TL;DR
AI tools have made it nearly impossible to distinguish skilled candidates from those faking competence. The core issue is that if everyone uses AI to answer every question, hiring becomes a lottery.
- Candidates using AI assistance is inevitable, but the ethics depend on transparency and explanation ability
- The line between acceptable use and cheating lies in whether candidates can explain their reasoning
- Recruiters must redefine fairness by testing understanding, not just answers
- Detection requires moving beyond traditional proctoring to behavioral and conversational analysis
- Companies that adapt their processes will find authentic talent; those that don't will hire subscriptions
Introduction
A hiring manager recently shared a frustrating experience: out of 20 technical interview candidates, 18 gave nearly identical answers to a complex system design question. The responses were polished, comprehensive, and structured with perfect bullet points. They were also almost certainly generated by AI.
This scenario raises a question that has no easy answer: Is using AI in interviews cheating, or is it just smart preparation?
The debate has grown heated because both sides have valid points. Candidates argue that AI is simply another tool, like spell-checkers or calculators. Recruiters counter that they need to assess actual human capability, not subscription services. Somewhere between these positions lies an ethical boundary that the hiring industry is scrambling to define.
This post explores where that boundary exists, why the old rules no longer apply, and how recruiters can build fair processes that identify genuine talent in an AI-saturated world.
Why has AI made interview ethics so complicated?
The ethics were simpler when cheating required obvious actions: bringing notes into an exam room, having someone else take your test, or lying on a resume. These behaviors had clear intent and clear detection methods.
AI assistance exists in a gray zone because the technology has become invisible and ubiquitous. Tools like ChatGPT, Cluely, and Interview Coder can run as invisible overlays on a candidate's screen, feeding real-time answers while the interviewer sees nothing unusual. The candidate appears thoughtful and articulate. The AI does the heavy lifting.
This creates a fundamental problem for hiring. If two candidates give the same quality answer, but one derived it through years of experience and the other read it from a hidden screen, the interview has failed to measure what it was designed to measure.
The complication deepens because AI use exists on a spectrum:
- Preparation assistance Using AI to practice interview questions, understand concepts, or improve communication skills before the interview.
- Real-time support Having AI tools generate answers during the interview itself, whether through invisible overlays, secondary devices, or audio prompts.
- Complete substitution Using AI to entirely replace human thinking, with the candidate serving as little more than a voice relay for generated content.
Most people would agree that category one is acceptable and category three is fraud. The challenge lies in drawing a clear line that everyone can understand and follow.
Where is the ethical boundary between AI assistance and cheating?
The ethical boundary comes down to one principle: candidates should be allowed to use AI, as long as they can explain their reasoning and demonstrate genuine understanding.
This principle recognizes a practical reality. Banning AI entirely is unenforceable and arguably counterproductive. Workers in nearly every industry now use AI tools daily. Testing whether someone can work without AI may not even be relevant to job performance.
However, the principle also protects what matters most in hiring: finding people who can think, adapt, and solve problems when the AI gives them a wrong answer or no answer at all.
Consider two scenarios:
Scenario A: A candidate uses AI to help prepare for a technical interview. During the interview, they provide a solid answer to a system design question. When the interviewer asks follow-up questions about trade-offs, edge cases, or alternative approaches, the candidate engages thoughtfully and demonstrates clear understanding of why they chose specific solutions.
Scenario B: A candidate uses AI during the interview to generate answers in real-time. They deliver the same initial answer as Scenario A. But when asked follow-up questions, they pause for exactly four seconds (waiting for the AI to process), then provide another perfectly structured response that sounds memorized rather than reasoned.
The difference is not whether AI was involved. The difference is whether the candidate owns the knowledge or is merely relaying it.
This framing shifts the conversation from detection to verification. Instead of asking "Did they use AI?", recruiters can ask "Do they understand what they told us?"
The ethical line becomes clear: using AI to enhance your existing capabilities is acceptable. Using AI to fabricate capabilities you do not possess is fraud.
How can recruiters define fairness in modern hiring?
Fairness in hiring has always meant giving candidates an equal opportunity to demonstrate their abilities. In an AI-enabled world, this definition needs expansion: fairness now also means ensuring the assessment actually measures the candidate, not their tools.
Recruiters can build fair processes by following three principles:
1. Test understanding, not recall
Static questions with static answers are now essentially worthless. Any question that can be googled or fed to an AI will produce interchangeable responses across candidates.
Fair assessments focus on reasoning. Instead of asking "How would you design a rate limiter?", ask the candidate to design one, then probe their choices: "Why did you choose this approach over alternatives? What would break if traffic doubled? Tell me about a time this approach failed for you."
These follow-up questions force candidates off-script. AI tools struggle to maintain coherent context when rapidly switching between technical details and personal experiences, especially negative ones.
2. Make expectations explicit
Many candidates genuinely do not know where the line is. They have grown up using AI assistants and may not distinguish between using one for homework versus using one during an assessment.
Fair hiring requires clear communication. Tell candidates upfront what is and is not acceptable. Explain that AI preparation is fine, but real-time AI assistance will be treated as misrepresentation. This gives honest candidates clarity and removes any ambiguity that bad actors might exploit.
3. Adapt the process to the threat
Take-home assignments have become nearly impossible to evaluate fairly. With AI tools completing most standard coding challenges in under five minutes, these assessments now measure AI proficiency more than candidate skill.
Fair processes acknowledge this reality. Live assessments with real-time interaction remain far more resistant to AI assistance because they require immediate responses to unpredictable questions. Conversational formats where interviewers can drill down on any answer create conditions where cheating tools provide limited advantage.
How can companies detect when candidates cross the line?
Detection has evolved beyond traditional proctoring methods like monitoring tab switches or requiring browser lockdowns. Modern cheating tools bypass these measures entirely through invisible overlays and secondary devices.
Effective detection now focuses on behavioral signals that cheating tools cannot hide:
Response timing patterns
Natural human responses vary in timing. Simple questions get quick answers; complex ones require thought. Candidates using AI assistance often show suspiciously consistent delays, typically three to five seconds, regardless of question difficulty. This happens because the AI processing chain takes roughly the same time whether answering "What's your name?" or "Explain distributed consensus algorithms."
Eye movement signatures
When people recall information, their eyes tend to drift upward or to the side. When people read text, their eyes move horizontally in a steady left-to-right pattern with quick snaps back to start the next line. Candidates reading from invisible overlays display this reading pattern even while appearing to speak spontaneously.
Language coherence
AI-generated responses tend toward perfect grammar, rigid structure, and formulaic phrases. Human speech includes hesitations, self-corrections, and natural variation. When a candidate suddenly shifts from conversational language to suspiciously polished prose, or uses vocabulary mismatched with their experience level, these signal potential AI assistance.
Contextual consistency
Cheating tools struggle with rapid context switches. Asking a candidate to explain their answer differently, connect it to a personal failure, or apply it to an unexpected scenario often breaks the coherence of AI-assisted responses.
Platforms like Fabric have built detection systems that analyze these signals in combination. Rather than relying on any single indicator, which can produce false positives, Fabric's approach fuses behavioral, audio, and content signals to generate probability scores indicating likelihood of synthetic assistance. This allows recruiters to verify authenticity while avoiding the rejection of nervous but honest candidates.
Conclusion
The question is no longer whether candidates will use AI. They will. The question is whether your hiring process can distinguish between candidates who have genuinely developed skills and those who are renting competence through subscription services.
The ethical boundary is clearer than the debate suggests: AI assistance that enhances genuine capability is acceptable. AI assistance that fabricates capability is fraud. The test is simple: can the candidate explain their reasoning, adapt their thinking, and demonstrate understanding beyond what they initially stated?
Companies that redesign their processes around this principle will continue to find authentic talent. Those that cling to static assessments and traditional proctoring will increasingly hire candidates who are skilled at one thing only: using AI to pass interviews.
FAQ
Is it cheating if a candidate uses ChatGPT to prepare for an interview?
No. Using AI to practice questions, understand concepts, or improve communication before an interview is similar to using any other study resource. The line is crossed when AI generates answers during the assessment itself.
How can interviewers tell if someone is using AI in real-time?
Key signals include consistent response delays regardless of question difficulty, reading eye movements while speaking, suspiciously polished language, and inability to elaborate when asked follow-up questions. Combining multiple signals provides more reliable detection than any single indicator.
What is Fabric?
Fabric is an AI-powered interview platform that conducts conversational technical assessments while detecting cheating through analysis of behavioral, audio, and content signals. The platform uses adaptive questioning to verify candidate understanding and generates integrity scores based on over 20 detection signals.
Can companies ban AI use in interviews entirely?
They can try, but enforcement is nearly impossible with current technology. Invisible overlay tools and secondary devices bypass most detection methods. A more effective approach focuses on testing understanding rather than banning tools.
How does Fabric help recruiters maintain fair hiring processes?
Fabric replaces static assessments with dynamic, conversational interviews that adapt based on candidate responses. When candidates provide answers, Fabric's AI interviewer probes for deeper understanding, asks about failures and edge cases, and switches contexts rapidly. This approach makes AI assistance less useful while the platform's detection engine identifies behavioral signals associated with synthetic responses.