AI in Staffing: Staffing Owners and leaders - Welcome, and thanks for joining!

AI in Staffing:
Legal Risks and Considerations

 Staffing Owners and leaders
 Welcome, and thanks for joining!

Sheri Tischer
 VP of Business Development - Staffing
Sheri brings to her role at TCI Business Capital over 15
years of front-line staffing experience and has an
authentic passion for the industry. She is responsible
for developing staffing industry partnerships and
driving our payroll funding solutions throughout the
nation.
Sheri’s leadership experience and understanding of
sales, recruiting, and operations in the staffing industry
allow her to better assist staffing Owners in getting their
financial needs met.
She serves on the Board of Directors for the Minnesota
Recruiting and Staffing Association. She is a mentor
and volunteer for the American Staffing Association,
and an active corporate partner with NAWBO-MN.

Learning Objectives:

1. Understand the risk associated with data security & bias

 with the use of AI

2. Analyze vendors for risks and shifts in liability

3. Strategize on uses of AI for recruiting and candidate

 suitability

Kate Bischoff

Kate Bischoff is an overly enthusiastic, sarcastic, and
opinionated management-side employment attorney and
human resources professional. She works closely with
management, HR folk, and technology companies to improve
organizations through training, policy, and investigation work
in addition to everyday advice and counseling. Prior to starting
her own business, Kate served as the HR Officer for Consulate
General Jerusalem and U.S. Embassy Lusaka, Zambia.

Kate has been recognized by The New York Times, CNN.com,
Wall Street Journal, USA Today, National Public Radio, and
other journalistic sources as a leading authority on
harassment, technology in the workplace, and employment
law.

Artificial Intelligence
• HR Tech is a $20 BILLION industry
• National Artificial Intelligence Initiative
 Act of 2020 defines as a “machine-based
 system that can, for a given set of
 human-defined objectives, make
 predictions, recommendations or
 decisions influencing real or virtual
 environments.”
• AI will affect every facet of the employee
 lifecycle
• Potential for new regulations & a new
 frontier for litigation

AI & Analytic Forms
• Text analytics
• Audio & video analytics
• Usage analytics
• Predictive modeling

Use Today
• Recruiting:
 – Analyzing candidate pools
 – Finding candidates
 – Background checks
• Employee:
 – Determining productivity
 – Monitoring for security risks
 – Determining potential
 – Compensation analysis
 – Potential flight risks
 – Succession planning
• Post-employment:
 – Use of confidential information
 – Network analysis for solicitation

Problems
• Are the factors correlations?
 – Particularly for machine learning as more
 learning affects the analysis
 – What is job-related?
• Is the data any good or useful?
 – “All HR data is bad data”
 – Contains multitudes of bias
 – Is the data accurate or complete?
• Will we know how it works?
 – Black boxes?
 – Ability to test or share it?
• How can we verify not biased?

The Law
• The law is bad & slow
• Discrimination
 – Disparate treatment doesn’t quite work
 – Disparate impact doesn’t quite work
 – Another option?
• Privacy
 – EU & California leading the law on the use of employee data
 – Allow employees to control their data to a certain extent
 – If using private data, does the employee/candidate know &
 give meaningful consent?
 – Do they understand what it is they are consenting to?
 – Get around the regulations by anonymizing?

How the Bill of Rights Works
• Two-part test:
 – Automated systems that
 – Have the potential to meaningfully impact the
 American public’s rights, opportunities, or access to
 critical resources or services
• Civil rights & liberties
• Privacy
• Equal opportunities
• Critical resources & services

California Consumer Privacy Act
• Privacy law designed to ”give agency” to
 individuals over their own data
• Includes data collected by employers
• Rights
 – To know
 – Opt out of sale or sharing
 – Opt out of automated decision making
 – Correction
 – Deletion
 – Limit use of sensitive

NYC AI in Employment Decisions
• Subject automated employment decision tools to a
 bias audit within one year of its use
• Ensure that the results of such audits are publicly
 available
• Provide particular notices to job candidates
 regarding the employer’s use of these tools
• Allow candidates or employees to potentially
 request alternative evaluation processes as an
 accommodation
• Enforcement delayed until April 2023

Amazon Example
• Amazon is huge into automation
• Reviewing resumes/applications is a long, labor-
 intensive endeavor
• Review 100 candidates, spit out the top five
• The tool learned Amazon preferred men
• Tried to fix this – remove proxies for gender
• Still a problem
• Scrapped the program in 2018
• Some tech company is trying to do this

Privacy: Illinois AI Interview
• Notify applicants that AI will be used in their video
 interviews.
• Explain to applicants how the AI works & what
 characteristics the AI will be tracking in relation to their
 fitness for the position.
• Obtain the applicant’s consent to use AI to evaluate the
 candidate.
• Only share the video interview with those who have AI
 expertise needed to evaluate the candidate and must
 otherwise keep the video confidential.
• Employers must comply with an applicant’s request to
 destroy their interview video within 30 days

HireVue
• HireVue is a video-interviewing platform
 that added AI to conduct analysis on the
 videos themselves
• Word choice, word complexity, facial
 analysis, eye contact all used as factors to
 determine candidate rating
• Finally, conducted analysis on whether bias
 could play a part
• it did!!
• Announcement 1 year after stopped using it

To Ask A Vendor
• Has the process demonstrated adverse impact?
• What validation evidence has been collected to establish
 the job relatedness of the algorithm? For each job?
• Does the validation evidence comply with the
 requirements of UGESP? Get a copy of the validation
 study
• Does it use an adverse bias mechanism to test for bias?
• What is the data security?
• How anonymized?
• What kind of ongoing monitoring do you provide as we
 continue using it?
• Contracting:
 – Vendor provides supervised training
 – Required to provide information if litigation or agency review
 occurs
 – Avoid indemnification language or ask for your org to be
 indemnified

Takeaways

• AI is coming
• So are the laws & regulations… eventually
• Assess where you’re at right now
 – What data do you or your vendor have?
 – How are you using it?
 – Any AI right this second?
 – Determine whether to evaluate
• Employer is ALWAYS responsible for employment
 decisions – NOT a vendor

Thank you!
Contact Sheri Tischer Contact Kate Bischoff

P: (952) 656-3492 P: (320) 249-9269
E: stischer@tcicapital.com E: kate@k8bisch.com

 Reminder:
 You can receive CE credits from
 the American Staffing Association
 (ASA) or the National Association
 for Personal Services (NAPS).