More Related Content Similar to How to Power Your HR Apps With AI And Make It Explainable (20) More from Harbinger Systems - HRTech Builder of Choice (20) How to Power Your HR Apps With AI And Make It Explainable1. How To Power Your HR Apps With AI And
Make It Explainable
Harbinger Systems
in association with HR.com
December 5, 2019
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Speaker Introduction
Shrikant Pattathil Maheshkumar Kharade
President
Harbinger Systems
AGM - Technology
Harbinger Systems
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Agenda
Key AI Trends in HR Tech
HRTech use cases of AI enabled applications
Challenges faced in leveraging AI features and how to overcome those?
Summary
4. Poll #1
Are you using AI-enabled HR applications and solutions in your organization?
A. Yes
B. No
C. Don’t know
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AI Trends in HRTech – Harbinger Analysis
2018 2019
• Talent Acquisition
• Training and Development
• Performance Management
• Time and Attendance
• Analytics and metrics
• Talent acquisition
• Training and Development
• Compensation and Payroll
Use of AI in HR Functions
Virtual Assistant
Recommendation Engine
Pattern Recognition
Analyze and Diagnose
Predict
Personalize
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Top Expectation for AI enabled HR Applications
Ability to analyze (e. g. Run an automated analysis of a data set and then spot patterns)
Ability to predict (e. g. Predict which job candidate will result in the highest quality of hire)
Ability to personalize (e. g. Personalize training modules based on current knowledge
level)
Ability to automate (e. g. Use AI to create answers for HR related questions)
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How to make sure that there is no bias in scoring and matching?
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Example #2: AI-based Personalized Learning Experience
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What details are captured to track user actions and search patterns? Is it collecting
any private information about users?
15. Poll #2
What challenges do you foresee while deploying such AI applications in
your organization?
A. Cost of implementation
B. AI technology is too new
C. Fear of bias in data
D. Lack of explanation how decision is made
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Challenges faced in leveraging AI features and
how to overcome those?
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• Data source
• Compliance
Quality of Data
• Organizing
• Cleansing
• Anonymizing
Data
Transformation • AI algorithms
• Explanation of
results
• Correctness
Insights into
working of AI
Challenges
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Quality of data
Good
Data
Right
Model
Better AI
Getting “Good Data”
is the biggest
challenge and most
time consuming!
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Data Transformation
Gathering
(Multiple
Sources)
Cleansing
Anonymizing
Organizing
Raw data transformation to
good AI data
Data
processing
is unique to
each
organization
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Explainable AI
Ref: https://www.datanami.com/2018/05/30/opening-up-black-boxes-with-explainable-ai/
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Example #4: Candidate Matching and Scoring
Using Explainable AI
22. Poll #3
Do you think all AI-enabled HR applications should be explainable?
A. Yes
B. No
C. Don’t know
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Other Challenges
Innovation first occurring outside of the suites
Of limited value to smaller/lower volume organizations
Data science skills and experience within the HR department to ensure safe and effective
operations
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Recap
AI in HRTech
• Talent acquisition
continue to lead the way
in using AI innovatively
• Use of AI for Prescriptive
and Predictive use cases
is catching up in modules
such as Training and
Development,
Compensation, Payroll
etc.
Challenges
• Bias in results due to
nature of data
• Evaluating the
correctness and
reliability of data
• Ensuring the ethical
capturing and use of data
• Understanding working
of AI models and
algorithms
Key to success
• Providing insights into
use of data by AI
• Explaining the AI results
• Provide explainable
interface
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Thank you
27
Contact Us
Shrikant Pattathil – shrikant@harbingergroup.com
Maheshkumar Kharade – maheshkumar@harbingergroup.com
Editor's Notes Agenda
- What u will get Are you worried about bias?
Effectiveness of AI currently implemented