Transcription of Understanding Black-box Predictions via Influence Functions
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Understanding Black-box Predictions via Influence Functions
Understanding Black-box Predictions via Influence Functions Pang Wei Koh 1 Percy Liang 1. Abstract point (Ribeiro et al., 2016) or by perturbing the test point to How can we explain the Predictions of a black - see how the prediction changes (Simonyan et al., 2013; Li box model? In this paper, we use Influence func- et al., 2016b; Datta et al., 2016; Adler et al., 2016). These tions a classic technique from robust statis- works explain the Predictions in terms of the model, but how can we explain where the model came from? [ ] 10 Jul 2017. tics to trace a model's prediction through the learning algorithm and back to its training data, In this paper, we tackle this question by tracing a model's thereby identifying training points most respon- Predictions through its learning algorithm and back to the sible for a given prediction .
Understanding Black-box Predictions via Influence Functions Figure 1. Components of influence. (a) What is the effect of the training loss and H 1 terms in I
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