The system then learns by processing all this data, and each layer of the Indonesia Email Database deep neural network learns to recognize progressively more complex features. With sufficient training, the AI may become highly accurate. But its decision processes are not always transparent open up. The AI black box and facilitate trust companies must develop AI systems. That perform reliably that is make correct decisions time after time. The machine-learning models on which the systems are based must also be transparent, explainable, and able to achieve repeatable results. We call this combination of features an AI model’s interpretability. It is important to note that there can be a trade-off between performance and interpretability.
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For example a simpler model may be easier to understand. But it won’t be able to process complex data or relationships. Getting this trade-off right is primarily the domain of developers and analysts. But business leaders should have a basic understanding. What determines whether a model is interpretable as this is a key factor in determining an AI system’s legitimacy. In the eyes of the business’s employees and customers. Data integrity and the possibility of unintentional biases are also a concern when integrating AI. In a 2017 PwC CEO Pulse survey, 76 percent of gulf email list respondents said potential for biases and lack of transparency were impeding AI adoption in their enterprise. Seventy-three percent said the same about the need to ensure governance and rules to control AI.
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Consider the example of the AI-powered mortgage loan application evaluation system. What if it started denying applications from a certain demographic because of human or systemic biases in the data? Or imagine if an airport security system’s AI program singled out certain individuals for additional screening at airport security on the basis of their race or ethnicity. Business leaders faced with ensuring interpretability, consistent performance, and data integrity will have to work closely with their organization’s developers and analysts. Developers are responsible for building the machine learning model, selecting the algorithms used for the AI application, and verifying that the AI was built correctly and continues to perform as expected. Analysts are responsible for validating the AI model created by the developers to be sure the model addresses the business need at hand.