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How to Power Your HR Apps With AI And Make It Explainable

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A webcast on ‘How to Power Your HR Apps With AI And Make It Explainable’ by Harbinger Systems.

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How to Power Your HR Apps With AI And Make It Explainable

  1. 1. How To Power Your HR Apps With AI And Make It Explainable Harbinger Systems in association with HR.com December 5, 2019
  2. 2. ©Harbinger Systems | www.harbinger-systems.com 2 Speaker Introduction Shrikant Pattathil Maheshkumar Kharade President Harbinger Systems AGM - Technology Harbinger Systems
  3. 3. ©Harbinger Systems | www.harbinger-systems.com 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. 4. Poll #1 Are you using AI-enabled HR applications and solutions in your organization? A. Yes B. No C. Don’t know
  5. 5. ©Harbinger Systems | www.harbinger-systems.com Key AI Trends in HR Tech
  6. 6. ©Harbinger Systems | www.harbinger-systems.com 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
  7. 7. ©Harbinger Systems | www.harbinger-systems.com 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)
  8. 8. ©Harbinger Systems | www.harbinger-systems.com HR Tech Use cases of AI enabled applications
  9. 9. ©Harbinger Systems | www.harbinger-systems.com Example #1: Candidate Matching and Scoring
  10. 10. ©Harbinger Systems | www.harbinger-systems.com How to make sure that there is no bias in scoring and matching?
  11. 11. ©Harbinger Systems | www.harbinger-systems.com Example #2: AI-based Personalized Learning Experience
  12. 12. ©Harbinger Systems | www.harbinger-systems.com What details are captured to track user actions and search patterns? Is it collecting any private information about users?
  13. 13. ©Harbinger Systems | www.harbinger-systems.com Example #3: AI-based HR Help Desk
  14. 14. ©Harbinger Systems | www.harbinger-systems.com Are the questions coming from a reliable source?
  15. 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
  16. 16. ©Harbinger Systems | www.harbinger-systems.com Challenges faced in leveraging AI features and how to overcome those?
  17. 17. ©Harbinger Systems | www.harbinger-systems.com • Data source • Compliance Quality of Data • Organizing • Cleansing • Anonymizing Data Transformation • AI algorithms • Explanation of results • Correctness Insights into working of AI Challenges
  18. 18. ©Harbinger Systems | www.harbinger-systems.com 18 Quality of data Good Data Right Model Better AI Getting “Good Data” is the biggest challenge and most time consuming!
  19. 19. ©Harbinger Systems | www.harbinger-systems.com 19 Data Transformation Gathering (Multiple Sources) Cleansing Anonymizing Organizing Raw data transformation to good AI data Data processing is unique to each organization
  20. 20. ©Harbinger Systems | www.harbinger-systems.com Explainable AI Ref: https://www.datanami.com/2018/05/30/opening-up-black-boxes-with-explainable-ai/
  21. 21. ©Harbinger Systems | www.harbinger-systems.com Example #4: Candidate Matching and Scoring Using Explainable AI
  22. 22. Poll #3 Do you think all AI-enabled HR applications should be explainable? A. Yes B. No C. Don’t know
  23. 23. ©Harbinger Systems | www.harbinger-systems.com 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
  24. 24. ©Harbinger Systems | www.harbinger-systems.com Summary
  25. 25. ©Harbinger Systems | www.harbinger-systems.com 25 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
  26. 26. ©Harbinger Systems | www.harbinger-systems.com QnA Please use Question Box
  27. 27. ©Harbinger Systems | www.harbinger-systems.com Thank you 27 Contact Us Shrikant Pattathil – shrikant@harbingergroup.com Maheshkumar Kharade – maheshkumar@harbingergroup.com