Air Canada lost a tribunal case in 2024 because its chatbot invented a bereavement fare refund policy that did not exist. The airline treated a probabilistic text prediction as binding policy. That is the core design failure this article addresses: AI systems are probabilistic engines wrapped in deterministic interfaces. The output is a weighted guess. The interface presents it as fact. Users and organizations act accordingly, sometimes in medical diagnostics, financial forecasting, or customer-facing legal commitments.
The article's practical value is in how it reframes AI outputs as signals, not conclusions. A 60% purchase completion confidence and a 90% confidence are not the same design problem. The first requires persuasion mechanics: testimonials, comparisons, reassurance. The second requires friction removal. The author includes a structured prompt template for evaluating designs against neurodivergent user needs, covering layout, cognitive barriers, and content appropriateness, with a SWOT output and probability score. The caveat is explicit: simulations reflect historical behavior, not future change. A model trained on mobile interaction data will underpredict engagement for elderly voice-interface users, not because the concept fails, but because the dataset does.
The article also names the bias problem directly. Modi's example from the AI Summit in France: ask a model to generate an image of a left-handed writer and it often produces a right-handed one, because right-handedness dominates the training data. High confidence scores are not accuracy guarantees. A 40% signal is not automatically noise. The argument throughout is that probabilistic thinking is a design skill, not a technical one, and AI accelerates the need for it without replacing the judgment required to use it responsibly.
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