CITATION.cff

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title: >-
  CERTIFAI: A Common Framework to Provide
  Explanations and Analyse the Fairness and
  Robustness of Black-box Models
message: >-
  A python implementation of CERTIFAI framework for
  machine learning models' explainability.
type: software
authors:
  - given-names: Shubham
    family-names: Sharma
    email: shubham_sharma@utexas.edu
    affiliation: >-
      Department of Electrical and Computer
      Engineering, University of Texas at Austin
  - given-names: Jette
    family-names: Henderson
    email: jhenderson@cognitivescale.com
    affiliation: CognitiveScale
  - given-names: Joydeep
    family-names: Ghosh
    email: jghosh@utexas.edu
    affiliation: CognitiveScale
identifiers:
  - type: doi
    value: 10.1145/3375627.3375812
repository-code: 'https://github.com/Ighina/CERTIFAI'
abstract: >-
  Concerns within the machine learning community and
  external pressures from regulators over the
  vulnerabilities of machine learning algorithms have
  spurred on the fields of explainability,
  robustness, and fairness. Often, issues in
  explainability, robustness, and fairness are
  confined to their specific sub-fields and few tools
  exist for model developers to use to simultaneously
  build their modeling pipelines in a transparent,
  accountable, and fair way. This can lead to a
  bottleneck on the model developer’s side as they
  must juggle multiple methods to evaluate their
  algorithms. In this paper, we present a single
  framework for analyzing the robustness, fairness,
  and explainability of a classifier. The framework,
  which is based on the generation of counterfactual
  explanations1 through a custom genetic algorithm,
  is flexible, model-agnostic, and does not require
  access to model internals. The framework allows the
  user to calculate robustness and fairness scores
  for individual models and generate explanations for
  individual predictions which provide a means for
  actionable recourse (changes to an input to help
  get a desired outcome). This is the first time that
  a unified tool has been developed to address three
  key issues pertaining
keywords:
  - Responsible Artificial Intelligence
  - explainability
  - fairness
  - robustness
  - machine learning
date-released: '2020-12-29'