AI in HRTech has long been touted as a solution for wage equalization and the elimination of human bias, especially in the past decade as companies increasingly adopt automated compensation systems. However, as 2025 is at the closing of its first quarter, the gender pay disparity still exists, albeit with layers of code covering it up. Under the pretense of “neutral algorithms,” AI-driven compensation systems frequently serve to perpetuate historical wage inequities rather than address the issue.
TAI is often seen as an objective tool, yet it inherits the patterns of the society it learns from—including gender disparities. The way we develop, train, and implement AI determines whether it challenges or reinforces these biases.
Table of Contents:
1. When AI Learns from an Unfair Past
2. The Algorithmic Excuses for Unequal Pay
3. Can AI Actually Fix the Problem?
1. When AI Learns from an Unfair Past
HRTech platforms train their pay models using past salary data. However, AI only escalates the unfairness more effectively; it doesn’t address historical pay disparities.
According to a 2024 World Economic Forum research, closing the gender pay gap globally will take 131 years at the current rate. AI was meant to accelerate progress, still it frequently has the opposite effect.
Although compensation algorithms assert that they can ascertain “fair market value,” whose data constitutes that market? AI makes it more difficult for women to negotiate salary increases if the baseline is already prejudiced, such as undervaluing women’s leadership or technological achievements.
2. The Algorithmic Excuses for Unequal Pay
Under the guise of data-driven decision-making, many HR systems defend wage inequities. However, these defenses disintegrate when we examine it more closely:
“AI calculates salaries based on merit.”
The definition of “merit” is frequently distorted. According to McKinsey research, women are more likely to be given important but underappreciated non-promotable jobs like administrative and mentoring duties. The loop is maintained when AI models trained on historical promotion data tend to devalue these contributions.
As more businesses worldwide implement AI-driven, location-based pay adjustments, remote workers, especially women (who make up 54% of the remote workforce), are disproportionately affected. This disregards the fact that remote work often boosts output and revenue for businesses.
“AI simply reflects the job market.”
AI only serves as a digital mirror of existing disparities rather than a vehicle for change if the labor market has been structurally unjust for decades. If nothing is done, AI-driven pay decisions will not challenge the current quo but rather maintain it.
3. Can AI Actually Fix the Problem?
AI isn’t bad in and of itself. The architecture of these systems and who controls them are the true problems. Businesses must reconsider their strategy if they want AI in HRTech to be a driver of pay equity rather than another obstacle.
- Create transparency in algorithms. In order to contest unfair judgments, workers need to have access to AI-driven compensation models.
- Check for bias in AI. Racial and gender pay disparities should be checked for in regular audits of compensation recommendations.
- Use data to empower staff. Instead of keeping AI a company secret, provide employees with tools for measuring salaries so they can bargain for fair compensation.
- Advocate for stricter rules. In order to prevent AI from being used as a legal justification for discrimination, pay equity rules must change.
AI Won’t Solve Pay Parity—People Will
AI is capable of processing facts, but it cannot make moral decisions. Pay disparities will continue to exist as long as businesses prioritize cost reduction over equity, even with enhanced mathematical analysis. The actual remedy? Ensuring human control at every stage and holding AI accountable.
Better AI is not the ultimate goal in the fight for fair compensation. The true goal is to ensure that AI benefits human interests rather than just the financial ones.
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