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Explainable AI and model interpretability are hyper-active and hyper-hot areas of current research (think of holy grail, or something), which have been brought forward lately not least due to the (often tremendous) success of deep learning models in various tasks, plus the necessity of algorithmic fairness & accountability. Here are some state of the ...


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The paper Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges argues that ensuring fairness is not a trivial task and that the current statistical formalizations of fairness lead to a long list of criteria that are each flawed (or even harmful) in different contexts, that is, there are trade-offs between the proposed ...


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The $\sim$ symbol means that a random variable is drawn from the given distribution, i.e. if I were to say $X$ has a Standard Normal distribution I would write $X \sim \text{Normal}(0,1)$. They write two explicit expectations here because $a$ is a random variable with distribution $\mu_x$ but $X$ is also a random variable with distribution $V$. I believe you ...


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In a simple linear model of the form $y = \beta_0 + \beta_1 x $ we can see that increasing $x$ by a unit will increase the prediction on $y$ by $\beta_1$. Here we can completely determine what the effect on the models prediction will be by increasing $x$. With more complex models such as neural networks it is much more difficult to tell due to all the ...


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There are many frameworks which allow you to do that. One of them, which supports many different techniques for visualization, can be found here: https://github.com/marcoancona/DeepExplain


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Yes there definitely is, and research into this has actually resulted in some really cool behaviour. One of the simplest ways is to simply back propagate the gradient all the way back to the input. Areas of the input that affected the final decision will receive larger gradients. Interestingly, this also sort of works as a rudimentary form of semantic ...


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