Tag Archives: Artificial Intelligence

How to Effectively Mitigate Unconscious Bias in Sourcing

With 78% of talent professionals and hiring managers saying that diversity is the top trend impacting how they hire, it is critical to take measures to remove as much bias from the hiring process as possible.

While there is no shortage of opinion that AI can be applied to reduce or remove human bias in hiring, there is a simple way of significantly mitigating unconscious bias at the top of the funnel without using AI and avoiding the risk of algorithmic bias: blind review and selection.

Blind Review and Selection

Whether you use “blind,” “anonymous,” “masked” or “obfuscated” to describe the technique, the goal and the end result are the same:  prevent sourcers, recruiters and hiring managers from being unconsciously biased when reviewing and selecting applicants, resumes or profiles when considering people for employment.

If you cannot see a person’s name, you are mitigating unconscious bias as you are prevented from having any easy insight into a person’s gender, race or ethnicity.

If you cannot see where a person went to school (school name or country), you are prevented from exercising any unconscious bias you might have towards or against specific schools, and you also help mitigate unconscious bias with regard to race and/or ethnicity.

If you do not show when someone graduated from school, and if you impose a limit to the maximum number of years of experience to be visible (e.g., 10 where the job does not require more than 10), you prevent people from being unconsciously biased against people who have more than the required years of experience, and this can effectively combat ageism.

Solutions Pushing the Envelope

The good news is that there are already some solutions on the market today that offer blind results review and selection.

Eightfold.ai is a company I had the distinct honor of consulting with back in the fall of 2016. At the time I advised the team of what I believed to be a significant opportunity for their solution to help with diversity and inclusion. Since then, they have made many advancements in this space, including blind review and selection.

With Eightfold.ai, you can configure their solution to mask several profile elements that can mitigate unconscious gender, race, ethnicity, and age bias – and this includes people from within your ATS/CRM:

  • Name
  • Social Media/Picture
  • Communication history
  • Specific location
  • Name of school
  • Date of graduation
  • More than 8 years of experience

Here is an example of a masked profile in their system:

In case you were wondering, that person is a woman and she graduated with a Bachelor’s degree from a top university in India. But with the masked profile, it could just as easily be a man who graduated from Stanford, so there is no way a person could use the name and school/country to consciously or unconsciously discriminate against her.

So does this work?

The Eightfold team informed me that they recently completed a pilot with a multi-national company in which it was clear that for a specific set of roles, hiring managers preferred one gender over the other by 50% when selecting candidates for phone screen prior to rolling out the masked screening process. After the rollout of masked screening, there was virtually no difference in the selection rate between gender.

Entelo was an early mover in facilitating external diversity sourcing, and they have recently announced their “unbiased sourcing mode” where users are able to anonymize and hide many elements of profiles that are commonly associated with unconscious bias, including employment gaps and substituting gender-specific pronouns throughout the profiles.

Spire is another solution with blind review and selection capabilities, allowing you to match your applicants and other candidates to jobs without seeing names, pictures, universities, date of graduation or years of experience greater than required.

Another solution I am aware of that offers similar functionality is SeekOut. If you are aware of others, please let me know, and I would be happy to share them.

A Call to All HR Technology Solution Providers

I believe configurable blind review and selection should become a standard and required feature of any HR technology solution that involves reviewing applicants or resumes/profiles when considering people for employment, including internal mobility.

Without it, users are fully prone to the effects of unconscious bias when it comes to reviewing and selecting (or not!) applicants and potential candidates.

While blind review and selection don’t address unconscious bias that can creep into the interview and offer stages, it can practically eliminate it from the top of the talent funnel, leading to more diverse applicants and candidates getting into the hiring process in the first place.

If you caught my mention of algorithmic bias in the beginning of this post, stay tuned as I will be writing about the risks associated with using AI in sourcing and recruiting soon.

Video: Discussing AI in Sourcing and Recruiting

I recently had the chance to participate in a Google Hangout with Jeremy Roberts of HiringSolved as an introduction to the speakers of their upcoming conference on Feb 7 in NYC (HIREconf).

You can watch the recording of our chat here or play the video below to learn a little about my background and my thoughts about the evolving role of technology and specifically artificial intelligence solutions when it comes to sourcing and recruitment, which will be the topic of my opening keynote at HIREconf. I’m planning on addressing how intelligent machines are changing talent acquisition and how sourcers and recruiters can prepare for today and tomorrow.


The folks at HiringSolved have really put together a solid list of speakers for their 1 day event in NYC:

Here’s a peek at the agenda:


If you can make it, I’d love to see you there!

Talent Sourcing: Man vs. AI/Black Box Semantic Search

Back in March 2010, I had the distinct honor of delivering the keynote presentation at SourceCon on the topic of resume search and match solutions claiming to use artificial intelligence in comparison with people using their natural intelligence for talent discovery and identification.

Now that nearly 2 years has passed, and given that in that time I’ve had even more hands-on experience with a number of the top AI/semantic search applications available (I won’t be naming names, sorry), I decided it was time to revisit the topic which I am very passionate about.

If you’ve ever been curious about semantic search applications that “do the work for you” when it comes to finding potential candidates, you’re in the right place, because I’ve updated the slide deck and published it to Slideshare. Here’s what you’ll find in the 86 slide presentation:

  • A deep dive into the deceptively simple challenge of sourcing talent via human capital data (resumes, social network profiles, etc.)
  • How resume and LinkedIn profile sourcing and matching solutions claiming to use artificial intelligence, semantic search, and NLP actually work and achieve their claims
  • The pros, cons, and limitations of automated/black box matching solutions
  • An insightful (and funny!) video of Dr. Michio Kaku and his thoughts on the limitations of artificial intelligence
  • Examples of what sourcers and recruiters can do that even the most advanced automated search and match algorithms can’t do
  • The concept of Human Capital Data Information Retrieval and Analysis (HCDIR & A)
  • Boolean and extended Boolean
  • Semantic search
  • Dynamic inference
  • Dark Matter resumes and social network profiles
  • What I believe to be the ideal resume search and matching solution
Enjoy, and let me know your thoughts.

The Future of Sourcing and Talent Identification

If you listen to certain people in the recruiting industry, you’d think that being able to leverage information systems for talent discovery and identification will be an obsolete skill for recruiters and that sourcers will have to find another profession in the near future.

According to these folks, people with sourcing skills won’t be necessary because the future of sourcing will lie in total automation – they believe that applications that employ semantic search, AI and NLP (Natural Language Processing) will be able to perform the entire candidate matching process for you.

However, neither Watson, Artificial Intelligence, Natural Language Processing nor semantic search will be putting any sourcer or recruiter out of a job anytime soon unless all they’re doing is basic keyword and title searching. Continue reading