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.
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For Better or For Worse: How Artificial Intelligence is Impacting Diversity and Inclusion in Our Organizations
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
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
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
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
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?