Prof. Dr. Lisa Giermindl

Professor of Leadership and HR Management

Lisa Marie Giermindl is Professor for Leadership and HR Management at the University of Applied Sciences St.Gallen. Her research focuses on People Analytics and AI as well the positive and dark side of information systems in workplace settings and the future of work. She has recently published her research titled “The dark sides of people analytics: reviewing the perils for organisations and employees” in the high-ranked European Journal of Information Systems. Lisa is also leading a large funded research project on predictive people analytics with several international companies, which aims to promote predictive people Analytics in Swiss companies and to contribute to a sustainable development of analytics competencies in HR departments. Furthermore, she conducts applied research on future skills, digital technologies and the future of HR. In her Ph.D. thesis she explored the positive and impact of (Enterprise) Social Media on organisational collaboration and performance.

Prior to her academic career, she worked in different fields of HR Management for multinational companies. For instance, she has held multiple leadership roles in Talent Acquisition at Siemens and was closely collaborating with the people analytics department in conducting global engagement surveys and analyzing large amounts of internal and external data.

All Sessions by Prof. Dr. Lisa Giermindl

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Day 1: Dec 7, 2021

Day 2: Dec 8, 2021

1:45 pm

1:45 pm

AFTERNOON KEYNOTE: ALGORITHMIC BIAS

Deepen your Understanding of the Algorithms Behind Advanced People Analytics Practices and Avoid Unintentional Bias

Many of the current applications used for screening resumes and employee behaviour may contain algorithms that actually perpetuate unintended biases. Maintain an equitable approach to your employee selection and management by learning how to combat negative biases in your algorithms. Take away valuable insights to help you:

  • Uncover how algorithms work and the impact they can have on data.
  • Understand the math behind the algorithms that power current HR data tools.
  • Develop an approach to assessing people analytics data tools to ensure you receive unbiased results.

Avoid the negative impact algorithms can have on your people analytics program.