Sapia.ai, the leader in AI-powered chat interviewing, has announced a significant upgrade to its AI-generated content (AGC) detection system, initially launched in June 2023. The enhancement achieves groundbreaking accuracy, with a 98% detection rate for AI-assisted responses and a false positive rate of just 1%, ensuring fair and authentic candidate evaluations.
This innovation leverages Sapia.ai’s proprietary dataset of over 2 billion words, derived from more than 20 million interview question-answer pairs spanning pre-ChatGPT times to the present day.
“The hiring landscape has fundamentally changed since ChatGPT, but our commitment remains clear: AI should amplify human potential, not penalise it,” said Barb Hyman, CEO and founder of Sapia.ai. “This breakthrough fosters authentic hiring conversations. Our real-time warning system helps candidates make better choices and gives enterprises confidence in their selection decisions. It aligns with our mission of building ethical AI that people trust and love.”
“This enables us to maintain high accuracy even when candidates modify AI-generated responses to appear more human.”
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The system was rigorously tested on over 25,000 responses written by humans and those generated by leading language models like GPT-4, Claude-3.5, and Llama-3. Recent data from Sapia.ai’s platform shows that around 8% of candidates use AI-generated content in three or more responses. Instead of disqualifying these candidates, the platform offers real-time warnings, allowing them to revise their answers and present their true selves.
“Our detection model’s strength lies in its foundation of real-world interview data collected from diverse roles and regions,” said Dr Buddhi Jayatilleke, Chief Data Scientist at Sapia.ai. “This enables us to maintain high accuracy even when candidates modify AI-generated responses to appear more human.”
With this upgrade, Sapia.ai solidifies its position as a leader in ethical AI-powered hiring solutions. Its platform accelerates global hiring processes while ensuring integrity and fairness in candidate assessment.
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