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The Tragedy of Bias in Technical Hiring in Five Acts (Grace Hopper 2014)

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Why do some companies succeed in hiring women engineers while others struggle with even attracting qualified female candidates? This talk will follow fictitious hiring manager Monty Gue from startup Roam.io and savvy engineer Julie Ette through the recruiting and interviewing process while exposing subtle biases in hiring practices that drive technical women elsewhere. Using recent behavioral psychology research on judgment and bias, it will provide insight for better approaches.

Published in: Recruiting & HR, Engineering
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The Tragedy of Bias in Technical Hiring in Five Acts (Grace Hopper 2014)

  1. The Tragedy of Bias in Technical Hiring in Five Acts Kelsey Foley Oct 10, 2014 2014 #GHC14 2014
  2. Why are there so few women in tech? 1. “The Pipeline” – not enough trained women 2014
  3. Why are there so few women in tech? 1. “The Pipeline” – not enough trained women 1. Industry doesn’t know how to recruit and 2014 hire women. 1. Industry doesn’t know how to retain women. (Hint: Industry must hire women before retaining them!)
  4. 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
  5. 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
  6. Meet Julie Ette: 2014 • BS in CS from StateU • 5 years work experience with two mobile software companies • Looking for a new job
  7. Meet Monty and Ben: 2014 Monty Gue, Engineering manager at hot mobile startup Roam.io Ben Volio, Technical recruiter at Roam.io
  8. Will Julie find a match with Monty’s team? 2014 Let’s find out…
  9. Act 2: The Birthplace of Bias “Wisely and slow. They stumble that run fast.” - William Shakespeare, Romeo and Juliet 2014
  10. The Two-Systems Model of Judgment and Choice System 1 Thinking:  Fast  Effortless  Automatic, involuntary  Takes mental short cuts  Driven by impressions, patterns, intuitions, memories, and feelings  Prone to error and bias 2014 unless checked by System 2 System 2 Thinking:  Slow  Effortful, limited energy budget  Conscious engagement required  Can be lazy  Applies methodical, reasoned, and coherent thinking to the System 1 raw data (Kahneman, Daniel. Thinking, Fast and Slow. Farrar, Straus and Giroux. 2011.)
  11. System 1 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
  12. The Birthplace of Bias Cognitive bias happens when System 1 decides without System 2 helping to catch errors, assumptions, biases, and mental short cuts! (We all do this! Don’t feel bad. It’s part of being human!) 2014
  13. Tech Company Culture Exacerbates Bias “People who are cognitively busy are also more likely to make selfish choices, use sexist language, and make superficial judgments in social situations…. but of course cognitive load is not the only cause of weakened self-control. A few drinks have the same effect, as does a sleepless night.” - Dr. Daniel Kahneman, Thinking, Fast and Slow, pp.41 2014
  14. Common Biases in Hiring 2014  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
  15. Common Biases in Hiring 2014  Confirmation Bias − Seeking data that confirms our ideas  Fundamental Attribution Error, or the Negativity Effect − Over-emphasizing traits in others while under-emphasizing situations (luck) in ourselves
  16. Common Biases in Hiring  Predicting by Representativeness − Making decisions using association with a 2014 stereotype  Projection Bias − Unconsciously assuming that others share our own perspectives, thoughts, and values
  17. 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, this sensible procedure is least likely to be applied when it is needed most.” - Dr. Daniel Kahneman, Thinking, Fast and Slow, pp.417 2014
  18. 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 “Maybe, I can’t tell.” If you can’t tell, that means No Hire. It’s really easier 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
  19. Act 3: Attracting Diverse Candidates 2014
  20. Subtle Cues in Job Descriptions The purpose of a job description? 1. Internal: communicate hiring requirements 2. External: promote the job and company How can the job description project bias? (See also: Gaucher, D., Friesen, J., & Kay, A. C. (2011, March 7). Evidence That Gendered Wording in Job Advertisements Exists and Sustains Gender Inequality. Journal of Personality and Social Psychology. ) 2014
  21. Bad (and real!) examples “Do you have a passion for quality 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
  22. Bad (and real!) examples “The Application Programmer Analyst plays a vital role on the ZZZZ Medical Group Support team, demonstrating our values of patient-centered care and service; respect, caring and compassion; teamwork and partnership; continuous learning and improvement; and leadership. In this position you will: Enhance existing computer programs to add value throughout the organization…” (Representative stereotypes exist for female-dominated roles too) 2014
  23. Bad (and real!) examples “QQQ Software runs a developer paradise: the latest technologies and platforms and an elite team of great developers. No résumé needed! Great work speaks for itself. We'd love links to your GitHub or StackExchange profile! Your Profile: You live, eat and dream about code and test! Your drive to know more and do better makes you evolve...” (Projection Bias and Representativeness) 2014
  24. Some recent (real!) examples Education Required: BS (Technical ), Masters preferred Experience Required: Prior experience at the Director level or equivalent Physical Requirements: Must be able to execute a two-handed reverse dunk on a ten-foot rim without the aid of a trampoline. 2014
  25. Who wants these as coworkers? (And why are they all holding weapons?) 2014
  26. To Attract More Diverse Candidates:  Be aware of the impact of language.  Write job descriptions that don’t create role biases! − Look carefully for values, traits, behaviors, and 2014 motivations − Find gender-neutral ways to “sell” the job  Circulate several versions to attract candidates with diverse motivations and career objectives.  Get people with different perspectives to edit job descriptions – how would someone in marketing write an engineering job description?
  27. Monty’s JD Ben’s JD Roam.io is hiring versatile software engineers with a passion for making products that impact our customer’s lives. Our developers support and challenges each other to continuously learn and improve. We believe in working at a sustainable pace following Agile philosophies. Roam.io offers flexible schedules and a respectful and fun work environment. Join us and make a difference! Do you live, breathe, eat, and dream about coding and mobile? Do you crave fanfare and adulation from users? Roam.io is hiring! We’re looking for software geeks who can thrive in our high-energy 2014 open office environment. We offer great benefits, free food and beer, foosball, and tons of fun. Come join our elite team and push the barriers in mobile technology! (Which one would Julie apply for? What about Julio?)
  28. Act 4: The Interview 2014
  29. 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
  30. How effective are tech interviews? “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 Interviews Exposed: Secrets to Landing Your Next Job, 2013, pp. xxvi 2014
  31. Schmidt, F.L. & Hunter, J.E. (1998) The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings,” Psychological Bulletin, 124, 262–274. Personnel Measures Predictive Validity (r) Work sample tests 0.54 General cognitive ability 0.51 tests 2014 Employment interviews, structured 0.51 Job knowledge tests 0.48 Behavioral questions 0.45 Employment interviews, 0.38 unstructured Reference checks 0.26 Job experience 0.18 Years of education 0.10 Age -0.01
  32. What can this look like in practice?  Train interviewers about cognitive biases  Ask some coding questions  Also ask behavioral, work habits, and job knowledge questions to assess all the other success factors  A great resource for technical managers to use with their teams is this book: Rothman, Johanna. Hiring Geeks That Fit. Rothman Consulting Group, Inc. 2013. 2014
  33. Julie’s interview: 2014 Welcome with recruiter Ben Volio Software lifecycle, Agile, communication style, personal work habits with Merlin Cutio Petra Escalus: CS fundamentals - complexity, networks, threads, databases, OS Ty Balt and Amy Bram: lunch at Café Verona and behavioral and 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
  34. 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! 2014
  35. Act 5: The Decision 2014
  36. The Post-Interview Debrief  Review job requirements first  Avoid the Affect Heuristic! − Ask: “What evidence did you see that Julie has the skills for job requirement #1?” − Don’t ask: “What did you think of Julie?”  Watch for cognitive landmines: − personality traits, intuitions, impressions, or stereotypes, − Slow down, engage System 2 − Dig into possible bias with questions  Give a numerical score for each job requirement − Numerical scores engage System 2 − Don’t use “hire” or “no hire” which uses System 1. 2014
  37. Epilogue “If this were play'd upon a stage now, I could condemn it as an improbable fiction.” - William Shakespeare, Twelfth Night 2014
  38. Outcomes Changing technical interviews won’t convert every hire from a tragedy to a romance… But, it sets the stage for candidates of all kinds to audition on equal terms. 2014
  39. And what of Julie and fair Roam.io? 2014
  40. Thank you! Want to use #System2Hiring in your workplace? Or: Want to experience #System2Hiring yourself? My team is hiring! 2014 Kelsey Foley kelsey@moz.com @EnigmaticKelsey
  41. Got Feedback? Rate and Review the session using the GHC Mobile App To download visit www.gracehopper.org 2014

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