A bank introduces an AI-based employee-performance system. The system predicts that certain employees have a high probability of leaving within six months. HR immediately labels these employees "low commitment" and excludes them from leadership-development programmes. Subsequently, some of these employees actually resign. Which interpretation is most methodologically and HR-wise appropriate?

This is a high-level HR analytics + behavioural HRM question. The AI initially predicts that certain employees are likely to leave. Management then: 1. Labels them as low commitment. 2. Removes development opportunities. 3. Potentially reduces their career prospects. 4. Employees subsequently leave. The eventual turnover does not necessarily prove that the original prediction was inherently correct. The organisation's intervention may have contributed to the outcome. This is a possible self-fulfilling prophecy: Prediction → organisational treatment → employee response → predicted outcome. Why the other options fail A: Prediction is not proof of causation. B: The intervention itself may have influenced subsequent turnover. D: Predictive analytics can be useful, but it must be interpreted carefully and ethically. E: Organisational treatment can influence turnover. Therefore, C is the strongest answer.