Explaining machine learning predictions : rationales and effective modifications

Deep learning models have demonstrated unprecedented accuracy in wide-ranging tasks such as object and speech recognition. These models can outperform techniques traditionally used in credit risk modeling like logistic regression. However, deep learning models operate as black-boxes, which can limit their use and impact. Regulation mandates that a lender must be able to disclose up to four factors that adversely affected a rejected credit applicant. But we argue that knowing why an applicant is turned down is not enough. An applicant would also want actionable advice that can enable them to reach a favorable classification. Our research thus focuses on both the desire to explain why a machine learning model predicted the classification it did and to find small changes to an input point that can reverse its classification. In this thesis, we evaluate two variants of LIME, a local model-approximation technique and use them in a generate and test algorithm to produce mathematically-effective modifications. We demonstrate that such modifications may not be pragmatically-useful and show how numerical analyses can be supplemented with domain knowledge to generate explanations that are of pragmatic utility. Our work can help accelerate the adoption of deep learning in domains that would benefit from interpreting machine learning predictions.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Mishra, Sudhanshu Nath.

Advisor dc:contributor.advisor

• Randall Davis and Andrew W. Lo.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/121599

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/121599