AI Predicts Driver Crash Risk — But Experts Warn About Ethics, Privacy

AI Predicts Driver Crash Risk — But Experts Warn About Ethics, Privacy
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Scientists at the University of Sharjah have developed an artificial‑intelligence model that can assess a driver’s likelihood of being involved in a crash before they sit behind the wheel. The system combines psychological assessments, physiological signals (like heart rate and eye movements), and performance in a driving simulator to classify drivers as lower‑ or higher‑risk — potentially transforming how transport and taxi companies screen and train drivers.

The researchers say the model could make hiring safer and more evidence‑based, help design personalized training programs, and shift safety management from reactive to preventive — potentially reducing crashes, insurance costs, and improving public trust in road safety.

However, the proposal has raised ethical and privacy concerns among technology and data experts. Machine‑learning systems like this require collecting and processing sensitive personal information — including biometric data and personality traits — which could raise risks of privacy invasion, unauthorized access, or misuse of data if safeguards aren’t strong.

Critics also warn that using personal traits in automated risk scoring could lead to biased decisions or reinforce unfair stereotypes if the algorithms are not transparent and carefully audited. There’s also debate over who would be responsible if such a prediction system influences hiring decisions or safety interventions — the developers, employers, or regulators.