Can AI be trusted in the field of human resources? Learn about potential pitfalls, internal and external challenges, and how to combat them.
HR's Biggest Challenge: Trusting Generative AI
The use of generative AI in human resources (HR) software and processes presents both internal and external challenges that require human awareness and action.
While advanced AI promises efficiency and insight, its current limitations require users to maintain responsible oversight.
Recent advances in generative AI allow HR platforms to automate tasks like screening resumes, conducting interviews, analyzing employee data, and generating job descriptions.
However, these AI systems are prone to bias, inaccuracies and abuse without thoughtful human guidance. For example, automated resume screening based on AI evaluations of past applicants can perpetuate historic biases against minorities, women and other groups.
Inside organizations, generative AI relies on training data that is only as good as human-generated source material. Outdated, biased, or simply erroneous HR data can lead AI systems to make unfair or irrational choices about candidates or employees.
When generative models produce text, such as job descriptions or performance reviews, they risk reflecting outdated styles or perspectives unless trained on current best practices.
These internal dangers point to the need for HR teams and technology leaders to carefully evaluate:
The data sources used to build and improve AI models, reducing historical biases.
The models’ real-world results, to catch unexpected errors.
Ongoing monitoring and refinement of AI to adapt to changes in workplace norms.
Externally, even responsibly designed generative AI can enable dishonest uses like creating fake applicant profiles or falsifying workplace harassment. While advanced detection systems may curb some misuse, human oversight is key.
Ultimately, managers employing these technologies bear responsibility for monitoring AI ethics and performance. Rather than seeing AI as a "set it and forget it" type solution, users must audit and guide its impacts, considering both its benefits and current limitations compared to human judgment.
With thoughtful oversight, organizations can harness the advantages of AI for HR while safeguarding workplace and applicant dignity.
The Promise of Generative AI in HR
Generative AI, a subset of artificial intelligence, enables machines to generate human-like text, images, and other content. In HR, it can be applied to automate various tasks, such as creating job descriptions, screening resumes, or even conducting initial interviews. The allure of using AI in HR lies in its potential to save time, reduce bias, and improve decision-making.
However, the deployment of Generative AI in HR is not without its challenges, both internal and external.
1. Data Quality and Bias: Generative AI relies heavily on data. If the data used to train these systems is biased, it can lead to biased results in HR processes.
This is a significant concern as it perpetuates existing disparities in hiring, promotions, and other HR-related decisions. George Lawton supports our argument in his article published in Techtarget. Organizations should ensure their training data is diverse and free from bias.
2. Lack of Understanding: Many HR professionals may not fully understand how Generative AI works, leading to reluctance to embrace it. A white paper by HRD translates the same problem being faced by HR while implementing AI. 
3. Overreliance on Technology: As Generative AI becomes more prevalent in HR processes, there is a risk of HR professionals becoming too reliant on it. This could lead to a disconnect between the human element of HR and the technology.
1. Technology Errors: Generative AI is not infallible. There are instances where it generates incorrect or misleading information.
These errors can negatively impact HR decisions. In their technology report on AI challenges, HCM suggested the need for robust quality control processes to identify and rectify errors promptly. 
2. Candidate Experience: While AI can automate many aspects of the HR process, candidates may feel disconnected or dehumanized when interacting with machines. Poor candidate experience can tarnish an organization's reputation
Solutions to Combat Generative AI Challenges
1. Data Quality and Bias Mitigation: Organizations should actively work to diversify their training data and invest in AI tools designed to reduce bias in HR decision-making. Regular audits and assessments of AI systems can help identify and rectify bias.
2. Education and Training: HR professionals should undergo training to understand how Generative AI works and how to interpret its outputs. This will help them make more informed decisions and maintain a human-centric approach.
3. Human Oversight: Human judgment should always complement AI decisions. HR professionals must remain actively involved in all HR processes, even those partially automated by AI. This human oversight ensures a more balanced approach to HR.
4. Quality Control: Establish a robust quality control process to detect and correct AI errors promptly. This can prevent costly mistakes in HR decisions.
5. Candidate-Centric AI: Use Generative AI to enhance the candidate experience rather than replace it. Ensure that candidates feel engaged and well-informed throughout the hiring process.
Generative AI has the potential to revolutionize HR processes, but the human problems associated with it cannot be ignored. By addressing internal and external challenges and implementing the solutions discussed in this article, organizations can harness the power of AI while maintaining the human touch in HR. As technology continues to advance, the successful integration of Generative AI into HR hinges on a harmonious balance between technology and human expertise.
Want to learn more about how Mosh JD has thoughtfully integrated generative AI into our job description software to facilitate simplistic human oversight? Click here to learn more!