It’s no secret, HR controls vast amounts of information. Such data is regularly reviewed through the lens of talent analysis and people analytics. Technology enables HR to quickly gather and analyze vast amounts of historical employee data in an effort to make better, more informed business decisions.  There is a growing concern, however, that the use of historical data in personnel decisions is imbued with a bias inherent to such data. Data ultimately tells a story. In this session, we’ll discuss how to validate data to ensure the story told is not based on a bias causing a disparate impact. Learn about the current state of Information Management in AIIM’s latest report: http://info.aiim.org/2017-state-of-information-management

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Big Data
And its Use in Human Capital and
Workforce Planning Decisions
March 2017

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Hell…
…is for Real!

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Dante’s Nine Circles of Hell

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Was Dante a Recruiter?
Too experienced
Too weak
Moved around too
much
It hurts to read this resume
Experience is too
dated
We haven’t used that
skill in 15 years…
This is great, but…
Long periods of
unemployment
This guy’s held the same
job title for 10 years!
I’d rather have someone with
a higher level of education
Community college?
Really?
They’re asking for how much? Gee, I wish I made
that much when I was their age!

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Artificially
Intelligent Hiring
Manager…?
Ma
gic

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Prove the Ideal is Really Ideal…
Wait, What?
At best, Big Data identifies correlations not causation.

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Squaring Dante’s Circles is like
Herding Cats
Recruiting success factors
– Predictive success factors
• Skills measurements
• Years experience
• Educational achievements
• Job progression
– Ideal behavioral indices
• Self starter
• Independent
• Reliable

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And the EEOC is Watching
“Employers should be concerned with the disparate
impact of their employment-related data mining and
analysis….the first step is to show and look at the tool.
Does it cause a disparate impact? Once you get there,
the tool would be considered illegal if it does not
accurately predict success in the job.
“…indeed, if you do possibly have prejudices built into
the data, something might be validated as predicting
success on the job…. So there’s going to be a lot of
interesting thought that needs to be done and
technology work, really, around understanding how to
validate these kind of concerns.”
Carol Miaskoff, EEOC Assistant Legal Counsel, September 2014

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Disparate Impact
Office of Federal
Contract
Compliance
Programs
(OFCCP)
Equal
Employment
Opportunity
Commission
(EEOC)
Office of Labor-
Management
Standards
(OLMS)
Employment and
Training Admin
(ETA)
Employee
Benefits
Security
Admin (EBSA)
Office of
Disability
Employment
Policy
(ODEP)Occupational
Safety and
Health Admin
(OSHA)
Office of Workers’
Compensation
Programs
(OWCP)
Wage and
Hour
Division
(WHD)
Office of Public
Engagement
(OPE)
Department of Labor
Pending New
Secretary

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The Real World of HR

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“Born Digital, Stays Digital”
With regulatory focus on “bad business” practices
– Leading the pack – risky
– Lagging the pack – terminal
– The safe zone – benchmark
Find synergy between data and actionable insights
– Recognize the need to harness volumes of data
– Blend structured and unstructured data
– Establish and disseminate a single version of “truth”
– Develop techniques for timely analysis and interpretation
– Produce highly interactive data visualization

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Strategic Imperative
Business
Risks
Negative
Business
Impact
Organizational
Impact
AnalysisPeople
Technology
Processes

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Impact Analysis

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Questions to Be Answered
• Data ultimately tells a story… a story based on bias
– Has the technology been validated? If so, how?
– Is there a legal agreement with the Big Data Analytics Provider?
– How and what are we capturing?
– Is the data accurately reflecting optimum performance indices?
– How do we segregate “silly data” from actual predictive data?
– Analytically, is bias “baked into” the data?
– Can we reliably and consistently defend our determinations?
– Are we confident decisions are legally sound?
– Is obsolete, irrelevant, or biased data purged?

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In Summary, Dig Deeper
Think discriminatory pattern or practice bias
– “Hidden” or “subjective” bias
– “Horizon of conditions”
Are we prepared to defend by “show[ing] and look[ing]” at OUR
OWN tool?
– Can we confidently answers key questions?
– What about data storage and those hidden traps?
– Compliance reporting capabilities need to go beyond the standard report
capabilities…what does that really mean?
– Transparency of data is going to be vital moving forward.
While IT owns the tools, HR MUST own the Rules

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Thank You!
Presenter:
Cathleen M. Hampton
Hampton Resources, Inc.
(703) 794-9442
Email: Cathleen.Hampton@HamptonResources.com
Website: www.HamptonResources.com
LinkedIn Profile: http://www.linkedin.com/in/crihr/
Twitter: HamptonCM