Employment Law Implications of Automated Decisions and Disparate Outcomes

Algorithmic management, AI bias, and workforce accountability done responsibly.

By attending this course you will gain a clear understanding of how AI systems are being used to assign tasks, schedule shifts, and evaluate performance; the risks created when human oversight is reduced in promotion, discipline, or termination decisions; and how biased training data and limited transparency can reinforce discrimination. We will also discuss current efforts to build fair and accountable AI systems. You’ll leave equipped to identify and mitigate legal and operational risks and translate these insights into practical policies and reviews that improve outcomes for your workforce and organization. All of these topics and skills make this an important, timely investment in responsible AI practice.

Learning Objectives

• You will be able to define algorithmic management and automated decision-making in workplace contexts and distinguish them from traditional, human-led supervisory practices.
• You will be able to describe how AI tools assign tasks and evaluate performance, and where risks emerge when human oversight is reduced in promotion, discipline, or termination decisions.
• You will be able to identify signals of bias linked to training data and opacity in AI systems and how these can reinforce discrimination in hiring, evaluations, or promotions.
• You will be able to explain practical governance measures-such as review checkpoints and accountability frameworks-that mitigate over reliance on automated systems and support fairer outcomes.