my heart :-) Fairly short and accessible Gives a good overview of current research and uses Attempts to clarify and classify types of explainability Sets the groundwork for further development on the topic
of a machine in contexts that have financial, safety, security, or personal ramifications to an individual, would you blindly trust its decision? How can we hold accountable Artificial Intelligence (AI) systems that make decisions on possibly unethical grounds, e.g. when they predict a person’s weight and health by their social media images or the world region they are from as part of a downstream determination about their future, like when they will quit their job, commit a crime, or could be radicalized into terrorism.”
Comprehensible: Give high-level indicators on symptoms and test results Interpretable: Step-by-step walkthrough of reasoning and decisions Your target audience determines the level of explainability you need!
to the user to apply their own knowledge, bias, and understanding. This can be dangerous as based on the above factors, different people might come up with different explanations for the same decision. They lack a line of reasoning that explains the decision-making process of a model using human-understandable features of the input data.