Explainability
How well a system can show why it reached a decision. It matters most where the decision affects somebody — a loan, a diagnosis, a job application — and “the model said so” is not an answer.
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How well a system can show why it reached a decision. It matters most where the decision affects somebody — a loan, a diagnosis, a job application — and “the model said so” is not an answer.
Markup for ratings and reviews, including who wrote them. It feeds the star ratings in search results, and gives an assistant something concrete to cite about your reputation.
Turning spoken words into text. It underpins voice search, dictation and call transcription, and it now handles accents and background noise far better than it used to.
A system that suggests what somebody might want next based on behaviour — the “customers also bought” of an online shop. One of the oldest commercial uses of machine learning.
Whether a website can be reached, understood and quoted by AI assistants. It covers crawler access, structured data, clear headings, published contact details and speed — the unglamorous things that decide whether a model will name you.
Marking AI-generated content so it can be identified later, often invisibly. Part of the same effort as provenance, and equally early.
Turning written text into spoken audio. Modern voices are convincing enough to be used for real narration, which raises its own questions about disclosure.