The tragedy of bias in technical hiring

2014 The Tragedy of Bias in Technical Hiring in Five Acts Kelsey Foley Oct 10, 2014 #GHC14 2014 2014 Why are there so few women in tech? 1. “The Pipeline” – not enough trained women 2014 Why are there so few women in tech? 1. “The Pipeline” – not enough trained women 1. Industry doesn’t know how to recruit and hire women. 1. Industry doesn’t know how to retain women. (Hint: Industry must hire women before retaining them!) 2014 Synopsis  The Birthplace of Bias – and how to combat it  How bias manifests in: − Job descriptions − The Interview Process − The Hire or No-Hire Decision 2014 Act 1: The Players “All the world’s a stage, and all the men and women merely players.” - William Shakespeare, As You Like It 2014 Meet Julie Ette: • BS in CS from StateU • 5 years work experience with two mobile software companies • Looking for a new job 2014 Meet Monty and Ben: Monty Gue, Engineering manager at hot mobile startup Roam.io Ben Volio, Technical recruiter at Roam.io 2014 Will Julie find a match with Monty’s team? Let’s find out… 2014 Act 2: The Birthplace of Bias “Wisely and slow. They stumble that run fast.” - William Shakespeare, Romeo and Juliet 2014 The Two-Systems Model of Judgment and Choice (Kahneman, Daniel. Thinking, Fast and Slow. Farrar, Straus and Giroux. 2011.) [...]... Common Biases in Hiring  Confirmation Bias − Seeking data that confirms our ideas  Fundamental Attribution Error, or the Negativity Effect − Over-emphasizing traits in others while underemphasizing situations (luck) in ourselves 2014 Common Biases in Hiring  Predicting by Representativeness − Making decisions using association with a stereotype  Projection Bias − Unconsciously assuming that others share... 4: The Interview 2014 The Classic Software Interview  Short call with a recruiter  A technical phone screen, some coding  On-site interview with 4-6 sessions, all with heavy coding  Many tech companies do no training on how to interview − Some focus on legal areas of questioning − A few give training but do not monitor how these techniques are used in interviews 2014 How effective are tech interviews?... culture fit Cindy Paris: mad programming skillz - languages, algorithms, data structures, coding Phil Laurence: Debugging and testing in mobile & embedded Finish with Hiring Manager Monty Gue 2014 Interviewing is bi-directional! Julie is also evaluating: − − − − − The manager Potential coworkers The company The workplace environment The technology stack The interview experience will impact Julie’s final decision!... most.” - Dr Daniel Kahneman, Thinking, Fast and Slow, pp.417 2014 System 1 in Interviews The optimal time to make a decision about the candidate is about three minutes after the end of the interview… I ask interviewers to write immediate feedback after the interview, either a “hire” or “no hire”, followed by a one or two paragraph justification It’s due 15 minutes after the interview ends.” “Never say... “For the record, we don’t think that the way interviewing is done today is necessarily the way it should be done The current paradigm puts too much emphasis on the ability to solve puzzles and familiarity with a relatively limited body of knowledge, and it generally fails to measure a lot of the skills that are critical to success in industry.” - Mongan, John, Eric Giguere, and Noah Kindler Programming... few drinks have the same effect, as does a sleepless night.” - Dr Daniel Kahneman, Thinking, Fast and Slow, pp.41 2014 Common Biases in Hiring  Casuistry − using specious reasoning to rationalize behavior or decisions  The Halo Effect − First impressions influence later experience  Affect Heuristic − People answer an easy question with System 1 instead of a harder one with System 2 2014 Common Biases... than you’d think Can’t tell? Just say no! If you are on the fence, that means No Hire… Mechanically translate all the waffling to “no” and you’ll be all right.” - Joel Spolsky, The Guerrilla Guide to Interviewing v3.0, Oct 25, 2006 http://www.joelonsoftware.com/articles/GuerrillaInterviewing3.html 2014 Act 3: Attracting Diverse Candidates 2014 Subtle Cues in Job Descriptions The purpose of a job description?... and values 2014 So… How do we overcome our biases? “What can be done about biases? How can we improve judgments and decisions, both our own and those of the institutions that we serve and that serve us? The way to block errors that originate in System 1 is simple in principle: recognize the signs that you are in a cognitive minefield, slow down, and ask for reinforcement from System 2 Unfortunately,... gaming and auto racing? XXXX Game Studios is hiring! You are a Senior Software Development Engineer with broad game development experience and world-class software engineering skills You’re the kind of person who drives projects to completion, sometimes across multiple functions and groups.” (See any Projection Bias? Casuistry? Representativeness?) 2014 Bad (and real!) examples The Application Programmer... Source Data Comes from the cultural soup we experience every day since infancy: • Role models - parents, teachers, siblings, and caregivers • TV, books, music, and cultural memes • Peers and their own source data! System 1 creates a meaningful story from our senses and experiences! (Efforts to fix The Pipeline change the next generation’s patterns.) 2014 The Birthplace of Bias Cognitive bias happens when . 2014 The Tragedy of Bias in Technical Hiring in Five Acts Kelsey Foley Oct 10, 2014 #GHC14 2014 2014 Why are there so few women in tech? 1. The Pipeline” – not enough trained women 2014 Why. retaining them!) 2014 Synopsis  The Birthplace of Bias – and how to combat it  How bias manifests in: − Job descriptions − The Interview Process − The Hire or No-Hire Decision 2014 Act 1: The. Effect − Over-emphasizing traits in others while under- emphasizing situations (luck) in ourselves 2014 Common Biases in Hiring  Predicting by Representativeness − Making decisions using association