Removing Unnecessary Employment Barriers

Let’s play some DE&I trivia!  As many of you know, the landmark case in employment discrimination is Griggs v. Duke Power.  But, what was the aspect of Duke Power’s hiring that got them into court?

If you said their use of pre-employment tests, you’d only be partially right.  The decision was also based on the use of discriminatory educational requirements (in this instance, a high school diploma).  Interestingly, after that tests got a bad name, but companies continued to use school credentials with little or no problem.

As the US economy and culture pushed more and more students towards college, racial disparities in educational attainment have persisted.  Yet, companies rarely questioned whether asking for high school or college degrees for certain jobs really gets them better candidates.  In some cases, this requirement is a classic “like me” bias?

Of course, the only way to see if a high school or college degree is necessary for a job is to conduct a job analysis and compare that knowledge, skills, and abilities with a high school or college curriculum.  Yes, I want my surgeon to have an MD, thank you very much. Far too often companies have used degrees as a de facto job requirement without ever thinking about its impact on organizational performance (are we turning away qualified people?) or fairness.  This is particularly true in IT where there are many self-taught people in the field.

Due to a confluence of factors, some big companies have rethought their use of degrees as qualifications.  Besides this leading to potentially more diverse hiring, it will also save them money (but be an economic boom to the new hires).  Whether it would lead to less college enrollment and lower higher education costs is certainly possible.  More importantly, it would lead to a paradigm shift of associating all white collar jobs with college degrees.

One can argue that getting a college degree shows tenacity and commitment over a long period of time.  And I would agree.  But, there are other ways to show this as well.

Change only comes when we do things in a different way.  And solutions to long term problems often require big actions.  Removing high school or college degrees as job qualifications when they are unnecessary removes a significant barrier to employment for racial minorities that could have an impact at your company.

Does AI Mean Bias in Hiring?

Using artificial intelligence (AI) in hiring has grabbed the attention of law makers in California, New York, and other states.  The gist of the proposed California law is that any AI used in hiring would have to show no adverse impact before implementation, adverse impact would have to be reviewed annually, and they could not be used if found to have adverse impact.  Exceptions for business necessity would still be made.  Also, the test could still be used if it had adverse impact but the impact was less than a previously used process.  The proposed law would represent an extension of the federal anti-bias laws that apply to pre-employment tests.

The proposed New York law requires greater transparency in the use of AI in selection.  Like the California bill, AI assessments would have to demonstrate a lack of bias before being used.  Companies would have to disclose to candidates when they use AI technology for hiring and the specific job qualifications or characteristics the AI is evaluating.  I have no idea how the latter helps reduce discrimination.

There are some important impacts and nuances to these proposed laws:

  1. The California law uses the 4/5ths rule as the determiner of adverse impact.  Federal agencies are moving away from this standard to one that focuses more on the scored differences between groups.  I would advise using the latter standard when assessing adverse impact.
  2. The laws ask that the AI be pre-tested for adverse impact, but interestingly, not validity, unless there is AI.
  3. Speaking of the pre-tests, it may be a challenge for employers to demonstrate lack of adverse impact on applicant groups since they are not required by law to provide information on their race, sex, or age when taking a test.  Of course, if video interviewing is part of the AI, then the system can gather that information.
  4. I found it interesting that companies could use systems that had adverse impact as long as they had less bias than the previous test.  This is not exactly an incentive for developers to create fair tests.

There’s some common sense and science to using any selection system, especially one that is strictly data driven, including:

  1. Know what it is scoring.  If the AI algorithm doesn’t make sense, then it is likely taking advantage of something unusual in the data from which it was developed.  This means that it is unlikely to be a valuable predictor in the future.  And, I don’t think you want to tell a jury why you are hiring people based on how many times they tug their ear lobe during an interview.
  2. Look closely at missing demographic data.  As I mentioned above, providing this information is optional for applicants and plenty of people will not give it.  A vendor should be able to tell you about their experience with missing cases and how it affects your adverse impact.
  3. Machine based scoring models are geared towards finding very small, but consistent, differences among people.  This is valuable it you hire 100,000 people a year (or trying to get someone to buy a different brand of soap), but less so in most circumstances.  Be sure to evaluate the business impact of the scoring model, particularly those elements that may have adverse impact.
  4. You can always ask the vendor to only use scoring elements that do not have adverse impact.  If it also turns that the assessment doesn’t have any validity, then it’s time to find a new test.

There’s no inherent reason to be suspicious of AI in selection.  However, we also do not want to rely on “black boxes” either.

Training Hiring AI Not to be Biased

Artificial Intelligence (AI) and Machine Learning (ML) play integral roles in our lives.  In fact, many of you probably came across this blog post due to a type of one of these systems.  AI is the idea that machines should be taught to do tasks (everything from search engines to driving cars).  ML is an application of AI where machines get to learn for themselves based on available data.

ML is gaining popularity in the evaluation of job candidates because, given large enough datasets, the process can find small, but predictive, bits of data and maximize their use.  This idea of letting the data guide decisions is not new.  I/O psychologists used this kind of process when developing work/life inventories (biodata) and examining response patterns of test items (item response theory—IRT).  The approaches have their advantages (being atheoretical, they are free from pre-conceptions) and problems (the number of people participating need to be very large so that results are not subject to peculiarities about the sample).  ML accelerated the ideas behind both biodata and IRT, which I think has led to solutions that don’t generalize well.  But, that’s for another blog post.

What is important here is the data made available and whether that data is biased.  For instance, if your hiring algorithm includes zipcodes or a classification of college/university attended, it has race baked in.  This article has several examples of how ML systems get well trained on only the data that goes in, leading to all kinds of biases (and not just human ones).  So, if your company wants to avoid bias based on race, sex, and age, it needs to dig into each element the ML is looking at to see if it is a proxy for something else (for instance, many hobbies are sex specific).  You then have to ask yourself whether the predictive value of that bit is worth the bias it has.

Systemic bias in hiring is insidious and we need to hunt it down.  It is not enough to say, “We have a data driven system” and presume that it is not discriminatory.  If the ML driving it was based on inadvertent bias, it will perpetuate it.  We need to check the elements that go into these systems to ensure that they are valid and fair to candidates.

I’d like to thank Dennis Adsit for recommending the article from The Economist to me.

Are Organizations Becoming Less Biased?

I’ve written quite a bit about bias in this blog. It is an important topic to me because I believe that people in HR and industrial psychology can be gatekeepers to a more fair society while improving organizational performance. Of course, bias in employment is merely an extension of what happens in the greater society. One of the assumptions about bias is that it is fairly stable so we have to almost trick people into being fair.

However, this study has some better news. Their analysis indicates that over a 20 year period bias against skin color and sexual orientation have been reduced. However, bias against weight has increased. Attitudes towards age and disability have stayed the same. Strangely, gender bias is not addressed.

The study raises many interesting questions about whether these changes are being experienced across demographic groups or only primarily within specific ones. However, it does provide some questions for HR practices, such as:

  • What steps can we take to reduce bias in hiring based on weight? Phone interviews instead of live ones?
  • Do we need to change our anti-discrimination training to focus more on weight and less on other issues?

The data does seem to point to those characteristics that we perceive as choices (being overweight) as having stronger biases than those that we have always perceived as innate (skin color) and those that the culture is now thinking of as such (sexual orientation).

Each organization can see where its implicit bias “blind spots” are by analyzing its hiring and promotional data. I understand that this can lead to some unkind truths. But, it will also allow for focus on areas where bias can be reduced.

Addressing the Last Mile of Discrimination

Many organizations have taken steps (valid tests, removing pictures and names from resumes, blind auditions, etc.) to erase discriminatory practices in hiring.  While by no means perfect, these actions have reduced bias in many places.  A much lauded effort was done in orchestras.  They switched to auditions where the players were behind a screen (and walked across carpet so that shoes would not be a gender giveaway).  This led to a much greater number of women being hired by major orchestras.

Where many organizations now struggle is in pay equity.  It is not secret that equally qualified women make less money than men.  To help correct this, several states and cities have made it illegal for prospective employers to ask about salary history, which historically would be skewed against women.

Of course, what to pay someone has a myriad of factors involved (supply/demand, internal pay equity, recruitment strategy, etc.).  But, organizations with specialized talent face an even trickier task when explaining pay differences between men and women.  Like the Boston Symphony Orchestra (BSO).

The BSO conducts blind auditions, etc, but make job offers and negotiate salaries face-to-face.  This process has led the lead flautist to sue them because the lead oboe player (who is male) makes about $70k per year more than she does.  See this for a fascinating insider’s view of the suit and how these top-flight orchestra musicians get paid. What it comes down to is that the plaintiff says, “I do essentially the same work as him and I’ve been featured in the orchestra more, so there is no reason that I should be paid less than him.”  The orchestra responds, “That may or may not be true, but lead oboists are harder to find and retain compared to flutists, therefore we have to pay him more to retain his services.”

I would think that more pay equity laws will be enacted in the near term.  Organizations would be wise to review whether they have pay equity, and if they do not, make corrections where it exists.  Or provide a pretty good explanation for where it does exist.  Similarly, providing specific pay-for-skill and/or pay-for-scarcity explanations will go a long way towards pay equity.  And may prevent a sad song from being played on the way out of the courthouse.

Is Age Discrimination a Result of Change?

A class-action age discrimination lawsuit has been filed against IBM.  Much of the complaints in the action come from a report that purports to outline how the company has systematically replaced older workers with newer ones.  IBM is denying the allegations.

There are a couple of compelling issues here.  One is whether IBM is using sly methods to rid itself of older (read: more expensive) workers.  The other is whether workers who are in mid-career are technologically behind their younger counterparts in a meaningful way.  I’ll leave the former to the courts.  I’m much more interested in the latter.

There are some national studies that indicate that openness to new experiences does decrease with age.  However, the ability to learn does not. So, we can assume that older employees who are open to learning new technologies can certainly do so.

Whether it is how we get to a friend’s house or how we use technology, most of us like to stick to what we know and adapt to change in ways that keep our patterns of behavior.  To keep up-to-date on new technology or techniques not only requires a desire to learn, but also the willingness to give up what we have been good at.

There probably is not data to support the idea, but I am guessing that the hiring strategy at many companies is that they would rather select people who know the new stuff rather than try to train for it. If companies decide that they want to bring on those who have experience with newer technology, their layoff/hiring practices will likely show adverse impact against those 40 and older.

A person’s background is instructive in this area.  For people who have stayed up to date on technology throughout their careers, it is foolish to assume that they will not pick up (or haven’t already picked up) on the next new thing.  As such, I don’t believe that they are behind younger workers.  Senior management would have reason to be concerned about older workers who have not shown a willingness to update their skills.

Are older workers less likely to adapt to new technologies?  On the whole, probably.  However, painting them with a broad brush is likely a mistake.  Companies should do a thorough evaluation of the experienced talent before making decisions that can land them in court.

Is College Recruiting Ageist?

When we hear about age discrimination employment lawsuits, they are typically centered on older workers being let go when a company reorganizes so that less expensive (e.g., younger) employees are retained. Of course more subtle examples of ageism are present in workplaces and we need to be as aware of them as we are of bias against women, the LGBTQ+ community, and racial minorities.

Recently, the US District Court in California allowed an age discrimination case to proceed as a class action. As summarized here, the plaintiffs claim that the company used only college recruiting to bring on entry level hires, hence discriminating against potential hires who were not in college (re: people of 40). As evidence, they present that web postings of the positions only appeared through college recruiting sites and not on their regular career site and that resumes from older workers were regularly rejected. They also argue that the company has a general culture which values younger workers over older ones. The company counters these arguments by saying their process is merit-based and that given the number of candidates who apply, using the current process makes business sense.

There are several aspects of this case which are interesting and instructive:

1) There is nothing inherently wrong with college recruiting, especially for entry level jobs. However, if this is the ONLY way a person can get into the pipeline, by definition you are primarily looking at candidates in their 20s.

2) It shows a presumption that older workers will not take entry level positions. That may be true in some situations, but it is really up to the job candidate to make that determination. If an entry level job pays well relative to the experience necessary, why wouldn’t an older worker take it?

3) Like many class action suits, the statistical data will be a key point in determining if there was adverse impact against those age 40 and older. If, as the company claims, only 3% of college candidates get hired (I can see a huge legal argument about who was an applicant and how many there were), the plaintiffs will have to show that fewer than 2.4% of older candidates (again, a fight over who were actually applicants) were hired for the positions. That seems like a pretty low bar to get over.

4) The company’s second argument that college recruiting is efficient, therefore is OK even if it does discriminate (which they argue it does not), will be a tough one to make. Civil rights laws allow neutral selection techniques to have adverse impact if they are job related, but make no exclusions based on expense. I honestly do not see how this is relevant to the complaint.

This case will take years to wind its way through the courts. However, it does provide a timely reminder to review recruiting processes and valid selection tools for adverse impact based on age and not only race and gender. College recruiting is not in and of itself ageist, but you will want to be sure that it is not the only point of entry into your company.

But we Trained Them!

Workplace controversies that make headlines are a bonanza for corporate trainers. Even in states like California that have mandatory sexual harassment training (companies with 50 or more employees are required to provide all supervisors two hours of sexual harassment prevention training within six months of hire or promotion, and every two years thereafter), you can bet that the #MeToo movement has led to an explosion in programs for managers devoted to the topic.

While providing basic information about sexual harassment is a good thing, it is more of a “check the box” activity than a creator of change. The underpinnings of what made it allowable and tolerated run deeper than what can be addressed in a two-hour mandatory training session or firing a couple of executives for egregious behavior. So, how can a company create an environment where incidents of sexual harassment are reduced?

1) Recruit, hire, and promote qualified women. Sociologists tell us that the roots of harassment are power differences. Having women and men participate in an organization with equal footing will likely reduce harassment incidents. Oh, and while you are at it, equal pay based on skill and experience goes a long way.

2) Reward at least some of the means, not just the ends. Cultures that have a win at all costs mentality are prime breeding grounds for harassment. If an organization only focuses on results, top producers can rationalize and get away with more bad behavior. Consider rewarding important process indicators (voluntary turnover, complaints to HR, engagement survey results, etc.) as part of evaluating manager’s performance.

3) Apply corporate sexual harassment policies quickly and as intended. This is where training has benefit. If manager know the policy and implement it correctly, it tells employees that it is as important as other policies and procedures.

Sexual harassment in the workplace did not happen, nor will it disappear, overnight. Our challenge is to create cultures that strongly discourage it. And that takes more than a two hour training band-aid.

What Implicit Bias Looks Like

The idea of implicit bias has been making its way into the business vernacular.  It involves the attitudes or stereotypes that affect our understanding, actions, and decisions in an unconscious manner.  As you probably gathered from the definition, implicit bias is something we all have.  They are little mental shortcuts we have which can lead to discriminatory behavior.

Examples of implicit bias are found throughout the hiring process, including recruiting, interviews, and performance appraisals.  I think that you will find this interview very helpful in understanding how these biases creep into our decision making. 

It really breaks down the abstract to the actual behaviors and their impacts.

At this point of the blog is where I normally come up with a prescription of what to do.  The only problem is that there are no good empirical studies showing how to reduce implicit bias.  There are some lab studies with college students which support some short-term effectiveness, but some police departments swear that they are a waste of time.  So, the jury is still out.  But, there are some things you can do to reduce the opportunity for bias:

  • You can (mostly) decode gender out of job postings.
  • Take names off of applications before they are sent for review. The law requires that race, gender, and age information be optional on applications to help avoid discrimination.  For the same reason, you should redact names on applications and resumes before they are evaluated (if they are not already being machine scored).
  • If you are using pre-employment tests that do not have adverse impact, weight them more than your interviews, which are likely loaded with bias. If you insist on putting final decisions in the hands of interviewers, use a very structured process (pre-written questions, detailed scoring rubrics, etc.).

All humans have implicit biases—we want to be surrounded by our in-group.  A reduction in these biases, or at least fewer opportunities to express them, will likely lead you to a more diverse, and better performing, team.

Just Pay People for Their Work

There’s been much talk about the new department of labor rule that will require overtime pay for salaried employees making less than $47,476 (the current threshold is $23,660) starting December 1, 2016.  This threshold will now update every three years.  This has led to some typical hand-wringing about whether this will help these employees (it’s a big raise since this ceiling hasn’t been raised in 12 years and no one thought of putting in a cost of living increase) or hinder them (employers will cut out the positions).

Others are really concerned that this will hurt opportunities for younger professionals.  The logic is that if new salaried employees aren’t working 12-14 hour days that they can’t show the boss how much work drive they have.  Or, they’ll miss out on those only-in-the-movies serendipitous meetings with the El Jefe that will put their careers on the fast track.  One executive is quoted as saying, “You wan to bump into the boss at 8 o’clock at night.”

I’ve got an idea. Why doesn’t everyone just leave the office by, oh, 7 o’clock?  OK, this idea is somewhat outdated since even if everyone was at home, they would still be doing work on their phones.  But, at least they are at home.

Another school of thought says that with fewer unpaid hours, “…they will not receive sufficient career development or see timely advancement and/or promotions.”  This is hogwash.  Career development benefits the company and the employee and if everyone is working under the same rules employers will make the time.

Let’s be clear: The employers that work professional people this much and don’t pay overtime are no different than sweatshop operators, even if they think people are putting in the extra hours “of their own volition” (read: they had better or they will get fired)..  They want free labor and are upset that they are going to lose it.

I do get the “this is how we build a hard working culture” argument to a point.  Those that put in the extra hours (and, presumably, the highest results) get rewarded.  This is tied into, “Well, this is how I got to where I am” logic.  Where the problem lies is that it perpetuates promoting a homogeneous group of people (those with a poor worklife balance), which limits you ability to grow the best talent.  Not everyone who puts in a lot of hours is a high performer (don’t confuse activity with results).

If we are to value work in a capitalist economy we have to pay for it.  Convincing people to work overtime for nothing is coercion, plain and simple.  That breeds a culture of fear and taking advantage of others.  Are those your company’s values?

Thanks for coming by!

Please provide this information so we can stay in touch.