Primer on Big Data and Hiring: Chapter 5

This is the fifth chapter of a primer on big data and hiring. The structure of the primer is based on the following graphic created by Evolv, a company that provides "workforce optimization" services. Evolv was selected not because it is sui generis; rather, it is emblematic of numerous companies, from start-ups to well-established companies that market "workforce science" services to employers.

The Evolv graphic below is intended to illustrate the process of workforce science.

Chapter 5: Big Data Networking

Results Are Benchmarked Against Big Data Network

According to Evolv, its network

extracts learnings and insight from the millions of real time talent data points – from across the Evolv client base, or Network – streaming in and out of the Evolv platform every single day, week and month.  

Some of the real time data points streaming in and out of the Evolv Network are data from the use of pre-employment personality tests administered to applicants and employees. As set out in the posts What Are The Issues?, ADA, FFM and DSM and Employment Assessment Are Designed to Reveal An Impairment, tests utilizing the Five-Factor Model of personality may be considered illegal medical examinations under the Americans with Disabilities Act (ADA).

Consequently, information obtained from those tests is confidential medical information, the use of which is subject to strict limits. Regulations require that confidential information be kept on separate forms and in separate files and it may not be intermingled with other information - i.e., shared with third parties on the Evolv network.

Not only are employers subject to claims by applicants and employees alleging breach of the confidentiality provisions of the ADA, the use of confidential medical information in assessment, hiring and other human resource function may have created a virus that has "infected" a variety of databases, applications and software solutions utilized by the employer.

Costs to employers from the illegal use of confidential medical information include damages payable to applicants and employees, defense transaction costs (i.e., legal fees), and costs to "sanitize" infected databases, applications and software solutions. As set out in the Damages and Indemnification Challenges for Employers post, even if companies like Evolv are willing to provide indemnification to all customers, those customers will have to determine whether the company and its insurers have adequate resources to indemnify all customers.

Primer on Big Data and Hiring: Chapter 4

This is the fourth chapter of a primer on big data and hiring. The structure of the primer is based on the following graphic created by Evolv, a company that provides "workforce optimization" services. Evolv was selected not because it is sui generis; rather, it is emblematic of numerous companies, from start-ups to well-established companies that market "workforce science" services to employers.

The Evolv graphic below is intended to illustrate the process of workforce science.

Chapter 4: Analyze and Predict

Data Analyzed Using Machine Learning and Predictive Algorithms

The theory of big data is to have no theory, at least about human nature. One just gathers huge amounts of information, observes the patterns and estimates probabilities about how people will act in the future. One does not address causality.

In linear systems, cause and effect is much easier to pinpoint. However, the world around us is considered a complex system where there are often multiple variables pushing an outcome to occur. Nigel Goldenfeld, a professor of physics at University Illinois, sums it up best: “For every event that occurs, there are a multitude of possible causes, and the extent to which each contributes to the event is not clear.”

Algorithms and big data are powerful tools. Wisely used, they can help match the right people with the right jobs. But they must be designed and used by humans, or they can go very wrong. As David Brooks wrote in the New York Times:

Data creates bigger haystacks. This is a point Nassim Taleb, the author of “Antifragile,” has made. As we acquire more data, we have the ability to find many, many more statistically significant correlations. Most of these correlations are spurious and deceive us when we’re trying to understand a situation. Falsity grows exponentially the more data we collect. 

There’s a saying in artificial intelligence circles that techniques like machine learning can very quickly get you 80% of the way to solving just about any (real world) problem, but going beyond 80% is extremely hard, maybe even impossible. The Netflix Challenge is a case in point: hundreds of the best researchers in the world worked on the problem for 2 years and the winning team got a 10% improvement over Netflix’s in-house algorithm.

A corollary of the above saying is that it is very rare for startup companies to ever have a competitive advantage because of their machine learning algorithms.  If a worldwide concerted effort can only improve Netflix’s algorithm by 10%, how likely are 4 people in an R&D department in a startup going to have a significant breakthrough.  Modern machine algorithms are the product of thousands of academics and billions of dollars of R&D and are generally only improved upon at the margins by individual companies.

Some of the best and brightest organizations have recognized that improvement, if any, in machine learning comes from outside the organization. Facebook, Ford, GE and other companies have run contests for data-science challenges on Kaggle, while NASA and other government agencies, as well as the Harvard Business School, have taken the crowdsource route on Topcoder.

Abhishek Shivkumar of IBM Watson Labs has listed the top ten problems for machine learning in 2013. These problems include churn prediction, truth and veracity, scalability and intelligent learning. This doesn’t mean machine learning isn’t ever useful – it just means one needs to apply it to contexts that are fault tolerant:  for example, online ad targeting, ranking search results, recommendations, and spam filtering.  Applying machine learning concepts in the context of persons livelihoods (and, potentially, lives) is problematic, not just for the individual applicant or employee but also for the employer and its potential liability exposure. 

Analyze and Predict

Data Analyzed Using Machine Learning and Predictive Algorithms

The theory of big data is to have no theory, at least about human nature. One just gathers huge amounts of information, observes the patterns and estimates probabilities about how people will act in the future. One does not address causality.

In linear systems, cause and effect is much easier to pinpoint. However, the world around us is considered a complex system where there are often multiple variables pushing an outcome to occur. Nigel Goldenfeld, a professor of physics at University Illinois, sums it up best: “For every event that occurs, there are a multitude of possible causes, and the extent to which each contributes to the event is not clear.”

Algorithms and big data are powerful tools. Wisely used, they can help match the right people with the right jobs. But they must be designed and used by humans, or they can go very wrong. As David Brooks wrote in the New York Times:

Data creates bigger haystacks. This is a point Nassim Taleb, the author of “Antifragile,” has made. As we acquire more data, we have the ability to find many, many more statistically significant correlations. Most of these correlations are spurious and deceive us when we’re trying to understand a situation. Falsity grows exponentially the more data we collect. 

There’s a saying in artificial intelligence circles that techniques like machine learning can very quickly get you 80% of the way to solving just about any (real world) problem, but going beyond 80% is extremely hard, maybe even impossible. The Netflix Challenge is a case in point: hundreds of the best researchers in the world worked on the problem for 2 years and the winning team got a 10% improvement over Netflix’s in-house algorithm.

A corollary of the above saying is that it is very rare for startup companies to ever have a competitive advantage because of their machine learning algorithms.  If a worldwide concerted effort can only improve Netflix’s algorithm by 10%, how likely are 4 people in an R&D department in a startup going to have a significant breakthrough.  Modern machine algorithms are the product of thousands of academics and billions of dollars of R&D and are generally only improved upon at the margins by individual companies.

Some of the best and brightest organizations have recognized that improvement, if any, in machine learning comes from outside the organization. Facebook, Ford, GE and other companies have run contests for data-science challenges on Kaggle, while NASA and other government agencies, as well as the Harvard Business School, have taken the crowdsource route on Topcoder.

Abhishek Shivkumar of IBM Watson Labs has listed the top ten problems for machine learning in 2013. These problems include churn prediction, truth and veracity, scalability and intelligent learning. This doesn’t mean machine learning isn’t ever useful – it just means one needs to apply it to contexts that are fault tolerant:  for example, online ad targeting, ranking search results, recommendations, and spam filtering.  Applying machine learning concepts in the context of persons livelihoods (and, potentially, lives) is problematic, not just for the individual applicant or employee but also for the employer and its potential liability exposure. 

Primer on Big Data and Hiring: Chapter 3

This is the third chapter of a primer on big data and hiring. The structure of the primer is based on the following graphic created by Evolv, a company that provides "workforce optimization" services. Evolv was selected not because it is sui generis; rather, it is emblematic of numerous companies, from start-ups to well-established companies that market "workforce science" services to employers.

The Evolv graphic below is intended to illustrate the process of workforce science.

Chapter 3: Cleanse and Upload

Structured and Unstructured Data Is Aggregated

Data isn't something that's abstract and value-neutral. Data only exists when it's collected, and collecting data is a human activity. And in turn, the act of collecting and analyzing data changes (one could even say "interprets") us. 

Workforce science requires enormous amounts of historic or legacy data. This data has to be consolidated from a number of disparate source systems within each company, each with their specific data environment and particular brand of business logic. That data consolidation then must be replicated across hundreds or thousands of companies.

Structured data refers to data that is identifiable because it is organized in a structure. The most common form of structured data is a database where specific information is stored based on a methodology of columns and rows (i.e., Excel). Structured data is understood by computers and is also efficiently organized for human readers.  

Unstructured data refers to information that either does not have a pre-defined data model or is not organized in a pre-defined manner. This results in irregularities and ambiguities that make it difficult to understand using traditional computer programs as compared to structured data.

Unstructured data consists of two basic categories; textual objects (based on written or printed language, such as emails or Word documents); and bitmap objects (non-language based, such as image, video or audio files).

There are many types of techniques that need to be put together in a complex data processing flow utilizing unstructured data. These techniques include: 

• information extraction (to produce structured records from text or semi-structured data)
• cleansing and normalization (to be able to even compare string values of the same type, such as a dollar amount or a job title)
• entity resolution (to link records that correspond to the same real-world entity or that are related via some other type of semantic relationship)
• mapping (to bring the extracted and linked records to a uniform schematic representation)
• data fusion (to merge all the related facts into one integrated, clean object)

Assumptions are embedded in a data model upon its creation. Data sources are shaped through ‘washing’, integration, and algorithmic calculations in order to be commensurate to an acceptable level that allows a data set to be created. By the time the data are ready to be used, they are already ‘at several degrees of remove from the world.’

Data is never raw; it’s always structured according to somebody’s predispositions and values. The end result looks disinterested, but, in reality, there are value choices all the way through, from construction to interpretation.

Primer on Big Data and Hiring: Chapter 2

This is the first chapter of a primer on big data and hiring. The structure of the primer is based on the following graphic created by Evolv, a company that provides "workforce optimization" services. Evolv was selected not because it is sui generis; rather, it is emblematic of numerous companies, from start-ups to well-established companies that market "workforce science" services to employers.

The Evolv graphic below is intended to illustrate the process of workforce science.

Chapter 2: Supplement

Psychometric tools gather predictive data on the workforce throughout the employment lifecycle

Psychometrics is the field of study concerned with the theory and technique of psychological measurement, which includes the measurement of knowledge, abilities, attitudes, personality traits, and educational measurement. The field is primarily concerned with the construction and validation of measurement instruments such as questionnaires, tests, and personality assessments.

To what extent are psychometric tools accurate predictors of behavior or performance?

According to a 2012 study by Oracle and Development Dimensions International (DDI), a global human resources consulting firm whose expertise includes designing and implementing selection systems, more than 250 staffing directors and over 2,000 new hires from 28 countries provided the following perspectives on their organization’s selection processes (the following are excerpts from the study):

• [O]nly 41 percent of staffing directors report that their pre-employment assessments are able to predict better hires.
• Only half of staffing directors rate their systems as effective, and even fewer view them as aligned, objective, flexible, efficient, or integrated. 
• [T]he actual process for making a hiring decision is less effective than a coin toss.

In a 2007 article titled, “Reconsidering the Use of Personality Tests in Employment Contexts”, co-authored by six current or former editors of academic psychological journals, Dr. Kevin Murphy, Professor of Psychology at Pennsylvania State University and Editor of the Journal of Applied Psychology (1996-2002), states:

The problem with personality tests is … that the validity of personality measures as predictors of job performance is often disappointingly low. A couple of years ago, I heard a SIOP talk by Murray Barrick … He said, “If you took all the … [factors], measured well, you corrected for everything using the most optimistic corrections you could possibly get, you could account for about 15% of the variance in performance [between projected and actual performance].” … You are saying that if you take normal personality tests, putting everything together in an optimal fashion and being as optimistic as possible, you’ll leave 85% of the variance unaccounted for. The argument for using personality tests to predict performance does not strike me as convincing in the first place.

The hiring assessment functionality of workforce analytics contains three divergent logic systems, legal, psychological and managerial. The legal logic system derives, in part, from the Americans with Disabilities Act (ADA) and its accompanying regulations and related caselaw. The psychological logic system derives primarily from the five-factor model of personality, or Big Five, as it has evolved over the past 20-25 years. The managerial logic derives from the implementation of the human resource function of the employer. Technology in the form of workforce analytics then attempts to tie these three logic systems together in order to create an automated assessment program that attempts to determine applicant "suitability" or "fit."

For example, many workforce analytic companies utilize the Big Five model in creating their personality assessments. As its name implies, the Big Five looks at five traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism, with each trait conceptualized on an axis from low to high (e.g., low neuroticism, high neuroticism). The Big Five, operating under various names, existed for a number of decades prior to its "rebirth" in the early 1990s, where it was embraced by organizational psychologists.

Since the late 1990s workforce analytics companies like Unicru (now owned by Kronos) have adopted the Big Five for use in their job applicant assessment program. The workforce analytic companies have created "model" psychological profiles and tested applicants against those profiles. In general, applicants receive either green, yellow or red scores on the basis of a 50/25/25 cutoff. Applicants scoring red are generally not interviewed, let alone hired.

The use of technology systems to hardwire workforce analytics raises a number of fundamental issues regarding the translation of legal mandates, psychological models and business practices into computer code and the resulting distortions. These translation distortions arise from the organizational and social context in which translation occurs; choices “embody biases that exist independently, and usually prior to the creation of the system.” And they arise as well from the nature of the technology itself “and the attempt to make human constructs amenable to computers.”

These distortions are compounded when psychological models, legal standards and managerial processes are turned over to programmers for translation into predictive algorithms and computer code. These programmers may know nothing of the psychological models, legal standards and management processes and their translation efforts are colored by their own disciplinary assumptions, the technical constraints of requirements engineering, and limits arising from the cost and capacity of computing.

Managerial processes may be poor vehicles for capturing nuance in legal policy and psychological models, especially in a context like employment discrimination where regulators have eschewed rules for standards and where the interpretation of those standards by regulators and psychological professionals may change over time. For example, the ADA prohibits pre-employment medical examinations and psychological tests used by workforce analytic companies may be considered medical examinations (please see ADA, FFM and DSM). Employers and workforce analytic companies have interpreted the medical examination requirement as prohibiting the use of tests that are designed to diagnose mental illnesses. 

This interpretation creates two significant risks for employers and workforce analytic companies. First, the legal standard does not speak to "tests designed to diagnose mental illnesses;" rather, it is "whether the test is designed to reveal an impairment of physical or mental health such as those listed in the Diagnostic and Statistical Manual of Mental Disorders." "Designed to reveal" is semantically and substantively different from "designed to diagnose" and, as set out in Employment Tests are Designed to Reveal an Impairment, Big Five-based tests are designed to reveal impairments by their screening out process. As depicted in the graphic, tests designed to diagnose are a subset of the overall category of tests designed to reveal an impairment.

Using the "designed to diagnose" category as the proxy for medical examinations puts employers and workforce analytic companies at significant risk of violating the ADA medical examination prohibition and the confidential medical information safeguards under the ADA. It may also result in other claims against the workforce analytic companies by job applicants, employers and insurers. The mistaken use of the "designed to diagnose" category as a proxy for medical examinations could be considered a design defect in the product liability arena or as negligent in a tort claim.

The second significant risk for employers and workforce analytic companies arises from their failure to account for the evolution of the Big Five model from non-clinical model to clinical model.  In her seminal review of the personality disorder literature published in 2007, Dr. Lee Anna Clark stated that “the five-factor model of personality is widely accepted as representing the higher-order structure of both normal and abnormal personality traits.” A clear sign of this evolution comes with the publication of the most current volume of the Diagnostic and Statistical Manual for Mental Disorders (DSM-5), published in May 2013, where the model used to diagnose many personality disorders is based on the Big Five. 

Consequently, even if the standard for defining a medical examination was focused solely on the use of a test that diagnosed a mental illness, the five-factor model has now evolved into a diagnostic tool used by the psychiatric community to define mental impairments, including personality disorders, of the kind set out in the DSM-5. The failure of employers and workforce analytic companies to account for the evolutionary development of the five-factor model puts them at significant risk due to their belief that the five-factor model is not a diagnostic tool - a belief that time and scientific advances have now overturned. 

A Primer on Big Data and Hiring: Chapter 1

This is the first chapter of a primer on big data and hiring. The structure of the primer is based on the following graphic created by Evolv, a company that provides "workforce optimization" services. Evolv was selected not because it is sui generis; rather, it is emblematic of numerous companies, from start-ups to well-established companies that market "workforce science" services to employers.

The Evolv graphic below is intended to illustrate the process of workforce science.

Chapter 1: New and Existing Data:

Companies capture and store workforce data.

Most companies have vast amounts of HR data (employee demographics, performance ratings, talent mobility data, training completed, age, academic history, etc.) but they are in no position to use it. According to Bersin by Deloitte, an HR research and consultancy organization, only 20% of the companies believe that the data they capture now (let alone historically) is highly credible and reliable for decision-making in their own organization.

The complexity of working with myriad data types and myriad, often incompatible, systems was underscored by Dat Tran of the U.S. Department of Veterans Affairs at the 2013 MIT Chief Data Officer and Information Quality Symposium. "The VA does not have an integrated data environment; we have myriad systems and databases, and enterprise data standards do not exist. There is no 360-degree view of the customer," Tran said in a discussion of the obstacles facing an agency dealing with 11 petabytes of data and 6.3 million patients.  

"Bad data" is data that has not been collected accurately or consistently or the data has been defined differently from person to person, group to group and company to company. In the recruitment and hiring context, unproctored online tests allow an applicant to take the test anywhere and anytime. 

That freedom creates conditions ripe for obtaining "bad' data. As stated by Jim Beaty, PhD, and Chief Science Officer at testing company Previsor:

Applicants who want to cheat on the test can employ a number of strategies to beat the test, including logging in for the test multiple times to practice or get the answers, colluding with another persons while completing the test, or hiring a test proxy to take the test.

And what about the accuracy of tests responses from those who are hired? Analyzing a sample of over 31,000 employees, Evolv found that employees who said they were most likely to follow the rules left the job on average 10% earlier, were 3% less likely to close a sale and were actually not particularly good at following rules.

Decision-makers increasingly face computer-generated information and analyses that could be collected and analyzed in no other way. Precisely for that reason, going behind that output is out of the question, even if one has good cause to be suspicious. In short, the computer analysis becomes a credible reference point even though based on poor data.

Zappos Insider: The Death of Job Postings and the Rise of the Borg

As set out in a prior post, Zappos has launched a new careers site and removed all job postings. Instead of applying for jobs, persons interested in working at Zappos will need to enroll in a social network run by the company, called Zappos Insiders. The social network will allow them to network with current employees by digital Q&As, contests and other means in hopes that Zappos will tap them when jobs come open.

"Zappos Insiders will have unique access to content, Google Hangouts, and discussions with recruiters and hiring teams. Since the call-to-action is to become an Insider versus applying for a specific opening, we will capture more people with a variety of skill sets that we can pipeline for current or future openings," said Michael Bailen, Zappos’ head of talent acquisition.

The idea, in part, is that recruiters will monitor the interactions, and alert promising applicants as job positions become available. "Our recruiters are focusing on proactive sourcing…so that we know EXACTLY who we want to interview once a position becomes available," Stacy Donovan Zapar, Zappos' Social Recruiting and Employer Branding Strategist, wrote in a posting  (Big News: The Day the Posting Died) explaining Zappos Insiders.

We are the Borg. You will be assimilated.

For those not Star Trek fans, Borgs are beings that are part organic and part artificial. They assimilate almost everyone they come in contact with into the Borg - a mass consciousness - and those persons become efficient drones who do not even remember their old life. A quote from one of the episodes:

"We are the Borg. Lower your shields and surrender your ships. We will add your biological and technological distinctiveness to our own. Your culture will adapt to service us. Resistance is futile.”

Assimilation by the Borg and the Zappos Insider program seem to have some potentially troubling similarities (though one might suggest that a Zappos Borg might be able to really "Deliver WOW Through Service" - Zappos' first core value ...). Zappos' gathering of personal information/borg assimilation process starts with a person's becoming a member of Zappos Insider. For example, a person may sign up to be a Zappos Insider by linking to their FaceBook or LinkedIn accounts. If signing in using a LinkedIn account, you are asked to allow Zappos the following accesses:

• Your full profile
• Your email address
• Your connections
• Your contact info
• Invitations and messages
• Company pages

If signing in using a Facebook account, you are asked to allow Zappos the following access:

• Your public profile
• Your friend list
• Your email address
• Your News Feed
• Your friends' likes

Once a person becomes an Insider, the information collection/borg assimilation process continues as Zappos tracks and records Insiders' interactions with the company - from discussions with recruiters and hiring teams, to Google Hangout participation, conversations with Zappos "ambassadors," to digital Q&As, to contests, etc. 

Borg Assimilation: All Good, Right?

On March 12, 2014, a panel of experts addressed the EEOC at a meeting held at EEOC headquarters in Washington and discussed the growing use of social media and how it impacts the laws the EEOC enforces. In her testimony at the meeting, one of the experts, Lynne Bernabei, Esq. stated:

Surveys of hiring managers show that they are increasingly using social media to screen job candidates, and that employers may make a determination on an employee's suitability for a job opening based on the content of his or her social networking website. However, social media websites also display "non-job-relevant information that could be used inappropriately for evaluating applicants, resulting in biased hiring decisions," and "the potential for legal liability is great, considering the dearth of research regarding whether [social network-] derived information validly predicts job performance." ... Marital status, family and childcare obligations, home location (and therefore length of commute), and even sports team affiliation may directly or indirectly affect an employer's willingness to hire a given candidate-and all of this information is easily accessible on Facebook.

In response to a March 25, 2012 letter from Senator Charles E. Schumer and Senator Richard Blumenthal, the EEOC stated that covered employers - like Zappos - may not use personal information from social networks to make employment decisions on a prohibited basis, be it race, color, religion, national origin, sex, age, disability, or protected genetic information. 

For example, the letter explained, rejecting an applicant because he is Muslim, as disclosed through social media postings, would violate Title VII of the Civil Rights Act of 1964, as amended (Title VII). Similarly, rejecting an individual with a family history of breast cancer, as shown in the caption to a photo of her completing the "Race for the Cure" in memory of her sister "who lost her battle with this horrible disease," would violate the Genetic Information Nondiscrimination Act (GINA). Finally, screening out an applicant because her Facebook page has photos of her 50th birthday celebration, would violate the Age Discrimination in Employment Act (ADEA).

The EEOC's May 2012 response to Senators Schumer and Blumenthal also discussed another important topic: pre-employment inquiries about protected status. The laws enforced by the EEOC have differing approaches to this issue. Title VII and the ADEA do not explicitly prohibit questions about race, gender, national origin, religion, or age 40 or above, but if an EEOC investigation shows that an employer inquired about these matters (for example, by viewing Facebook or LinkedIn profiles and profile pictures), the EEOC will assume that the employer considered the information relevant to its employment decision and will scrutinize the facts closely. By contrast, GINA and Title I of the Americans with Disabilities Act (ADA) explicitly provide that making a pre-employment disability-related inquiry, or requesting or requiring genetic information, constitute independent statutory violations subject to certain conditions and exceptions

Anything Particularly Problematic?

Renée Jackson, Esq., a lawyer who led the Social Media & Technology in the Workplace team within the Labor and Employment practice group at Nixon Peabody LLP, was one of the experts testifying at the March 12, 2014 EEOC meeting mentioned previously. Nixon Peabody LLP counsels employers and their clients range greatly-in size, location, and industry. 

A portion of Ms. Jackson's testimony is set out below, the portions of her testimony that may prove challenging for Zappos and the Zappos Insider program are highlighted in red. Advice from Ms. Jackson that may be particularly problematic for Zappos Insider are bolded:

I advise employers wishing to supplement traditional hiring practices with social media to consider the following options for compliance:

• Use social media as part of a larger recruitment plan, including both traditional and new media, in-person networking events, job fairs, and referrals.
• Have a third-party consumer reporting agency or a designated individual within the company (who does not make hiring decisions) conduct the social media background check and filter out any protected class information.
• When using social media information during the hiring process, confirm that the information obtained was actually created by or relates to the applicant (as opposed to someone else with the same name), and disregard any information that cannot be confirmed as accurate.
• Only review publicly available information about an applicant. Do not request usernames/passwords or otherwise try to gain access to non-public or password-protected social media.
• If possible, screen all applicants in a uniform and consistent manner. Have human resources conduct the search at the same stage of the hiring process for each candidate (before the offer of employment, for example), rather than have hiring managers randomly conducting searches on their own.
• Avoid recruiting solely from social media or limiting applicants to applying only through social media or only with their social media profile.
• Avoid making hiring decisions based on an applicant's lack of a social media presence, unless a social media presence is required for the position (e.g., employer is hiring a social media manager for the company).
• Make sure those responsible for hiring know the applicable protected classes (federal, state, and local). Train all supervisors and HR professionals on how to lawfully use social media during recruiting and hiring. Support all hiring decisions with legitimate, non-discriminatory reason(s) and documentation.

Ms. Jackson closed her testimony noting, “Employers must understand that the technology will always outpace the law and, as a result, employment policies and practices must be constantly revisited and updated accordingly. On the flip side, however, many of the employment law concepts that employers are already familiar with can be applied and should not be forgotten just because the spaces are new.”

Michael Bailen, who heads talent acquisition for Zappos, told the Wall Street Journal that he is surprised that Zappos appears to be the first to drop job listings. “We’re hoping a lot of other companies jump on board,” he said. For reasons set out in this and two prior posts (Zappos Hiring: Culturally Fit? Legally Defensible? and Zappos: The Future of Hiring and Hiring Discrimnation), Zappos may find other employers unwilling to adopt this practice. 

Zappos: The Future of Hiring and Hiring Discrimination?

Zappos has launched a new careers site and removed all job postings. Instead of applying for jobs, persons interested in working at Zappos will need to enroll in a social network run by the company, called Zappos Insiders. The social network will allow them to network with current employees by digital Q&As, contests and other means in hopes that Zappos will tap them when jobs come open.

"Zappos Insiders will have unique access to content, Google Hangouts, and discussions with recruiters and hiring teams. Since the call-to-action is to become an Insider versus applying for a specific opening, we will capture more people with a variety of skill sets that we can pipeline for current or future openings," said Michael Bailen, Zappos’ head of talent acquisition.

The idea is that recruiters will monitor the interactions, and alert promising applicants as job positions become available. "Our recruiters are focusing on proactive sourcing…so that we know EXACTLY who we want to interview once a position becomes available," Stacy Donovan Zapar, Zappos' Social Recruiting and Employer Branding Strategist, wrote in a posting explaining Zappos Insiders.

Ideally, the Zappos Insider strategy will result in an engaged, talented applicant pool from which the company can seamlessly fill positions. More importantly, at a company where cultural fit is 50 percent of the hiring equation, encouraging candidates and current employees to interact organically allows the company to gauge whether someone is a good fit before the hiring process officially begins, theoretically saving both time and money.

Zappos’ Bailen said he is surprised that Zappos appears to be the first to drop job listings. "We're hoping a lot of other companies jump on board," he said, but there are compelling reasons why Zappos may find itself alone.

Bias and Discrimination

In a comment to Donovan Zapar’s posting explaining the Zappos Insider initiative, Steve Kuzner asked:

Ultimately, the [Zappos Insider] policy makes the hiring process less transparent for job seekers, who will no longer even know there is an open position until they are deemed worthy to apply for it. While well-intentioned, such opacity provides a shield under which bias, conscious or not, can potentially flourish.

Donovan Zapar responded, in part, by acknowledging that there are laws prohibiting an employer from basing hiring decisions on the race, religion, national origin, gender, pregnancy, disability, genetic identity, or age of applicants (Protected Persons). That response is correct, as far as it goes, but it fails to address the broad scope of Title VII of the Civil Rights Act of 1964 (Title VII), its progeny (Age Discrimination in Employment Act (ADEA), Americans with Disabilities Act (ADA), Genetic Information Non-Discrimination Act (GINA), etc.), their implementing regulations, and court decisions over the past fifty years.

In addition to prohibiting discrimination in hiring, employment laws prohibit discrimination against Protected Persons in regard to recruiting, the work environment, or any other term, condition, or privilege of employment.  Not only do the laws prohibit intentional discrimination, they also prohibit neutral policies that disproportionately affect Protected Persons and that are not related to the job and the needs of the business.

Job requirements must be uniformly and consistently applied to all persons. Even if a job requirement is applied consistently, if it is not important for job performance or business needs, the requirement may be found unlawful if it excludes Protected Persons significantly more than others. Examples of potentially unlawful practices include: (1) soliciting applications only from sources in which all or most potential workers are of the same race or color; (2) requiring applicants to have a certain educational background that is not important for job performance or business needs; and, (3) testing applicants for knowledge, skills or abilities that are not important for job performance or business needs.

[Dis]Connection

Donovan Zapa wrote in her post:

In preparation for this day, we've built a brand new "Inside Zappos" social recruiting presence over the past couple of months on Twitter, Facebook, Instagram, LinkedIn and Pinterest …. We've used these social channels to share our company culture, our people, our campus, our events, the way we do business and also to get to know the people who might like to work for us someday.

Similarly, in response to the question, “How can I stand out from the pack and stay front-and-center in the Zappos Recruiters’ minds?” on the Zappos' Insider site, the company lists six ways to stand out, including: using Twitter, Facebook, Instagram, Pinterest and Google Hangouts; participating in TweetChats; following Zappos’ employees on various social media platforms; and, reaching out to Zappos’  “team ambassadors.” For the most part, all of the foregoing activities require broadband internet access and devices (tablets, smartphones, etc.) that run on those access networks.  A number of Protected Classes will be challenged by both the broadband access and social media participation requirements:

• As noted  by a PewResearch Internet Project Research report, African Americans have long been less likely than whites to have high speed broadband access at home, and that continues to be the case. Today, African Americans trail whites by seven percentage points when it comes to overall internet use (87% of whites and 80% of blacks are internet users), and by twelve percentage points when it comes to home broadband adoption (74% of whites and 62% of blacks have some sort of broadband connection at home).  
• The gap between whites and blacks when it comes to traditional measures of internet and broadband adoption is pronounced. Specifically, older African Americans, as well as those who have not attended college, are significantly less likely to go online or to have broadband service at home compared to whites with a similar demographic profile.
• According to the PewResearch Internet Project, even among those persons who have broadband access, the percentage of those using social media sites varies significantly by age:

Age	Social Media Participation
18-29	90%
30-49	78%
50-64	65%

Social medial participation is not solely a function of age. "Social media is transforming how we engage with customers, employees, jobseekers and other stakeholders," said Kathy Martinez, Assistant Secretary of Labor for Disability Employment Policy. "But when social media is inaccessible to people with disabilities, it excludes a sizeable segment of our population." Persons with disabilities (e.g., sight or hearing loss, paralysis), whether physical, mental, or developmental, face challenges accessing social media. Each of the social media platforms promoted by Zappos - Twitter, Facebook, Instagram, Pinterest, and Google Hangouts - have differing levels of support for those with disabilities (e.g., close captions or real live captions on image content that utilize sound/voice). 

Career transition and talent development consulting firm Lee Hecht Harrison asked hundreds of job seekers via an online poll, “How active are you on social networking sites?” Only forty-eight percent said they're active on a daily basis, with an additional nineteen percent saying they log on about two or three times per week. This leaves thirty-three percent who use social media sites one to three times a month, if at all. 

As Aristotle wrote, “There is nothing so unequal as the equal treatment of unequals.” It is for this reason that employment laws like Title VII prohibit facially neutral policies (e.g.,  enrolling in Zappos Insider, necessitating broadband internet access, and using social media platforms with varying degrees of accessibility ) that disproportionately affect Protected Persons and that are not related to the job and the needs of the business. Zappos' Insider may be a club that is "open" to everyone, but African Americans, persons over forty, and persons with disabilities, among others, will have a harder time being admitted and will be limited in their ability to use all the club has to offer.

Donovan Zapa, in her posting announcing Zappos' Insider, wrote:

[O]ur recruiters are focusing on proactive sourcing, driving people to join our Insider program, having two-way conversations, meeting people, networking, chatting with Insiders, answering questions, engaging on social media, employer branding and proactive pipelining so that we know EXACTLY who we want to interview once a position becomes available. It's old-school recruiting, made new and fresh again. 

It is likely that "old school recruiting" was part of the reason that laws like Title VII, ADEA, ADA and GINA were enacted. Persons making hiring decisions knew exactly who they wanted to interview and that did not include persons of color, women, older Americans, persons with disabilities, and persons of certain faiths. While likely not the intent, Zappos' Insider platform makes discrimination against "those persons" new and fresh (and painful and illegal) again.