AI is an accelerator for Talent Transformation. It’s a layer of technology that means great talent teams can be even more powerful, but for the best results, the right foundations need to be in place. In this first session of “Accelerating Talent Transformation with AI”, we’re cutting through the buzz and focusing on what will set talent leaders up for success when it comes to this new technology. What do today’s Talent leaders absolutely need to know about AI, and what questions should they ask when investigating new talent tech?

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The Fundamentals of AI in Talent Acquisition
June 2021

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Discover the webinar series
Accelerating Talent Acquisition with AI
1. AI in Talent Acquisition: The Fundamentals
2. The Hidden Dangers of “Black Box” AI
3. 3 Ways to Drive Talent Goals with AI
4. Talent in 2025: Designing the Future

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Jeet Mukerji
Product Manager, AI and Data Science

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The new normal

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Jobs YoY Increase
462%
Pipeline YoY Increase
46%
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Internal
Mobility
DE&I
Recruiter
Productivity
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How can AI create better employee
up/re-skilling experiences?
How can AI make talent acquisition
more objective at scale?
How can AI reconcile disparate data
and drive effectiveness?

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Workforce
Planning
Learning and
Development
Skills
Management
Performance
Management

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Greater focus on use cases
that are sensitive to AI
Increased adoption of
AI-powered HR tech
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Do you use deep learning or machine learning?
List all the models used in your solution.
How large is your dataset?
How big is your data science team?
Do you have a chatbot?
How do you ensure your AI is unbiased?

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Data
Models
Experiences

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Data
Models
Experiences
● Suitability
● Objectivity
● Explainability
● Validity
● Security

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Why are the chosen data
points used to train the
models? Is all of it relevant
for the use case?
What sources of data are
used? Why?
How is the data checked
for bias before it is used for
training models?
Are there data points that
may potentially cause
bias, such as gender, race
and ethnicity? How are
these handled and why are
these there?
Is the data representative
of the distribution in the
real world?
How is the data
segmented e.g. by
geography, industry?
How is the data stored and
connected? Is it in tables,
documents or graphs?
How do you maintain data
quality? Is it complete,
fresh and deduplicated?
What consent was
obtained when the data
was used or acquired?
How is data per customer
stored and used between
other customers?
What steps are taken to
ensure the data is securely
stored and cannot be
amended or stolen?
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Suitability Objectivity Explainability Validity Security

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Why were these models
chosen over other models?
What tests were done to
choose these particular
models?
How are the models
checked for bias? Are there
humans in the loop? How
often are they checked for
bias?
How does each model
work individually and
together to deliver on the
use case?
What quantitative and
qualitative methods do
you use to check if the
models are working
optimally?
Do you optimize for recall
or precision? What about
accuracy? Why?
What steps are taken to
ensure the models cannot
be interfered with?
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Suitability Objectivity Explainability Validity Security

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How does the AI improve
the user experience?
Where does it add value
and how does it enable
users to achieve their
goals more effectively than
without the AI?
How much influence does
the user have over the
model?
How does user interaction
and feedback on the
models impact the model
training?
How is the AI explained to
users? Is it clear on why
the AI made
recommendations? Is it
easily understandable and
reassuring?
How is the AI tested as part
of the user experience?
How do you measure
success?
What steps are taken to
ensure the AI experience is
secure and cannot be
interfered with?
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Suitability Objectivity Explainability Validity Security

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Implementation Adoption Satisfaction
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1. It’s now critical to understand how AI can drive value for HR
2. But remember, AI is not a magic bullet
3. Look for quality data, explainable models, valuable experiences
Three takeaways: