Can Artificial Intelligence Make Decisions About Employees Without Meaningful Human Review And Accountability?

Artificial intelligence is rapidly changing the modern workplace. From screening thousands of job applications to analysing employee performance, predicting attrition and even recommending disciplinary action, AI is increasingly being placed in positions once reserved for human managers and HR professionals.

 

The attraction is obvious: AI can process enormous quantities of information quickly, identify patterns and potentially reduce human inconsistency. But when an algorithm determines whether an individual should be hired, promoted, disciplined or dismissed, a fundamental question arises: can an employer allow AI to make decisions about employees without meaningful human review?

 

The answer is becoming increasingly complex. As AI moves deeper into employment decision-making, organisations must balance technological efficiency with fairness, transparency, privacy and accountability.

 

AI Recruitment: Efficient, But Not Necessarily Neutral

 

AI recruitment systems can screen CVs, rank candidates, analyse applications and identify individuals who appear suitable for a particular role. For employers handling large volumes of applications, this can significantly reduce recruitment time and administrative costs.

 

However, an algorithm is only as objective as the data and assumptions behind it. If an AI system is trained on historical hiring data reflecting existing workplace inequalities, it may reproduce or even amplify those patterns. A system could unintentionally disadvantage candidates based on factors such as gender, age, disability, nationality, educational background or career history.

 

This creates a difficult legal question: who is responsible when an algorithm discriminates? The employer cannot simply argue that “the AI made the decision”. Organisations remain responsible for the systems they deploy and the employment decisions made through them.

 

Performance Scoring and the Problem of the “Invisible Manager”

 

AI is also being used to monitor productivity, attendance, communication patterns, sales performance and other workplace indicators. In principle, data-driven performance management can help employers identify genuine performance issues. Yet employee performance cannot always be reduced to measurable statistics.

 

An employee may spend more time dealing with a difficult client, mentor colleagues, solve problems that do not appear in performance metrics or work in circumstances that an automated system cannot understand. An algorithm may therefore identify a numerical “underperformer” without understanding the circumstances behind that performance.

 

This is particularly concerning where an AI-generated score influences promotion, compensation or continued employment. Employees should have an opportunity to understand how significant decisions affecting their careers were reached and to challenge inaccurate or incomplete information.

 

Automated Disciplinary Decisions: Where Human Judgment Matters Most

 

The most controversial use of AI may be automated disciplinary action. Imagine an employee being flagged by an algorithm for alleged misconduct based on attendance records, communications, productivity data or workplace monitoring. If the system automatically recommends suspension, reduces a performance rating or contributes to termination, the consequences can be substantial.

 

Employment decisions frequently involve context, intent and proportionality. A late arrival may constitute misconduct, or it may have resulted from an exceptional circumstance. A communication may appear inappropriate to an algorithm while having an entirely legitimate explanation.

 

For this reason, human review should not merely be a procedural formality. A manager or HR professional should have the ability to examine the underlying facts, consider explanations provided by the employee and exercise independent judgment before serious employment action is taken.

 

Employee Data: How Much is Too Much?

 

AI systems require data. The more sophisticated the system, the greater the potential demand for employee information. Employers may collect information relating to attendance, performance, communications, location, productivity and behavioural patterns. While some data may be necessary for legitimate business purposes, the existence of technology capable of collecting information does not automatically justify its collection or use.

 

Organisations must consider fundamental data-protection principles such as transparency, purpose limitation, data minimisation, security and appropriate retention. Employees should know, where legally required, what information is being collected, why it is being processed and how it may influence decisions concerning them.

 

The challenge becomes even greater when employee data is fed into third-party AI platforms. Employers must understand where the data goes, who can access it, how it is stored and whether it may be used to train other systems.

 

Discrimination and Accountability: The Central Legal Challenge

 

The greatest legal concern surrounding workplace AI is not necessarily the technology itself, but the possibility of automating unfairness at scale. Traditional human decision-making can be discriminatory, but AI can potentially reproduce the same problem across thousands of decisions in a remarkably short period.

 

This makes governance essential. Employers deploying AI in employment decisions should consider conducting appropriate impact assessments, testing systems for discriminatory outcomes, maintaining audit trails and establishing clear internal responsibility for AI-assisted decisions.

 

Most importantly, accountability should remain with identifiable human decision-makers. An employee should not be left in the position of challenging an opaque algorithm with no explanation and no person willing to take responsibility for its outcome.

 

The Future: AI-Assisted, Not AI-Absolved

 

The debate should not necessarily be framed as AI versus humans. AI can be an extraordinarily useful decision-support tool. It can identify patterns that humans may overlook, reduce administrative burdens and help HR teams make more informed decisions.

 

The problem arises when efficiency replaces judgment. A sensible approach is therefore to establish a principle of meaningful human oversight for decisions that have significant consequences for an employee’s rights, livelihood or career.

 

Human review should involve more than clicking “approve”. The reviewer should understand the basis of the AI recommendation, have access to relevant information, be capable of questioning the system and possess genuine authority to reject its recommendation.

 

The future workplace will almost certainly involve more artificial intelligence. The real question is not whether AI will participate in employment decisions, but how much authority we are prepared to give it.

 

Conclusion

 

Technology may make decisions faster. It does not necessarily make them fairer. Ultimately, an algorithm cannot carry moral responsibility, understand every human circumstance or stand accountable before an employee whose career has been affected. That responsibility remains with the organisation and the people who govern it.

 

AI may assist in making employment decisions. But where livelihoods are at stake, human accountability should never become automated.

 

Anmol Chettri is a Trainee Legal Associate at UAE-based legal consultancy Kaden Boriss.