'Using Explainable AI in Decision-Making Applications' ai explainableai
The doctor’s task in this case, is to strip the information down to visually analyze the image to find suspicious patterns in the cellular structure. Based on the analysis, the doctor will provide diagnosis, or make a decision for surgical intervention.
The advantage of such systems is not only in operational speed and greater accuracy of analysis. The image can also be manipulated in different ways to provide a fuller picture for diagnostics and to make more accurate surgical decisions if needed. You can check theExplainable AI for text processing If suddenly the system gives a signal that there is a high probability that a person can harm himself, then this information is submitted to the community operations team for manual processing and verification. Explainable AI supports teams with constant scanning of media fields to find risky commentaries. After that, the human agent can use data explanations presented by the model that will point to how the machine understood if that unique person is showing the risk for suicide.
The explainability part is responsible for justifying the data by recognizing company, event, persona, or resource entities with named entity recognition technology. This allows the decision-support system user to understand the logic behind the model’s prediction. 5. Company Continues Diversification with Renewables Market Software Firm Partnership Class: Positive
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