AI Can Reduce Hiring Gender Bias If We Design It With Intent: Expert Explains

India’s education landscape has evolved significantly over the past decade. Women now account for nearly 48% of higher-education enrollments, creating one of the largest pipelines of skilled female talent globally. This shift presents a substantial opportunity for the Indian economy. However, representation at senior leadership levels has not yet kept pace with this progress.

Global compensation data indicates that, on average, women earn approximately 77 cents for every dollar earned by men in comparable roles. While multiple factors contribute to this gap, recruitment and promotion practices remain a critical part of the equation.

Historically, hiring decisions have often relied on informal assessments such as personal networks, intuition, or perceived cultural fit. While once considered practical, these approaches can unintentionally favour familiar profiles and overlook equally capable candidates.

This is where artificial intelligence is increasingly being explored not as a replacement for human judgment, but as a way to bring greater structure, consistency, and objectivity to hiring decisions.

When thoughtfully implemented, AI can help shift recruitment from subjective evaluation toward evidence-based assessment, ensuring that skills and performance indicators carry greater weight than background or identity.

The Myth of the Neutral Algorithm

To understand how AI can support fairer hiring, it is important to acknowledge a key reality: technology is not inherently neutral. Algorithms reflect the assumptions, data, and priorities embedded during their design. As a result, poorly designed systems can replicate existing inequalities rather than reduce them.

Designing with intent, therefore, becomes essential. This means moving away from opaque, black box models toward transparent, auditable systems that can be reviewed and corrected over time. 

In India, this approach is particularly relevant given that women currently occupy roughly 12% of C-suite roles, considering their equally relevant experience.

Many commonly used leadership filters, such as uninterrupted career paths or elite institutional pedigrees, do not consistently predict performance, yet they continue to influence selection decisions.

Intentional AI systems can be configured to focus instead on measurable capabilities, decision-making patterns, and behavioural indicators that correlate more directly with effectiveness in leadership and complex roles.

Standardisation as a Tool for Fairness in Hiring

Human decision-making naturally favours familiarity, whether in educational background, work history, or communication style. Over time, this tendency can lead to homogeneous teams and missed talent.

AI can help counter this by introducing standardised evaluation frameworks at early hiring stages. Every candidate is reviewed using the same benchmarks, without variability based on external factors such as location, personal circumstances, or network proximity. This consistency allows organisations to process large volumes of applications efficiently while maintaining fairness.

In a labour market where women’s workforce participation has reached approximately 41.7%, such structured systems are increasingly necessary to ensure that talent visibility keeps pace with talent availability.

Ultimately, the goal is not to favour one group over another, but to ensure that opportunity is governed by capability and performance. A hiring ecosystem built on these principles strengthens competitiveness, innovation, and long-term economic progress outcomes that benefit organisations and society alike.

Authored by Taru Shikha, Founder and CEO, HiredNext Recruitment | Views expressed by the author are their own.