A well trained Deep Learning Neural Network (DLNN - which is one form of AI) produce fairly accurate and consistent decisions. However, it is not possible to extract the rationale or the causation for any specific decision/prediction. That is what we call a black box. The decision making algorithm is developed out of the training examples and contained within the DLNN as billions of parameters, which cannot retrospectively be expressed as a logical sequence. DL decisions result from an integration of all examples/cases used in the training of DLNN. Each example results in the tuning of the parameters (weights and biases) of the neurons in the DL network. This is similar to the human intuition - a result of integration of all the perceptions we have experienced in a given field. Our intuition is also a black box - and not rational, but experiential. When an expert gives us an explanation for his decision (transparency), he is essentially trying to co-relate a rational explanation with his intuitional decision based on the integration of all his experience - trying to make it transparent. The rational interpretation is generally quite approximate - touching just a few of the data points involved in the intuitional decision process. Our trust in systems results not just because of transparency, but also because of system’s consistency in delivering good decisions. Hence, over time, DL AI may win our trust, even without transparency.