Understand
Predictive models are omnipresent in automated and assisted decision making scenarios.
- But for the most part they are used as black boxes which output a prediction without understanding partially or even completely how different features influence the model prediction avoiding algorithmic transparency.
- Rankings are ordering over items encoding implicit comparisons typically learned using a family of features using learning-to-rank models.
- In this paper we focus on how best we can understand the decisions made by a ranker in a post-hoc model agnostic manner.
Built on
Adversarial learning
Lowd, D., and Meek, C · 2005
Earlier work this paper cites.
Ranklib, 2013
Dang, V · 2013
Earlier work this paper cites.
Introducing LETOR 4.0 datasets
Qin, T., and Liu, T · 2013
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Adler, P., Falk, C., Friedler, S. A., Rybeck, G., Scheidegger, C., Smith, B., and Venkatasubramanian, S · 2016
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Then
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Ye, J., Chow, J.-H., Chen, J., and Zheng, Z · 2064
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