How Mentoring Can Help Break the Cycle of Bias in the Workplace

Discover how to reduce bias in the workplace through inclusive mentoring and smart matching technology.

Bias in the workplace is a subtle yet pervasive force that can undermine efforts to build inclusive, equitable, and high-performing organisations. Whether it’s in recruitment, mentoring, or day-to-day decision-making, unconscious biases can influence who gets hired, who gets promoted, and who receives development opportunities. 

As businesses across Australia place greater emphasis on diversity, equity, and inclusion (DEI), understanding the different types of bias — and how they manifest — is more important than ever. 

From similarity bias to authority bias, recognising these patterns is the first step toward creating fairer systems and more inclusive cultures.

Understanding Bias in the Workplace

Bias refers to a predisposition or prejudice towards or against something or someone, often in a way considered to be unfair. In professional settings, biases can be conscious or unconscious and can influence decisions related to hiring, promotions, and mentorship.​

Common Types of Workplace Bias

• Affinity Bias (Similarity Bias): Affinity bias occurs when individuals favor others who share similar backgrounds, interests, or experiences. This can lead to homogeneous teams and hinder diversity. 

• Confirmation Bias: This bias involves seeking out information that confirms existing beliefs while disregarding contradictory evidence. In hiring, this may result in overlooking qualified candidates who don't fit preconceived notions.

• Halo Effect: The halo effect happens when a positive impression in one area influences opinions in other areas. For example, assuming someone is competent because they are well-dressed.

• Horns Effect: The opposite of the halo effect, the horns effect involves allowing one negative trait to overshadow other qualities, potentially leading to unfair assessments.

• Authority Bias: This bias leads individuals to attribute greater accuracy to the opinion of an authority figure, which can suppress diverse viewpoints and innovation.​

• Proximity Bias: Proximity bias favors those who are physically closer, often disadvantaging remote workers in terms of recognition and opportunities.

• Gender Bias: Gender bias involves preferring one gender over another, often based on stereotypes, which can affect hiring and promotion decisions.

How Bias Affects Mentoring and Recruitment

Bias — whether unconscious or intentional — plays a significant role in shaping the opportunities individuals receive in the workplace. In mentoring and recruitment, where relationships and decisions often hinge on human judgement, bias can subtly influence outcomes in ways that perpetuate inequality and limit diversity.

Bias in Manual Mentoring Matches

In traditional mentoring programs, pairing mentors and mentees is often managed manually by administrators or executives. While well-intentioned, these manual matches are especially vulnerable to unconscious bias.

For example:

• Similarity bias might lead an administrator to match people based on shared background, gender, or communication style, rather than on developmental goals or complementary skills.
• Attraction bias — favouring individuals perceived as more charismatic or personable — might lead to certain employees being selected for mentoring more frequently.
• Halo and horns effects can cause decision-makers to overvalue or undervalue participants based on limited impressions or past interactions.

These patterns can reinforce existing power structures and exclude those from underrepresented backgrounds. Employees who don’t “fit the mould” might be passed over for valuable mentoring opportunities, which are known to be linked to career progression, retention, and leadership development.

Gatekeeper Bias in Participant Approval

In some organisations, participation in mentoring programs must be approved by managers or executives. This adds another layer where bias can creep in.

Gatekeepers may unconsciously favour:

• Employees who share their background or values
• Those who are more visible or vocal (proximity bias)
• High performers from majority groups while overlooking emerging talent from diverse backgrounds

This selective access means that the very employees who could benefit most from mentorship — such as women, culturally and linguistically diverse (CALD) individuals, LGBTQIA+ staff, and those with disabilities — may be unintentionally excluded.

Moreover, feedback mechanisms within mentoring programs are also vulnerable. A mentor may rate a mentee lower not because of lack of ability, but because of communication differences, cultural misunderstandings, or internalised bias.

Bias in Recruitment and Hiring

The recruitment process is rife with opportunities for bias to manifest, especially when it’s conducted manually or without structured criteria. This includes:

• Name bias, where candidates with non-Anglo names are less likely to be shortlisted
• Gender bias, which can influence perceptions of leadership potential or salary expectations
• Age bias, where older candidates are viewed as less adaptable
• Confirmation bias, where hiring managers seek evidence to support their initial impression rather than evaluating candidates objectively

Even the language used in job descriptions can deter diverse candidates from applying. Words like “competitive,” “aggressive,” or “dominant” are often associated with masculine stereotypes and may alienate women or non-binary individuals.

Once hired, bias can continue to affect the onboarding, development, and promotion processes — ultimately leading to disparities in advancement and retention.

Impact on Organisational Culture

Unchecked bias in mentoring and recruitment contributes to a cycle where underrepresented groups remain under-mentored, underdeveloped, and under-promoted. This not only affects the individuals directly involved but also weakens the organisation’s overall culture and performance.

• It fosters a lack of psychological safety, where employees feel they must conform to dominant norms to succeed.
• It limits innovation by stifling diverse perspectives.
• It affects employee engagement and turnover, particularly among marginalised groups who feel excluded or overlooked.

Ultimately, if mentoring programs and hiring practices are influenced by bias, they fail to deliver on their promise of equitable development and career progression. This underscores the importance of intentionally designing systems and processes that actively mitigate bias and promote fairness.

How Technology Mitigates Bias

While awareness training and policy change are important first steps in addressing workplace bias, one of the most powerful tools available today is technology. Software designed with diversity, equity and inclusion (DEI) in mind can play a critical role in reducing the impact of unconscious bias, particularly in processes such as mentoring, recruitment, performance reviews, and talent development.

Blind Matching Algorithms in Mentoring Programs

In traditional mentoring programs, mentor-mentee matches are often made manually by administrators or senior leaders. This opens the door to unconscious biases — for example, favouring those who appear more confident, are more familiar, or come from a similar background.

Modern mentoring software, however, can employ blind or semi-blind matching algorithms that rely solely on relevant data such as skills, goals, experience level, and development interests. Personal details like names, photos, or demographic information can be anonymised during the match-making process to prevent affinity bias or attraction bias from influencing decisions.

This data-led approach ensures that matches are based on compatibility and growth potential, rather than subjective judgement.

AI-Powered Candidate Screening Tools

In recruitment, bias often creeps in during résumé screening or initial interviews. Tools that use AI to assess candidates can mitigate this by focusing on job-relevant criteria rather than surface-level traits. For instance:

• Résumé anonymisation features can remove identifiers such as name, age, gender, or educational institution, which are often linked to systemic biases.
• Structured interview platforms can score responses based on key competencies and pre-set rubrics, reducing the influence of interviewer bias.
• Predictive analytics can help identify candidates with the highest likelihood of success based on their experience and skills, not their background.

However, it's essential that these systems are designed ethically and are regularly audited for fairness. AI is not inherently neutral — it reflects the data it is trained on — so vigilance is necessary to avoid encoding historical bias into new systems.

Bias-Detection Tools for Communication and Content

Several software platforms now offer bias detection in language — whether it's in job ads, performance reviews, or internal communications. These tools can flag potentially biased or exclusive language and suggest more inclusive alternatives.

For example, gendered terms like “rockstar” or “ninja” in job ads may deter women or older applicants. Bias-checking software can recommend neutral phrases that appeal to a broader range of candidates.

Within mentoring, this type of technology can be used to analyse mentor and mentee feedback, offering insights into any unconscious trends or discrepancies in language, tone, or evaluations across different demographics.

Real-Time Analytics and DEI Dashboards

Technology can also help organisations monitor their progress on inclusion by providing real-time data visualisations and DEI dashboards. These tools can:

• Track participation and outcomes across different groups (e.g., gender, ethnicity, location)
• Identify drop-off points in mentoring pipelines
• Highlight whether underrepresented employees are receiving equitable access to mentorship and development opportunities

By surfacing these insights, organisations can intervene early, address gaps, and make evidence-based decisions to improve their programs and policies.

Scalability and Consistency

A major advantage of using technology in DEI efforts is scalability. While manual processes might work for small teams, they become error-prone and inconsistent at scale. Software platforms can apply the same logic and criteria across thousands of users, ensuring fairness and consistency across the board.

For example, in a global mentoring program, technology can account for time zones, languages, and learning styles while still applying a bias-aware matching algorithm, creating a more inclusive experience for everyone involved.

How Brancher Addresses Bias in the Workplace Through Mentoring

Bias in the workplace, particularly in mentoring and development programs, can quietly undermine even the best-intentioned diversity and inclusion efforts. Without safeguards, manual decision-making processes tend to favour the familiar — often leaving underrepresented employees behind.

Brancher tackles this challenge head-on by minimising bias from the mentoring equation. Through data-driven matching, equitable access, structured feedback, and real-time DEI analytics, Brancher empowers organisations to build mentoring programs that are not only scalable and efficient, but truly inclusive.

Whether you're looking to support emerging leaders, connect cross-functional teams, or create equitable development pathways — Brancher provides the tools to do it fairly and effectively.

Ready to see how it works? Book a demo today and discover how Brancher can help your organisation deliver mentoring that drives real impact — for everyone.