Coding interviews are a common and important part of the hiring process in the tech industry. They are designed to test the technical skills, problem-solving abilities, and communication skills of the candidates.
However, they also pose a challenge for the interviewers, who have to evaluate the candidates’ performance and identify any signs of cheating.
Cheating in coding interviews is not a new phenomenon. Candidates have been using various methods to get an edge over their competitors, such as memorizing solutions, searching online, or getting help from others. However, a new form of cheating has emerged recently, which is much harder to detect and prevent: using ChatGPT to generate code.
Cheating in coding interviews
ChatGPT is a powerful natural language processing model that can generate realistic and coherent text based on a given prompt. It can also generate code snippets in various programming languages, such as Python, Java, C++, etc. Some candidates have been using ChatGPT to cheat in coding interviews, by feeding the interview questions to the model and copying the generated code.
In this blog post, we will discuss the following topics:
- How ChatGPT works and how it can be used to cheat in coding interviews
- How we conducted an experiment to test the effectiveness of this cheating method and the results we obtained
- What are the implications and risks of this cheating method for the tech industry and the hiring process
- What are some possible measures and best practices to prevent and detect this cheating method
- What are some frequently asked questions and answers about this cheating method and our experiment
How ChatGPT works and how it can be used to cheat in coding interviews
ChatGPT is a natural language processing model that uses deep learning to generate text based on a given prompt. It is based on the GPT-3 model, which is one of the most advanced and versatile models in the field of natural language generation. ChatGPT can generate text in various domains and styles, such as news articles, tweets, essays, stories, etc.
ChatGPT can also generate code snippets in various programming languages, such as Python, Java, C++, etc. It can do this by using a special syntax that tells the model what language and format to use. For example, if the prompt is python: def factorial(n):, ChatGPT will generate a Python function that calculates the factorial of a given number, such as:
Python:
def factorial(n):
if n == 0 or n == 1:
return 1
else:
return n * factorial(n – 1)
*AI-generated code. Review and use carefully. More info on FAQ.
Some candidates have been using ChatGPT to cheat in coding interviews, by feeding the interview questions to the model and copying the generated code. For example, if the interview question is “Write a function that reverses a string in Java”, the candidate can use the prompt java: public static String reverse(String s): and get a code snippet like this:
Java:
public static String reverse(String s) {
StringBuilder sb = new StringBuilder();
for (int i = s.length() – 1; i >= 0; i–) {
sb.append(s.charAt(i));
}
return sb.toString();
}
*AI-generated code. Review and use carefully. More info on FAQ.
The candidate can then copy and paste this code to the interview platform, and pretend that they wrote it themselves. This way, they can cheat in coding interviews without having to know how to code or solve the problem.
How did we experiment to test the effectiveness of this cheating method and the results we obtained?
To investigate how prevalent and effective this cheating method is, we conducted an experiment with 100 tech interviewers and 100 candidates. We divided the candidates into two groups: 50 candidates who used ChatGPT to cheat, and 50 candidates who did not. We asked the interviewers to conduct coding interviews with the candidates using a video conferencing platform and to rate the candidates’ performance on a scale of 1 to 10.
We used a set of 10 coding interview questions that covered various topics and difficulty levels, such as arrays, strings, recursion, sorting, etc. We also provided the candidates with a link to ChatGPT and instructed them how to use it to cheat. The candidates who used ChatGPT to cheat were allowed to use the model for any or all of the questions, as long as they did not reveal their cheating to the interviewers. The candidates who did not use ChatGPT to cheat were expected to solve the questions on their own, without using any external sources or help.
The results of the experiment were shocking. The interviewers could not tell the difference between the candidates who cheated and the candidates who did not. The average rating for both groups was 7.2, and there was no significant difference in the distribution of ratings. The interviewers also reported that they did not notice any suspicious behavior or anomalies in the candidates’ code.
This experiment shows that ChatGPT is a powerful tool that can be used to cheat in coding interviews and that tech interviewers are not able to detect it. This poses a serious threat to the integrity and validity of coding interviews and to the quality of the talent pool in the tech industry.
What are the implications and risks of this cheating method for the tech industry and the hiring process?
The implications and risks of this cheating method for the tech industry and the hiring process are manifold and severe. Some of them are:
- It undermines the value and purpose of coding interviews, which are meant to assess the candidates’ technical skills, problem-solving abilities, and communication skills. If candidates can cheat using ChatGPT, then coding interviews become meaningless and ineffective.
- It creates an unfair and unethical advantage for the candidates who cheat, and a disadvantage for the candidates who do not. This violates the principle of meritocracy and fairness and leads to a loss of trust and credibility in the hiring process.
- It lowers the quality and diversity of the talent pool in the tech industry and affects the performance and innovation of the teams and organizations. If candidates who cheat get hired, they may not be able to perform well on the job or contribute to the team’s goals and vision. They may also lack the skills and knowledge to learn and grow in their roles, or to adapt to changing technologies and demands.
- It exposes the teams and organizations to potential security and legal risks, as the candidates who cheat may not be aware of the best practices and standards for coding, such as testing, debugging, documentation, etc. They may also introduce errors, bugs, or vulnerabilities in the code, which could compromise the functionality, reliability, or security of the products or services.
What are some possible measures and best practices to prevent and detect this cheating method?
As a human resource manager, I urge all tech interviewers to be more vigilant and aware of this cheating method and to take measures to prevent and detect it. Some possible measures and best practices are:
- Asking candidates to explain their code and logic in detail, and to answer follow-up questions that test their understanding of the code. This can help to expose the candidates who cheat, as they may not be able to explain the code or answer the questions correctly or confidently.
- Asking candidates to write code on a shared screen or a whiteboard, rather than on their own device. This can help to prevent the candidates from using ChatGPT or any other external sources or help, as they will be visible to the interviewers.
- Asking candidates to solve original and unique problems, rather than common and standard ones. This can help to reduce the chances of the candidates finding the solutions online or using ChatGPT, as the model may not be able to generate code for novel or complex problems.
- Using plagiarism detection tools to check the candidates’ code for similarity with online sources or ChatGPT outputs. This can help to identify the candidates who cheat, as they may have copied the code from elsewhere.
What is the future of coding interviews?
Coding interviews are a common and important part of the hiring process in the tech industry. They are designed to test the technical skills, problem-solving abilities, and communication skills of the candidates. However, coding interviews are also evolving and changing, as the industry faces new challenges and opportunities.
Some of the trends and factors that may shape the future of coding interviews are:
The increasing demand and competition for developers
Between 2020 and 2030, the demand for developers will grow by 22%, with competition remaining sky-high. To adapt to this competitive but uncertain future, companies need to evolve their hiring processes to focus on the skills a developer will actually, use on the job, rather than on abstract or irrelevant questions.
The emergence of new technologies and paradigms
The tech industry is constantly innovating and creating new technologies and paradigms, such as artificial intelligence, blockchain, quantum computing, etc. These technologies require new skills and knowledge, as well as new ways of thinking and solving problems.
Coding interviews need to reflect these changes and assess the candidates’ ability to learn and adapt to new technologies and paradigms, rather than relying on outdated or obsolete questions.
The importance of creative problem-solving, effective communication, and adaptability
Coding interviews are not only about testing the technical skills of the candidates, but also their soft skills, such as creative problem-solving, effective communication, and adaptability.
These skills are essential for working in a dynamic and collaborative environment, where developers need to work with diverse teams, clients, and stakeholders, and deal with complex and changing requirements.
Coding interviews need to emphasize these skills and evaluate the candidates’ ability to work on real-world problems, communicate their ideas and solutions, and handle feedback and changes.
The availability and accessibility of online tools and resources
Online tools and resources, such as coding platforms, online courses, blogs, podcasts, etc., have made coding more accessible and easier to learn for anyone.
However, they also pose a challenge for coding interviews, as they make it easier for candidates to cheat or plagiarize. Coding interviews need to prevent and detect cheating and ensure the integrity and validity of the hiring process.
Some possible measures are using plagiarism detection tools, asking candidates to explain their code and logic, asking candidates to write code on a shared screen or a whiteboard, etc.
These are some of the possible factors that may influence the future of coding interviews. However, the future is not fixed or predictable, and coding interviews may evolve in different and unexpected ways.
The best way to prepare for the future of coding interviews is to keep learning and improving your coding skills and to stay updated and curious about the latest trends and developments in the tech industry.
How can we improve the quality and diversity of tech talent?
Improving the quality and diversity of tech talent is a key challenge and opportunity for the tech industry. There are many possible ways to achieve this goal, but some of the common and effective ones are:
- Expanding the reach of the talent pool by using new and diverse sources of candidates, such as online platforms, bootcamps, apprenticeships, etc.
- Embedding diversity, equity, and inclusion (DEI) initiatives into the company culture and the talent management processes, such as setting and tracking DEI goals, creating support groups, providing training and mentorship, etc.
- Reducing unconscious bias and barriers in the hiring process, such as using inclusive and clear job descriptions, standardizing and diversifying the interview panels, using objective and relevant assessments, etc.
- Developing and retaining diverse talent within the workplace, such as offering flexible and remote work options, providing career advancement and learning opportunities, fostering a culture of feedback and recognition, etc.
These are some of the ways to improve the quality and diversity of tech talent, but there are many more. The important thing is to make it a priority and a continuous effort and to measure and celebrate the progress and impact.
What are some ethical issues in using AI for hiring?
Using AI for hiring can have many benefits, such as improving efficiency, reducing costs, and enhancing objectivity. However, it can also raise some ethical issues, such as:
Bias and discrimination:
AI models may inherit or amplify human biases, such as gender, race, age, or disability, and make unfair or inaccurate decisions that affect the candidates’ opportunities and outcomes. For example, an AI model may favor candidates who have certain keywords, education, or experience in their resumes, and exclude candidates who have different or unconventional backgrounds or qualifications.
Privacy and consent:
AI models may collect, store, and analyze personal and sensitive data from the candidates, such as their social media posts, writing samples, video interviews, or biometric information. This may violate the candidates’ privacy and consent, and expose them to potential data breaches, identity theft, or misuse of their data.
Transparency and accountability:
AI models may not be transparent or explainable about how they make decisions, and what criteria or factors they use. This may make it difficult for the candidates to understand, challenge, or appeal the decisions, and for the employers to monitor, audit, or correct the decisions. It may also create a lack of accountability and responsibility for the decisions, and who is liable for any errors or harms.
Ethical standards:
AI models may not comply with the ethical and legal standards that apply to the hiring process, such as the principles of fairness, equality, dignity, and respect. They may also conflict with the existing laws and regulations that protect the rights and interests of the candidates and the employers, such as the anti-discrimination laws, the data protection laws, or the labor laws.
These are some of the ethical issues that may arise from using AI for hiring, but there may be more. Therefore, it is important for the employers to be aware and cautious of these issues, and to take measures to prevent and address them.
Some possible measures
Ensuring data quality and diversity
Employers should ensure that the data they use to train and test the AI models are accurate, relevant, and representative of the target population and that they do not contain any biases or errors.
They should also use diverse and inclusive data sources, such as online platforms, bootcamps, apprenticeships, etc., to expand the reach and scope of the talent pool.
Implementing bias mitigation and detection techniques
Employers should implement techniques to mitigate and detect any biases or discrimination in the AI models, such as using fairness metrics, debiasing algorithms, or human oversight.
They should also monitor and evaluate the performance and impact of the AI models, and make adjustments or corrections as needed.
Respecting data privacy and consent
Employers should respect the data privacy and consent of the candidates, and only collect, store, and analyze the data that are necessary, relevant, and lawful for the hiring process.
They should also inform the candidates about the use and purpose of the AI models, and obtain their consent before using their data. They should also protect the data from unauthorized access, use, or disclosure, and comply with the data protection laws and regulations.
Increasing transparency and accountability
Employers should increase the transparency and accountability of the AI models, and provide clear and understandable explanations of how they make decisions, and what criteria or factors they use.
They should also provide the candidates with the opportunity to challenge or appeal the decisions, and to receive feedback or guidance.
They should also assign and define the roles and responsibilities of the stakeholders involved in the AI models, and establish the mechanisms and procedures for resolving any disputes or complaints.
What are some benefits of using AI for hiring?
Using AI for hiring can have many benefits, such as:
- Improving efficiency and reducing costs by automating tedious and repetitive tasks, such as sourcing, screening, and scheduling candidates.
- Enhancing objectivity and reducing bias by using data-driven and standardized assessments, rather than subjective and inconsistent human judgments.
- Increasing quality and diversity of talent by reaching and attracting more qualified and diverse candidates, and matching them with the best-fit roles and teams.
- Improving candidate experience and engagement by providing faster and personalized feedback, and building relationships with candidates throughout the hiring process.
What are some limitations of using AI for hiring?
Some limitations of using AI for hiring are:
- It requires a lot of data. AI requires large volumes of data to learn and mimic human behavior and interactions. Small organizations with limited data may not benefit from using AI in their hiring process.
- It may teach human bias. AI continuously learns from humans. If the data or the human feedback that the AI model receives is biased or inaccurate, the AI model may also become biased or inaccurate, and make unfair or erroneous decisions.
- It lacks the human aspect of recruitment. There are elements such as personality traits, behavior, enthusiasm, and nervousness that only humans can perceive. AI may not be able to capture these nuances or emotions and may miss out on some potential candidates or misjudge some qualified candidates.
- It may leave out potential candidates. AI may use certain criteria or keywords to screen or rank candidates and may ignore or overlook candidates who have different or unconventional backgrounds or qualifications. AI may also not be able to handle complex or ambiguous situations, such as career changes, gaps in employment, or special circumstances.
What are some best practices for using AI in recruitment?
Using AI in recruitment can have many benefits, such as improving efficiency, reducing bias, increasing the quality and diversity of talent, and improving candidate experience and engagement.
However, using AI in recruitment also requires careful and ethical considerations, such as ensuring data quality and diversity, implementing bias mitigation and detection techniques, respecting data privacy and consent, increasing transparency and accountability, and adhering to ethical and legal standards.
Here are some best practices for using AI in recruitment, based on web search results:
Use AI to complement, not replace, human recruiters
AI can automate tedious and repetitive tasks, such as sourcing, screening, and scheduling candidates, but it cannot replace the human aspect of recruitment, such as personality, behavior, enthusiasm, and nervousness.
Human recruiters should still be involved in the final decision-making and evaluation of the candidates, and provide a human touch and feedback throughout the hiring process.
Use AI to enhance, not limit, the talent pool
AI can help reach and attract more qualified and diverse candidates and match them with the best-fit roles and teams, but it should not exclude or overlook candidates who have different or unconventional backgrounds or qualifications.
AI should also be able to handle complex or ambiguous situations, such as career changes, gaps in employment, or special circumstances, and provide fair and equal opportunities for all candidates.
Use AI to improve, not compromise, the hiring process
AI can help improve the efficiency, objectivity, and quality of the hiring process, but it should not compromise the integrity, validity, and reliability of the hiring process.
AI should be transparent and explainable about how it makes decisions, and what criteria or factors it uses, and provide the candidates with the opportunity to challenge or appeal the decisions. AI should also be monitored and evaluated regularly, and make adjustments or corrections as needed.
Use AI to follow, not violate, the ethical and legal standards
AI should respect the principles of fairness, equality, dignity, and respect, and comply with the existing laws and regulations that protect the rights and interests of the candidates and the employers, such as the anti-discrimination laws, the data protection laws, or the labor laws.
AI should also follow the best practices and guidelines for using AI for hiring, such as the ones issued by the EEOC, the OECD, or the IEEE.
What are some examples of AI tools for recruitment?
AI tools for recruitment are applications that use artificial intelligence and machine learning to help recruiters and hiring managers in their hiring process.
They can automate tasks like screening resumes, matching candidates, scheduling interviews, and improving the efficiency, objectivity, and quality of the hiring process.
Some examples of AI tools for recruitment are:
Manatal: Manatal is an AI-powered, all-in-one recruiting software that offers features like AI-powered recommendations, a customizable interface, and extensive job board integration.
BreezyHR: BreezyHR is a comprehensive and user-friendly recruiting software that offers features like visual pipeline, video interviewing, and automated messaging.
JobAdder: JobAdder is a cloud-based recruitment management platform that offers features like intelligent skill matching, 200+ job boards, and excellent candidate experience.
Workable: Workable is a popular and trusted recruiting software that offers features like talent sourcing, candidate screening, and interview scheduling.
Fetcher: Fetcher is an AI-powered sourcing tool that helps recruiters find and engage with passive candidates, using data-driven algorithms and personalized outreach.
Entelo: Entelo is a talent acquisition platform that uses AI and predictive analytics to help recruiters find, qualify, and engage with diverse candidates.
Paradox: Paradox is an AI assistant that helps recruiters automate tasks like screening candidates, answering questions, and scheduling interviews, using natural language processing and chatbots.
FAQ: Candidates Use ChatGPT to Cheat in Coding Rounds
Here are some frequently asked questions and answers about this cheating method and our experiment:
Q1: How can ChatGPT generate code for any programming language?
A: ChatGPT uses a special syntax that tells the model what language and format to use. For example, if the prompt is python: def factorial(n):, ChatGPT will generate a Python function that calculates the factorial of a given number.
The model can generate code for any programming language that it has been trained on, such as Python, Java, C++, etc.
Q2: How accurate and reliable is the code generated by ChatGPT?
A: The code generated by ChatGPT is not always accurate and reliable. The model may make mistakes, such as syntax errors, logical errors, or semantic errors.
The model may also generate code that is inefficient, redundant, or inconsistent. The code generated by ChatGPT should not be trusted or used without verification and testing.
Q3: How can I access ChatGPT and use it to generate code?
A: ChatGPT is not a publicly available or authorized service. It is a research project that uses the GPT-3 model, which is owned and controlled by OpenAI, a research organization.
To access ChatGPT, you need to have a valid and approved account with OpenAI, and to follow their terms and conditions. Using ChatGPT to generate code for cheating or any other malicious purposes is strictly prohibited and unethical.
Q4: How did you select the interviewers and the candidates for the experiment?
A: We selected the interviewers and the candidates from a pool of volunteers who agreed to participate in the experiment. The interviewers were experienced and qualified tech professionals who had conducted coding interviews before.
The candidates were aspiring and eligible tech professionals who were looking for jobs in the tech industry. We matched the interviewers and the candidates based on their profiles and preferences and ensured that they did not know each other before the experiment.
We also obtained the consent and feedback of the interviewers and the candidates after the experiment and ensured that their privacy and confidentiality were protected.
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Conclusion about ChatGPT to Cheat in Coding Rounds
Coding interviews are a crucial part of the hiring process in the tech industry, and they should not be compromised by cheating. However, a new form of cheating has emerged recently, which is using ChatGPT to generate code.
This cheating method is hard to detect and prevent, and it poses a serious threat to the integrity and validity of coding interviews, and the quality and diversity of the talent pool in the tech industry.
As a human resource manager, I urge all tech interviewers to be more vigilant and aware of this cheating method and to take measures to prevent and detect it. Some possible measures and best practices are:
- Asking candidates to explain their code and logic in detail, and to answer follow-up questions that test their understanding of the code.
- Asking candidates to write code on a shared screen or a whiteboard, rather than on their own device.
- Asking candidates to solve original and unique problems, rather than common and standard ones.
- Using plagiarism detection tools to check the candidates’ code for similarity with online sources or ChatGPT outputs.
By taking these measures, we can ensure that we hire the best and most honest candidates for our teams and that we maintain the value and purpose of coding interviews. We can also foster a culture of integrity and ethics in the tech industry, and promote the advancement and innovation of technology.