fbpx
Unique Challenges and Opportunities of Artificial Intelligence Applications in Human Resource Functions Unique Challenges and Opportunities of Artificial Intelligence Applications in Human Resource Functions
Editor’s note: Seema Chokshi is a speaker for ODSC APAC this August 22-23. Be sure to check out her talk, “State... Unique Challenges and Opportunities of Artificial Intelligence Applications in Human Resource Functions

Editor’s note: Seema Chokshi is a speaker for ODSC APAC this August 22-23. Be sure to check out her talk, “State of AI in Human Resource Functions: Unique Opportunities and Challenges,” there!

Conceptually there is consensus amongst experts on the value that AI can drive for businesses in general and Human Resource Management in particular. AI applications can, not only improve business productivity, they can also help to improve employee experience and participation in organizational initiatives if used in an optimal way. Despite this common understanding, the ground reality is often conflicting. Surveys by firms such as Boston Consulting Group and MIT found that 7 out of 10 AI projects failed to realize the impact that they were expected to have and AI implementation plans dropped from 20% in 2019 to 4% in 2020. These numbers get further depressed when we focus on AI applications related to Human Resource functions within organizations. 

Before I address some of the nuances of applying AI to solve problems for HRM, I want to go back to the basic understanding of what HR is primarily set out to achieve. The seminal paper by researchers, March and Simon, 1958 stated that employees face two fundamental decisions in their interactions with the organizations, one is the “decision to produce” and the other is the “decision to participate”.  The decision to produce involves whether employees are willing to produce (create) as much as the organization demands them to, whereas the decision to participate means that employees can choose to remain with the organization or leave. Both these are substantially different decisions and managing the motivational problems in helping employees reach their maximum contribution to organizational outcomes, is one of the main missions and purpose of human resource functions in any organization. With that in mind, we would agree that employees are at the heart of all functions of HRM. 

In-Person and Virtual Conference

September 5th to 6th, 2024 – London

Featuring 200 hours of content, 90 thought leaders and experts, and 40+ workshops and training sessions, Europe 2024 will keep you up-to-date with the latest topics and tools in everything from machine learning to generative AI and more.

Given that AI systems are built on algorithms leveraging underlying employee data, the good thing is that these models can be used to optimize employee performance by motivating and incentivizing people by creating personalized performance evaluation plans and incentives. Also, when used in a responsible manner, these models can help to reduce bias and unfairness which is often engrained in managerial evaluations as per the subjective opinions of the person in charge. On the flip side though, we hear about examples of discriminatory models that only further scaled the bias inherent in the underlying data used to build these models. Employees’ negative reactions to surveillance can also give rise to distrust and unwillingness to comply with policies based on algorithmic outcomes. The ethical risks are too high when building AI systems for HR given that they have important consequences for individuals and society at large. In my upcoming talk at ODSC, I will elaborate on the unique challenges and opportunities of deploying AI applications for HR. Some of the highlights are given below: 

  • The use cases of AI in HR and challenges when working to develop accurate scores based on machine learning models on HR data. 
  • Data preparation approaches that can help improve accuracy of HR models 
  • Algorithmic management and employee reactions to being managed by AI instead of a human manager 
  • Ways to prevent misuse of AI models by employees who try to game the systems once AI contributes to decision-making. 
  • The cautions that need to be taken when walking on this road, to avoid risking the reputation and cost of unintended consequences of AI gone bad! 

Be there to know more and hear me speak

About the Author:

Seema Chokshi is an entrepreneur helping organizations in participating in the AI revolution. Seema is an adjunct faculty of Analytics in Singapore Management University and believes that AI can augment powers for everyday people in ways unimaginable! Seema has written multiple case studies on AI adoption and worked on many ambitious data science projects over the last two decades. Seema is a women’s empowerment champion and runs a women’s network to bring like-minded women together to help them navigate through the unique challenges. Read more in her newsletter here https://seemas-newsletter.beehiiv.com.  

Connect with her on linkedin.com/in/seemachokshi to read more from her and know her journey. 

Cover image generated using MidJourney.ai by Seema Chokshi

ODSC Community

The Open Data Science community is passionate and diverse, and we always welcome contributions from data science professionals! All of the articles under this profile are from our community, with individual authors mentioned in the text itself.

1