{"id":35426,"date":"2018-11-13T19:06:34","date_gmt":"2018-11-14T00:06:34","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-for-recruiting-at-amazon-challenges-and-opportunities\/"},"modified":"2018-11-13T19:06:34","modified_gmt":"2018-11-14T00:06:34","slug":"machine-learning-for-recruiting-at-amazon-challenges-and-opportunities","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-for-recruiting-at-amazon-challenges-and-opportunities\/","title":{"rendered":"Machine Learning for Recruiting at Amazon \u2013 Challenges and Opportunities"},"content":{"rendered":"

Machine learning has been around for sixty years, but just recently a confluence of factors came together that have enabled the field to make exponential progress: enormously increased data, significantly improved algorithms, and substantially more powerful computer hardware.[1]<\/a> As a result, machine learning is becoming ubiquitous. For example, in a recent study of 170 industrial organizations, 96% of respondents agreed or strongly agreed that machine learning is automating process-change management inside their organization. Two advantages of machine learning are self-adapting processes that can enhance customization, and self-repairing processes that can solve their own problems.[2]<\/a> While machine learning cannot be used to draw causal conclusions, it can be deployed to make predictions such as personalized recommendations for customers, forecasting long-term customer loyalty, anticipating the future performance of employees, and rating the credit risk of loan applicants.[3]<\/a><\/p>\n

As machine learning becomes more widely used, it is being applied to a growing number of industries and corporate functions. Recruiting is no exception. Today, companies take an average of 42 days at an average cost of $4,129 to fill each requisition. In addition, hiring mistakes are extremely costly: one study found that 41% of employers estimated a single bad hire costs at least $25,000, and 25% put the figure at $50,000 or more. When stakes are this high and the war for talent only becoming more competitive, companies must seek any edge.<\/p>\n

Amazon was unusually well suited to enter the machine learning for recruitment space because it had already effectively deployed machine learning elsewhere in the company. Amazon pushed its machine learning capability by creating Echo and the Alexa voice platform that powers it, which was in effect Amazon\u2019s Watson \u2013 a moonshot project that built a capability that could be leveraged throughout the company. Alexa \u201cspurred a larger AI renaissance at the company,\u201d enabling Amazon to apply the capabilities developed through Alexa to other products such as Fire TV, voice shopping, the Dash wand for Amazon fresh, and ultimately Amazon Web Services.[4]<\/a> Machine learning is now embedded in Amazon\u2019s culture.<\/p>\n

With these important successes under its belt, Amazon recently attempted to integrate machine learning into its recruitment process. Amazon\u2019s \u201cexperimental hiring tool used artificial intelligence to give job candidates scores ranging from one to five stars \u2013 much like shoppers rate products on Amazon.\u201d Initially, Amazon had high hopes for the project: \u201cEveryone wanted this holy grail\u2026they literally wanted it to be an engine where I\u2019m going to give you 100 resumes, it will spit out the top five, and we\u2019ll hire those.\u201d Unfortunately, the project stalled and was ultimately abandoned once it was determined that the machine-developed algorithm was biased against women: it penalized resumes that include the word \u201cwomen\u2019s,\u201d and it favored candidates who described themselves using terms more commonly found on male engineers\u2019 resumes such as \u201cexecuted\u201d and \u201ccaptured.\u201d[5]<\/a> According to recruiting expert Kaya Payseno, Amazon made three critical mistakes: thinking the bias is coming from the machine, limiting the data set, and deriving future predictions from past events.[6]<\/a> If Amazon had acknowledged these shortcomings, perhaps the project would still exist today.<\/p>\n

Despite this setback, opportunities still exist for Amazon to learn from other organizations that are effectively leveraging machine learning within their recruitment process. According to Susan Poser and Sharad Sinha of Oracle, \u201cwith the right strategy, computers can find correlations that humans overlook, leading to better candidates.\u201d[7]<\/a> Companies are \u201ctraining machine learning algorithms to help employees automate repetitive aspects of the recruitment process such as resume and application review;\u201d they are also integrating machine learning into other aspects of the process such as talent sourcing and candidate screening and engagement. Indigo, Canada\u2019s largest bookstore chain with 6,500 employees, used machine learning to reduce cost per hire by 71% and triple the quantity of qualified candidates in its applicant pool. Other technologies are helping companies tap into the large pool of passive candidates: for example, Entelo claims that its More Likely to Move algorithm can identify individuals who have a 30% likelihood of changing jobs within the next ninety days.[8]<\/a> All of these are important opportunities for Amazon to consider as it builds its massive workforce.<\/p>\n

When we look toward Amazon\u2019s future with machine learning in its recruitment process, open questions remain including (1) what conditions are necessary for machine learning to add value not only to recruitment but also selection; (2) is machine bias equivalent to human bias; and (3) what can be done to eliminate machine bias in the future.<\/p>\n

[1]<\/a>Erik Brynjolfsson and Andrew McAfee. “What\u2019s Driving the Machine Learning Explosion?\u201d 性视界 Business Review<\/em>. July 18, 2017. https:\/\/hbr.org\/2017\/07\/whats-driving-the-machine-learning-explosion<\/p>\n

[2]<\/a> Allan Alter, Sharad Sachdev, and H. James Wilson. \u201cBusiness Processes are Learning to Hack Themselves.\u201d 性视界 Business Review<\/em>. June 27, 2016. https:\/\/hbr.org\/2016\/06\/business-processes-are-learning-to-hack-themselves<\/p>\n

[3]<\/a> Mike Yeomans. \u201cWhat Every Manager Should Know About Machine Learning.\u201d 性视界 Business Review<\/em>. July 7, 2015. https:\/\/hbr.org\/2015\/07\/what-every-manager-should-know-about-machine-learning<\/p>\n

[4]<\/a> Steven Levy. \u201cInside Amazon\u2019s Artificial Intelligence Flywheel.\u201d Wired. February 1, 2018. https:\/\/www.wired.com\/story\/amazon-artificial-intelligence-flywheel\/<\/p>\n

[5]<\/a> Jeffrey Dastin. \u201cAmazon scraps secret AI recruiting tool that showed bias against women.\u201d Reuters. October 9, 2018. https:\/\/www.reuters.com\/article\/us-amazon-com-jobs-automation-insight\/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G<\/p>\n

[6]<\/a> Kaya Payseno. \u201cAvoid Amazon\u2019s 3 Biggest AI Recruiting Mistakes.\u201d Smartrecruiters.com. October 16, 2018. https:\/\/www.smartrecruiters.com\/blog\/avoid-amazons-3-biggest-recruiting-ai-mistakes\/<\/p>\n

[7]<\/a> Susan Poser and Sharad Sinha. \u201cHow Machine Learning Can Improve Recruiting.\u201d Oracle Profit Magazine. February 2018. https:\/\/blogs.oracle.com\/profit\/how-machine-learning-can-improve-recruiting<\/p>\n

[8]<\/a> Kumba Sennaar. \u201cMachine Learning for Recruiting and Hiring \u2013 6 Current Applications.\u201d TechEmergence. December 15, 2017. https:\/\/www.techemergence.com\/machine-learning-for-recruiting-and-hiring\/<\/p>\n","protected":false},"excerpt":{"rendered":"

Amazon’s challenges and opportunities using machine learning for recruiting<\/p>\n","protected":false},"author":11161,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","categories":[52,4919,2493],"class_list":["post-35426","hck-submission","type-hck-submission","status-publish","hentry","category-amazon","category-machinelearning","category-recruiting","hck-taxonomy-organization-harvard-business-school","hck-taxonomy-industry-education","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nMachine Learning for Recruiting at Amazon \u2013 Challenges and Opportunities - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-for-recruiting-at-amazon-challenges-and-opportunities\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning for Recruiting at Amazon \u2013 Challenges and Opportunities - 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