Title:
AI and Automated Decision-making Systems in Employment: A Look at State General Statutes Addressing of Bias Audits, Transparency, and Legal Accountability in the US

Authors:
Brandon Burgess, Dr. Steven Cates, USA

Abstract:

Increasingly, companies are utilizing electronic decision-making systems powered by AI for the hiring, evaluation, and upskilling of employees. While automation may help, evidence suggests that algorithmic devices can replicate and potentially intensify social bias. This research examines whether the use of AI in hiring and selection is legal and fair, by creating discriminatory processes into employment practices. This study examines bias audits, transparency obligations, and new regulations such as New York City’s Local Law 144 in a doctrinal legal process. This research examines whether the proposed Bias Notification Duty will enhance accountability. The study indicates that while AI facilitates efficient processes in the workplace, it also risks increasing discrimination and bias. It is known that humans design AI and they might have inherent biases. These computer professionals then create technological systems like AI that inherit these biases. HR managers and leaders have obligations for the responsible implementation of algorithmic systems to promote fairness and equality in HR processes. The findings in examining legislation addressing AI for practicing managers indicate that the integration of AI into employment decisions carries profound operational, legal, and ethical implications. As managers make greater use of automated decision-making systems, they do not avoid accountability; rather, they increase it.

DOI: https://doi.org/10.51505/IJEBMR.2026.10710