• Skip to main content
  • Skip to primary sidebar
AAAI

AAAI

Association for the Advancement of Artificial Intelligence

    • AAAI

      AAAI

      Association for the Advancement of Artificial Intelligence

  • About AAAIAbout AAAI
    • AAAI Officers and Committees
    • AAAI Staff
    • Bylaws of AAAI
    • AAAI Awards
      • Fellows Program
      • Classic Paper Award
      • Dissertation Award
      • Distinguished Service Award
      • Allen Newell Award
      • Outstanding Paper Award
      • Award for Artificial Intelligence for the Benefit of Humanity
      • Feigenbaum Prize
      • Patrick Henry Winston Outstanding Educator Award
      • Engelmore Award
      • AAAI ISEF Awards
      • Senior Member Status
      • Conference Awards
    • AAAI Resources
    • AAAI Mailing Lists
    • Past AAAI Presidential Addresses
    • Presidential Panel on Long-Term AI Futures
    • Past AAAI Policy Reports
      • A Report to ARPA on Twenty-First Century Intelligent Systems
      • The Role of Intelligent Systems in the National Information Infrastructure
    • AAAI Logos
    • News
  • aaai-icon_ethics-diversity-line-yellowEthics & Diversity
  • Conference talk bubbleConferences & Symposia
    • AAAI Conference
    • AIES AAAI/ACM
    • AIIDE
    • IAAI
    • ICWSM
    • HCOMP
    • Spring Symposia
    • Summer Symposia
    • Fall Symposia
    • Code of Conduct for Conferences and Events
  • PublicationsPublications
    • AAAI Press
    • AI Magazine
    • Conference Proceedings
    • AAAI Publication Policies & Guidelines
    • Request to Reproduce Copyrighted Materials
  • aaai-icon_ai-magazine-line-yellowAI Magazine
    • Issues and Articles
    • Author Guidelines
    • Editorial Focus
  • MembershipMembership
    • Member Login
    • Developing Country List
    • AAAI Chapter Program

  • Career CenterCareer Center
  • aaai-icon_ai-topics-line-yellowAITopics
  • aaai-icon_contact-line-yellowContact

Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 8: AAAI-22 Technical Tracks 8

Constraint-Driven Explanations for Black-Box ML Models

February 1, 2023

Download PDF

Authors

Aditya A. Shrotri

Rice University


Nina Narodytska

VMware Research Inc.


Alexey Ignatiev

Monash University


Kuldeep S Meel

National University of Singapore


Joao Marques-Silva

IRIT, CNRS,Toulouse, France


Moshe Y. Vardi

Rice University


DOI:

10.1609/aaai.v36i8.20805


Abstract:

The need to understand the inner workings of opaque Machine Learning models has prompted researchers to devise various types of post-hoc explanations. A large class of such explainers proceed in two phases: first perturb an input instance whose explanation is sought, and then generate an interpretable artifact to explain the prediction of the opaque model on that instance. Recently, Deutch and Frost proposed to use an additional input from the user: a set of constraints over the input space to guide the perturbation phase. While this approach affords the user the ability to tailor the explanation to their needs, striking a balance between flexibility, theoretical rigor and computational cost has remained an open challenge. We propose a novel constraint-driven explanation generation approach which simultaneously addresses these issues in a modular fashion. Our framework supports the use of expressive Boolean constraints giving the user more flexibility to specify the subspace to generate perturbations from. Leveraging advances in Formal Methods, we can theoretically guarantee strict adherence of the samples to the desired distribution. This also allows us to compute fidelity in a rigorous way, while scaling much better in practice. Our empirical study demonstrates concrete uses of our tool CLIME in obtaining more meaningful explanations with high fidelity.

Topics: AAAI

Primary Sidebar

HOW TO CITE:

Aditya A. Shrotri||Nina Narodytska||Alexey Ignatiev||Kuldeep S Meel||Joao Marques-Silva||Moshe Y. Vardi Constraint-Driven Explanations for Black-Box ML Models Proceedings of the AAAI Conference on Artificial Intelligence (2022) 8304-8314.

Aditya A. Shrotri||Nina Narodytska||Alexey Ignatiev||Kuldeep S Meel||Joao Marques-Silva||Moshe Y. Vardi Constraint-Driven Explanations for Black-Box ML Models AAAI 2022, 8304-8314.

Aditya A. Shrotri||Nina Narodytska||Alexey Ignatiev||Kuldeep S Meel||Joao Marques-Silva||Moshe Y. Vardi (2022). Constraint-Driven Explanations for Black-Box ML Models. Proceedings of the AAAI Conference on Artificial Intelligence, 8304-8314.

Aditya A. Shrotri||Nina Narodytska||Alexey Ignatiev||Kuldeep S Meel||Joao Marques-Silva||Moshe Y. Vardi. Constraint-Driven Explanations for Black-Box ML Models. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.8304-8314.

Aditya A. Shrotri||Nina Narodytska||Alexey Ignatiev||Kuldeep S Meel||Joao Marques-Silva||Moshe Y. Vardi. 2022. Constraint-Driven Explanations for Black-Box ML Models. "Proceedings of the AAAI Conference on Artificial Intelligence". 8304-8314.

Aditya A. Shrotri||Nina Narodytska||Alexey Ignatiev||Kuldeep S Meel||Joao Marques-Silva||Moshe Y. Vardi. (2022) "Constraint-Driven Explanations for Black-Box ML Models", Proceedings of the AAAI Conference on Artificial Intelligence, p.8304-8314

Aditya A. Shrotri||Nina Narodytska||Alexey Ignatiev||Kuldeep S Meel||Joao Marques-Silva||Moshe Y. Vardi, "Constraint-Driven Explanations for Black-Box ML Models", AAAI, p.8304-8314, 2022.

Aditya A. Shrotri||Nina Narodytska||Alexey Ignatiev||Kuldeep S Meel||Joao Marques-Silva||Moshe Y. Vardi. "Constraint-Driven Explanations for Black-Box ML Models". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.8304-8314.

Aditya A. Shrotri||Nina Narodytska||Alexey Ignatiev||Kuldeep S Meel||Joao Marques-Silva||Moshe Y. Vardi. "Constraint-Driven Explanations for Black-Box ML Models". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 8304-8314.

Aditya A. Shrotri||Nina Narodytska||Alexey Ignatiev||Kuldeep S Meel||Joao Marques-Silva||Moshe Y. Vardi. Constraint-Driven Explanations for Black-Box ML Models. AAAI[Internet]. 2022[cited 2023]; 8304-8314.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept All”, you consent to the use of ALL the cookies. However, you may visit "Cookie Settings" to provide a controlled consent.
Cookie SettingsAccept All
Manage consent

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
CookieDurationDescription
cookielawinfo-checkbox-analytics11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional11 monthsThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy11 monthsThe cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytics
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Others
Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.
SAVE & ACCEPT