This document discusses how artificial intelligence (AI) impacts diversity, inclusion, and belonging in organizations. It outlines the goals of diversity and inclusion as attracting, engaging, and retaining a diverse mix of employees. AI tools can help with recruitment, training, and workplace optimization, but representative data and addressing biases are important. Organizations should involve experts, ask questions about data use and impact, and ensure humans make final decisions to develop trustworthy AI that supports diversity and inclusion goals.

1. FOR BETTER OR FOR WORSE:
How Artificial Intelligence is
Impacting Diversity and Inclusion
in Our Organizations
Sage Franch | @theTrendyTechie | sage@getcrescendo.co
Jeff Waldman | @JeffWaldmanHR | jeff@ibelong.work
#HackingHRToronto

2. Jeff Waldman Sage Franch

3. Agenda
The Goal
of Diversity &
Inclusion
Understanding
AI
De-Risking AI
in Your
Organization
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4. The Goal of Diversity & Inclusion

5. Diversity:
The mix of differences and similarities
to best serve the markets you serve.
● Attracting
● Engaging
● Retaining
Inclusion:
Making the mix work.
● Equality
● Fairness
● Respect

6. Is This Enough?
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7. Pat Wadors, LinkedIn
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8. “I am psychologically safe”
“I can bring my whole self to work”

9. Diversity Inclusion Belonging
Employee Engagement
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10. The Goal of DIBs is to
increase/improve...
● Productivity
● Brand reputation
● Market share
● Workplace innovation
● Market competitiveness
● Team cohesiveness & effectiveness
● Employee experience, not just
candidate experience

11. AI and DIB

12. AItoolstom
akework
practicesm
oreinclusive
How
AIintheworkplace
willim
pactdiversity
The inclusivity of AI
tools themselves
The AI / DIB
Conversation
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13. AI tools for DIB
● Recruitment - job postings,
screening and assessment
● Chat bot: candidate/employee
● D&I and unconscious bias
training
● Performance and career
progression
● Workplace optimization
● Remote work connectedness

14. Predictive
Tailored
Targeted
Personalized
Intelligent
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15. AI is just a fancy way of saying
“really good estimates of human decisions”
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16. DATA
Prediction
Model
Training
Data
Publish!
What goes in to AI?

17. representative data
is not always
fair data
Understanding the biased AI problem...
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18. How will AI impact my
organization?
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19. Information
Decision
ACTION
AI wields as much power as you give it
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20. De-risking AI in
your organization

21. Trustworthy AI tools
for DIB:
● Consult humans in the
decision and action
● Are transparent about how
they handle and use
people’s data

22. Questions to ask when assessing AI tools
Organizational:
● What problem are we solving and
how can this AI tool help solve it?
● Will our org feel comfortable with
the data being collected?
● What decision is the AI helping us
make? Does it actually help?
● When using the AI data who owns
the final decision and action?
● Who will this impact, and how?
Technical:
● How confident am I that the AI is
unbiased?
○ Was its training dataset large and
diverse?
● What standards do they follow for
data protection and security?
○ SOC 2, GDPR, etc.
● Vendor case studies - do the
outcomes match your goals?

23. Access your experts

24. Involve your…
C Suite
Security lead
IT team
ERGs
Focus groups

25. Key Themes
Belonging is
the Key
Technology is
not a silver
bullet
Be open to
trial and error
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26. Q&A
Sage Franch | @theTrendyTechie | sage@getcrescendo.co
Jeff Waldman | @JeffWaldmanHR | jeff@ibelong.work
#HackingHRToronto