One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency

27 Jan 2020Kacper SokolPeter Flach

The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the inner workings of these algorithms should be scrutinised and their decisions explained to the relevant stakeholders, including the system engineers, the system's operators and the individuals whose case is being decided... (read more)

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