level starting places – lights to cast shadows on the cave wall, if you will – are bad interview questions.

Imagine you are conducting an interview where every question you ask will be answered with perfect truth and fidelity. What kind of questions would you ask? Are the answers you’d want even answers to ask-able questions? No. The information you want is qualitatively un-askable and unanswerable. Unanswerable in words, at any rate.

For instance, something I look for in co-workers is, well, whether they know how a computer works. So in our imaginary full-fidelity, absolute-honesty world, maybe I could ask a candidate “hey candidate, do you know how a computer works?” hoping for a magical shortcut. But what would the answer be? If “yes,” what does that mean? Does it mean the candidate could give a formal proof of Turing-completeness for an arbitrary set of rules? Does it mean they could explain how a modern processor works in perfect detail? Does it mean they could debug an intermittent failure in minified Javascript? Any of those things might be examples of “knowing how a computer works” but any one of them doesn’t imply the others.

Again in this magical full-fidelity and perfectly honest world, imagine you ask a candidate “could you debug an intermittent failure in minified Javascript?” Maybe in this world, they say “yes” and maybe they even point you at an instance when they did exactly that.

Still, the information you’ve received is useless.

Let’s think about what you know, with your perfect fidelity and absolute honesty: you know one particular thing that candidate can do. You don’t know why they can do that, or how they do it, or whether they can teach someone else to do it. You don’t know if they can apply what they know from that particular thing to anything else.

So, let’s briefly talk about bad interview questions. There is a broad class of terrible interview questions I call “vocab” questions, and they basically are all instances of ‘tell me the definition of x’. These come in lots of different forms, but some common ones are “what is an immutable class?” or “what is a critical section?”

These might be things that the person you’re looking to hire should know, if you aren’t in a position to mentor and invest in them, but the problem is that the answer only gives you one piece of data. If the candidate answers the question perfectly, what do you know? You know they know the answer to that question, and that’s all. If they don’t know the answer, what do you know? You know they don’t know the answer, and that’s all.

So maybe part 1 to being a good interviewer is “avoid vocab questions.”

It’s deeper than that. I believe that much of the reason for hatred of technical interviews, and the reason they so frequently fail, is because so many interviewers reduce even interesting technical questions to vocab questions.

Let’s take one of my favorite questions, solve a boggle board. Here’s a terrible way to evaluate a candidate’s performance in that question:

“$CANDIDATE wrote code to use breadth-first search to find all the strings in the board, and looked up each string in the dictionary to find matches. $CANDIDATE’s code for breadth-first search was cleanly written and worked after a couple of minor changes. $CANDIDATE was unable to make further optimizations.”

Like the “what’s a critical section” question, you have only determined whether the candidate could recognize and code a breadth-first search. You’ve identified nothing beyond that; you have mapped an infinitesimally small piece of the candidate’s potato-space.

Presumably something else happened in the interview beyond the simple mechanics of the interviewer presenting the problem and the candidate writing code to solve it. If that was all there was to it, then we could replace the entire interview process with a written exam. So why don’t we? Because, in a good interview, we are looking for exactly the kind of data that cannot be obtained through a written exam.

What kind of data am I talking about? I’m talking about how the candidate communicates – can they ask questions, look for clarifications, listen to answers? Do they interrupt you when you’re talking? If they do, is it because they’re excited, or is it because they’re not listening? Can they clearly explain algorithms? How do they react to questions about their code?

Your job as an interviewer isn’t only to ask a series of question and see if the candidate can answer them correctly – indeed, that isn’t your job at all.

Your job as an interviewer is not strictly to see what a candidate knows or doesn’t know – that set of things is constantly changing, even over the course of an interview. Your job as an interviewer is to ask, using words, questions that cannot be asked in words and derive answers that cannot be answered in words.

So: how do you do that? As technical interviewers, we are very lucky in that we have a scaffolding on which we can hang the un-askable questions. We can and should ask technical questions – on the surface level we can assess what a candidate knows, which, as I said, is useful in a very narrow scope – and they serve as our scaffolding. This scaffolding gives you the words you can use to ask what can’t be asked in words.

The real strength of this scaffolding is that it gives us a structure to get to a thing the candidate doesn’t know and that’s where the real magic starts to happen. If you think about asking surface level questions as moving along one vector in the aforementioned potato-space, when you hit a boundary that vector explodes in all kinds of different directions. All of a sudden you’re not asking “do you know this thing,” you’re asking, obliquely, “what happens when you don’t know a thing?” When your job is to make something out of nothing – and that is really what you’re doing in any creative field, creating software among them – the most common situation you will face is not knowing. So if you as an interviewer can watch the candidate show you – not tell you! – what they do in what is both the most challenging and most important part of their day-to-day job, you are all of a sudden going to see all sorts of stuff you could never, ever ask directly about: are they threatened? Are the excited? Do they ask questions? Do they make theories and test them? Can they use their existing knowledge to build towards an answer?

All this isn’t to say that there’s not room for assessing a candidate’s immediate skills: that will “fall out” of the process of getting at the more interesting, deeper questions. Depending on what you need, you might need someone who doesn’t need training or investment before contributing to a particular problem. But if you are interviewing well, answering those questions will give you answers to much more interesting questions as well.

Postscript: Bias

There’s a thing that lurks in my mind when I talk about the aspects of interviewing beyond skills & knowledge, and that thing is bias. There is an argument that if you only look at objective results – knowledge, facts, etc. – you are less subject to biases both conscious and unconscious. This subject deserves its own post, and I’m working on that. Apologies for not addressing it directly, but know that it is something that I am thinking about, and I’m aware that it is a critique of talking about interviewing like this. In the meantime, I recommend this presentation and this article by Cate Huston

Published on 17 Jan 2016 Find me on Twitter!