A Categorisation of Post-hoc Explanations for Predictive Models
|Title:||A Categorisation of Post-hoc Explanations for Predictive Models||Authors:||Mitros, John (Ioannis); MacNamee, Brian||Permanent link:||http://hdl.handle.net/10197/12202||Date:||27-Mar-2019||Online since:||2021-05-26T10:33:57Z||Abstract:||The ubiquity of machine learning based predictive models inmodern society naturally leads people to ask how trustworthythose models are? In predictive modeling, it is quite commonto induce a trade-off between accuracy and interpretability.For instance, doctors would like to know how effective sometreatment will be for a patient or why the model suggesteda particular medication for a patient exhibiting those symptoms? We acknowledge that the necessity for interpretabilityis a consequence of an incomplete formalisation of the prob-lem, or more precisely of multiple meanings adhered to a par-ticular concept. For certain problems, it is not enough to getthe answer (what), the model also has to provide an expla-nation of how it came to that conclusion (why), because acorrect prediction, only partially solves the original problem.In this article we extend existing categorisation of techniquesto aid model interpretability and test this categorisation||Funding Details:||Science Foundation Ireland||Funding Details:||Insight Research Centre||Type of material:||Conference Publication||Publisher:||Association for the Advancement of Artificial Intelligence||Copyright (published version):||2018 Association for the Advancement of Artificial Intelligence||Keywords:||Machine learning & statistics; Interpretability||Other versions:||https://aaai.org/Symposia/Spring/sss19.php||Language:||en||Status of Item:||Peer reviewed||Conference Details:||The 2019 Association for the Advancement of Artificial Intelligence (AAAI) Spring Symposia on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019), Palo Alto, California, 25-27 March 2019||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
Insight Research Collection
Show full item record
If you are a publisher or author and have copyright concerns for any item, please email firstname.lastname@example.org and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.