Webinar

EXCITING USE

CASES OF AI IN HR

Thank you for your Interest!

This webinar provided a comprehensive overview of AI’s role in HR, emphasizing the need for ethical and responsible AI practices. Dr. Gargi Bhattacharya explored how AI is transforming recruitment, employee experience, and workforce planning. The discussion highlighted the importance of data governance and transparency, while also addressing the challenges of AI bias and the need for human oversight. Despite AI’s potential, particularly in automating tasks and improving efficiency, it has limitations—especially in areas requiring emotional intelligence or nuanced decision-making.

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Key Insights

      1. AI in HR and Talent Management
        • AI in HR: Gargi emphasized that AI, including machine learning and natural language processing, automates key HR functions such as recruitment and employee management. AI is increasingly used across the entire HR lifecycle, enhancing efficiency.
        • Types of AI: She distinguished between narrow, general, and super AI, with a focus on reactive AI and limited memory AI (like generative AI), which are widely applied in HR today.
      2. Generative AI in Recruitment
        • AI’s Impact on Talent Acquisition: Generative AI has transformed recruitment by automating up to 80% of tasks, such as drafting emails and managing candidate information. This enables recruiters to focus more on decision-making and personalization.
      3. Responsible and Ethical AI Use
        • Need for Ethical AI: With growing AI adoption, it’s critical for organizations to implement responsible AI practices. Gargi discussed the importance of having dedicated teams to ensure fairness and referenced the Workday ATS litigation case as an example of the need for transparency and ethical AI use.
      4. AI and the Future of HR
        • Emotional Intelligence (EQ) in AI: Tools like Inflection AI aim to integrate emotional intelligence into AI systems, though this is still an emerging area of research.
        • Theory of Mind AI: Gargi predicted that within the next 5 to 10 years, significant progress might be made toward AI systems with human-like cognitive abilities, driven by national lab research.
      5. AI Bias and Fairness
        • AI Bias: AI models, particularly those used in facial recognition, can inherit biases from training data. Gargi highlighted the need for diverse data sets and robust model training to ensure fairness in AI-driven HR systems.
      6. Demographic Changes and Workforce Planning
        • AI in Workforce Planning: AI can help HR professionals prepare for and manage demographic shifts, anticipating future workforce needs effectively.
      7. External Data Utilization
        • Leveraging External Data: Gargi emphasized using external sources like Glassdoor to gauge employee sentiment, which, when combined with internal surveys, has an accuracy rate of about 80%.
      8. AI Supervision in Recruitment and Screening
        • Ensuring Fairness: AI models used in recruitment should be carefully scrutinized to avoid perpetuating biases, especially in gender-balanced hiring and industries where diversity is a concern.
      9. Generative AI vs. Traditional AI
        • Different AI Models: While generative AI is currently a trending topic, Gargi explained that machine learning and neural networks have long been in use across different sectors, and generative AI is just one component of the broader AI ecosystem.
      10. Importance of Behavioral Science in AI
        • Behavioral Science in AI Models: Gargi emphasized the role of behavioral science in refining AI to ensure it remains unbiased, particularly when used for sensitive tasks like hiring and performance evaluations.
      11. AI in Learning and Development
        • AI in Leadership Coaching: AI is increasingly being integrated into Learning Management Systems (LMS) and leadership coaching. AI-generated learning content is about 80% accurate, allowing professionals to concentrate on more complex tasks.
      12. Governed vs. Ungoverned AI
        • The Value of Supervised AI: Gargi explained that supervised AI, which follows defined rules, offers more control and transparency compared to ungoverned AI, which may pose risks in decision-making processes.
      13. Challenges with NLP and Data Tagging
        • NLP in HR: Natural Language Processing (NLP) is crucial for organizing HR data and deriving insights. However, data categorization and tagging require regular updates to maintain accuracy in AI-generated outputs.
      14. AI in Employee Experience
        • Improving Employee Services: AI has transformed the employee experience by automating tasks like IT requests and benefits inquiries, leading to significant time savings and enhanced experiences. However, AI still has limitations, especially in accurately predicting human behavior, such as turnover.
      15. Conflict Resolution and AI
        • ChatGPT and Conflict Resolution: Dr. Shreya Sarkar-Barney shared a personal example of using ChatGPT for conflict resolution advice, demonstrating both the tool’s utility and its limitations in addressing complex human interactions.
      16. Data Responsibility and Transparency
        • Data Governance in HR: Gargi stressed the importance of understanding data sources and adhering to regulations like GDPR, particularly for European employees. She warned against vendors who may provide personal data and recommended careful scrutiny of data processing systems.
      17. Bias in AI Models
        • Addressing Bias in AI: Despite claims of objectivity, AI models can inherit biases from the data used to train them. Gargi recommended working with data scientists to ensure fairness and transparency, establishing guidelines for AI usage within organizations.
      18. Agentic AI
        • Agentic AI Models: Tools like Siri and Alexa, referred to as Agentic AI, are used for routine tasks in the workplace but require significant training and oversight, and they do not learn organically like humans.

Timestamped highlights:

 

00:04:12 – Gargi’s introduction and her background in HR and AI.

00:07:24 – Responsible AI practices and the Workday ATS case.

00:09:16 – AI definitions and its applications in HR.

00:21:12 – Generative AI’s role in recruitment.

00:22:35 – Emotional intelligence in AI and the future of AI development.

00:25:52 – Workforce planning and demographic shifts.

00:29:52 – External data sources for sentiment analysis.

00:35:03 – AI models like neural networks across sectors.

00:37:16 – Responsible AI and model governance.

00:43:04 – Bias reduction in job descriptions.

00:45:53 – AI and improved employee services.

00:50:10 – Importance of data governance in HR AI systems.

01:00:19 – Agentic AI and its limitations.

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