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How to Avoid Unconscious Bias in Recruitment

How to Avoid Unconscious Bias in Recruitment

Author
Nischal V Chadaga
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December 22, 2024
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4 min read
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Unconscious bias is one of the most significant challenges in hiring today. Despite organizations’ best intentions to create diverse and inclusive workplaces, biases—often unintentional—can influence hiring decisions. These biases can exclude qualified candidates and prevent organizations from building a truly diverse workforce, ultimately impacting innovation and productivity.

By understanding unconscious bias and taking proactive steps to mitigate it, recruiters can make more equitable, skills-driven hiring decisions.

What is unconscious bias in recruitment?

Unconscious bias refers to the automatic judgments and stereotypes we form about people based on factors like gender, age, race, ethnicity, education, or background. In recruitment, this bias can surface during resume screening, interviews, or even team assessments, leading to unfair hiring practices.

For example, a recruiter might favor candidates from prestigious universities or those with similar interests, even if these factors have no bearing on job performance.

Common types of unconscious bias in hiring

1. Affinity bias

This occurs when recruiters favor candidates who share similarities with them, such as hobbies, educational background, or personal traits.
Example in tech: A hiring manager who studied at a top-tier tech school like MIT might unconsciously favor candidates with similar alma maters, even when equally talented applicants from less prestigious institutions have comparable skills.

2. Confirmation bias

Recruiters form an early impression of a candidate—positive or negative—and then focus on details that confirm their initial judgment while ignoring contradictory evidence.
Example in tech: If a developer’s resume shows experience at a well-known tech company like Google, the recruiter might overlook their lack of exposure to specific tools or languages required for the role, assuming they’re already qualified.

3. Halo effect

A single positive trait overshadows other aspects of a candidate’s qualifications, leading to an unbalanced evaluation.
Example in tech: A candidate’s experience in building a popular open-source project might make a recruiter overlook their lack of teamwork experience, which is critical for agile development roles.

4. Gender and racial bias

Biases based on gender or ethnicity can lead to undervaluing a candidate’s abilities, even if unintentional.
Example in tech: A recruiter may unconsciously assume that women candidates are less proficient in systems engineering or DevOps roles, despite data showing no difference in technical capability across genders.

5. Name bias

Judging a candidate’s qualifications or personality based on their name, often tied to cultural or ethnic stereotypes.
Example in tech: A candidate named “Rajesh” may be assumed to have strong programming skills simply because of their name, or conversely, may face implicit doubts about their cultural fit in a predominantly Western tech team.

6. Experience bias

Overvaluing candidates from large or recognizable companies, while undervaluing those from smaller startups or self-taught backgrounds.
Example in tech: A developer from a major firm like Amazon might be preferred over a freelance coder with a robust portfolio, even though the latter has more relevant experience for the role at hand.

7. Age bias

Assuming younger candidates are better suited for roles requiring familiarity with cutting-edge technologies, or that older candidates are less adaptable.
Example in tech: A recruiter might favor a 25-year-old for a full-stack developer role requiring proficiency in React and Node.js, ignoring a 45-year-old candidate who’s equally skilled and experienced in modern frameworks.

Strategies to avoid unconscious bias in recruitment

Tech recruiters can counter these biases by implementing structured interviews, skill-based assessments, and blind hiring practices. HackerEarth’s PII masking feature, for example, anonymizes personal details like names and education during coding tests, ensuring candidates are judged only on their abilities. By focusing on skills, not assumptions, organizations can build more diverse and equitable tech teams.

1. Embrace structured hiring practices

Replace unstructured interviews with a structured approach where all candidates answer the same questions. This ensures fair comparisons based on relevant skills and performance, rather than subjective impressions.

2. Leverage skill-based assessments

Using skill-based tests is one of the most effective ways to avoid bias. These assessments focus solely on candidates’ abilities and problem-solving skills, removing factors like appearance, education, or background from the equation.

HackerEarth’s platform offers validated coding assessments and role-specific skill tests that provide unbiased insights into candidates’ technical capabilities. By focusing on what truly matters—skills—recruiters can eliminate bias in early hiring stages.

3. Implement blind hiring

Blind hiring removes identifiers like name, gender, age, or educational background from resumes and applications, ensuring candidates are evaluated based on their skills and experience alone.

Did you know? HackerEarth’s PII masking feature automatically anonymizes candidate information during assessments, making blind hiring seamless for recruiters and promoting fairness at scale.

4. Diversify hiring panels

Including a mix of genders, races, and professional backgrounds on hiring panels helps minimize the influence of individual biases. Diverse teams are also more likely to ask varied, inclusive questions that lead to better hiring decisions.

5. Use AI-driven tools cautiously

While AI can streamline recruitment, it’s essential to ensure these tools are free from embedded biases. Choose platforms like HackerEarth, which prioritize fairness and inclusivity through features like bias-free assessment design and analytics.

6. Provide unconscious bias training

Train hiring managers and recruiters to recognize and mitigate their biases. Awareness is the first step toward creating an equitable hiring process.

Measuring inclusivity in recruitment

Avoiding unconscious bias is not just a one-time effort. Regularly evaluate your hiring processes to ensure inclusivity. Key metrics include:

  • Diversity in candidate pools: Track how diverse your applicant and shortlisted candidate pools are.
  • Conversion rates: Analyze whether underrepresented groups are being hired at proportional rates.
  • Retention rates: Measure how successfully diverse hires integrate into your organization.

The role of HackerEarth Assessments in reducing bias

HackerEarth’s skill-first hiring philosophy ensures that talent is assessed based solely on their abilities. Features like:

  • Validated skill assessments: Evaluate candidates objectively with real-world scenarios.
  • PII masking: Remove personal identifiers to prevent bias during evaluation.
  • AI-driven insights: Use data to spot patterns and ensure hiring decisions are fair.

With HackerEarth, recruiters can confidently focus on merit and create equitable hiring practices, helping organizations build truly diverse teams.

Why bias-free hiring matters

Avoiding unconscious bias is not just about fairness; it’s a business imperative. Diverse teams are 70% more likely to capture new markets and deliver innovative solutions. By taking deliberate steps to eliminate bias, organizations can create a culture of inclusivity that attracts top talent and drives long-term success.

Adopt bias-free, skill-driven hiring practices to unlock the full potential of your recruitment efforts—and HackerEarth can help you lead the way.

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Author
Nischal V Chadaga
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December 22, 2024
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4 min read
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A New Era of CodeVibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today,...

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.Systems Design, in its...

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

  • Develop an understanding of basics of system design and architecture
  • Familiarize yourself with commonly asked systems design interview questions
  • Read about system design case studies for popular applications
  • Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

  • Plan the extent of the topics and scope of discussion in advance
  • Clearly define the evaluation criteria and communicate expectations
  • Quantify constraints, inputs, boundaries and assumptions
  • Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

  • Ask follow-up questions to challenge a solution
  • Probe candidates to gauge real-time logical reasoning skills
  • Make it a conversation and take notes of important pointers and outcomes
  • Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

  • Encourage candidates to explore and consider alternative solutions
  • Work with the candidate to drill the problem into smaller tasks
  • Provide context and supporting details to help candidates stay on track
  • Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.

How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers. The ease and...

Impact of Online Assessments in Technical Hiring


In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.

As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem



With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "

This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.

Common Cheating Tactics and How You Can Combat Them


  1. Using ChatGPT and other AI tools to write code

    Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
    • Debugging code
    • Optimizing existing code
    • Writing problem-specific code from scratch
    Ways to prevent it
    • Enable full-screen mode
    • Disable copy-and-paste functionality
    • Restrict tab switching outside of code editors
    • Use AI to detect code that has been copied and pasted
  2. Enlist external help to complete the assessment


    Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

    In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

    Ways to prevent it
    • Verify a candidate using video authentication
    • Restrict test access from specific IP addresses
    • Use online proctoring by taking snapshots of the candidate periodically
    • Use a 360 degree environment scan to ensure no unauthorized individual is present
  3. Using multiple devices at the same time


    Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.

    By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.

    Ways to prevent it
    • Track mouse exit count to detect irregularities
    • Detect when a new device or peripheral is connected
    • Use network monitoring and scanning to detect any smart devices in proximity
    • Conduct a virtual whiteboard interview to monitor movements and gestures
  4. Using remote desktop software and virtual machines


    Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.

    Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

    With remote desktops, candidates can screen share the test window and use external help.

    Ways to prevent it
    • Restrict access to virtual machines
    • AI-based proctoring for identifying malicious keystrokes
    • Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
  • Secure, sealed-off testing environment
  • AI-enabled live test monitoring
  • Enterprise-grade, industry leading compliance
  • Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
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