Effective Explanations for Entity Resolution Models
- URL: http://arxiv.org/abs/2203.12978v1
- Date: Thu, 24 Mar 2022 10:50:05 GMT
- Title: Effective Explanations for Entity Resolution Models
- Authors: Tommaso Teofili, Donatella Firmani, Nick Koudas, Vincenzo Martello,
Paolo Merialdo, Divesh Srivastava
- Abstract summary: We study the fundamental problem of explainability of the deep learning solution for ER.
We propose the CERTA approach that is aware of the semantics of the ER problem.
We experimentally evaluate CERTA's explanations of state-of-the-art ER solutions based on DL models using publicly available datasets.
- Score: 21.518135952436975
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Entity resolution (ER) aims at matching records that refer to the same
real-world entity. Although widely studied for the last 50 years, ER still
represents a challenging data management problem, and several recent works have
started to investigate the opportunity of applying deep learning (DL)
techniques to solve this problem. In this paper, we study the fundamental
problem of explainability of the DL solution for ER. Understanding the matching
predictions of an ER solution is indeed crucial to assess the trustworthiness
of the DL model and to discover its biases. We treat the DL model as a black
box classifier and - while previous approaches to provide explanations for DL
predictions are agnostic to the classification task. we propose the CERTA
approach that is aware of the semantics of the ER problem. Our approach
produces both saliency explanations, which associate each attribute with a
saliency score, and counterfactual explanations, which provide examples of
values that can flip the prediction. CERTA builds on a probabilistic framework
that aims at computing the explanations evaluating the outcomes produced by
using perturbed copies of the input records. We experimentally evaluate CERTA's
explanations of state-of-the-art ER solutions based on DL models using publicly
available datasets, and demonstrate the effectiveness of CERTA over recently
proposed methods for this problem.
Related papers
- DISCO: DISCovering Overfittings as Causal Rules for Text Classification Models [6.369258625916601]
Post-hoc interpretability methods fail to capture the models' decision-making process fully.
Our paper introduces DISCO, a novel method for discovering global, rule-based explanations.
DISCO supports interactive explanations, enabling human inspectors to distinguish spurious causes in the rule-based output.
arXiv Detail & Related papers (2024-11-07T12:12:44Z) - Explaining Explainability: Towards Deeper Actionable Insights into Deep
Learning through Second-order Explainability [70.60433013657693]
Second-order explainable AI (SOXAI) was recently proposed to extend explainable AI (XAI) from the instance level to the dataset level.
We demonstrate for the first time, via example classification and segmentation cases, that eliminating irrelevant concepts from the training set based on actionable insights from SOXAI can enhance a model's performance.
arXiv Detail & Related papers (2023-06-14T23:24:01Z) - Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
We propose a framework for efficient and effective counterfactual inference implemented with neural networks.
The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data.
Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
arXiv Detail & Related papers (2023-06-09T08:30:51Z) - Counterfactuals of Counterfactuals: a back-translation-inspired approach
to analyse counterfactual editors [3.4253416336476246]
We focus on the analysis of counterfactual, contrastive explanations.
We propose a new back translation-inspired evaluation methodology.
We show that by iteratively feeding the counterfactual to the explainer we can obtain valuable insights into the behaviour of both the predictor and the explainer models.
arXiv Detail & Related papers (2023-05-26T16:04:28Z) - Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries [10.818661865303518]
We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting.
We introduce diffusion-based causal models (DCM) to learn causal mechanisms, that generate unique latent encodings.
Our empirical evaluations demonstrate significant improvements over existing state-of-the-art methods for answering causal queries.
arXiv Detail & Related papers (2023-02-02T04:08:08Z) - MACE: An Efficient Model-Agnostic Framework for Counterfactual
Explanation [132.77005365032468]
We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE)
In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity.
Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
arXiv Detail & Related papers (2022-05-31T04:57:06Z) - Interpretable Data-Based Explanations for Fairness Debugging [7.266116143672294]
Gopher is a system that produces compact, interpretable, and causal explanations for bias or unexpected model behavior.
We introduce the concept of causal responsibility that quantifies the extent to which intervening on training data by removing or updating subsets of it can resolve the bias.
Building on this concept, we develop an efficient approach for generating the top-k patterns that explain model bias.
arXiv Detail & Related papers (2021-12-17T20:10:00Z) - Counterfactual Maximum Likelihood Estimation for Training Deep Networks [83.44219640437657]
Deep learning models are prone to learning spurious correlations that should not be learned as predictive clues.
We propose a causality-based training framework to reduce the spurious correlations caused by observable confounders.
We conduct experiments on two real-world tasks: Natural Language Inference (NLI) and Image Captioning.
arXiv Detail & Related papers (2021-06-07T17:47:16Z) - Beyond Trivial Counterfactual Explanations with Diverse Valuable
Explanations [64.85696493596821]
In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction.
We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss.
Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
arXiv Detail & Related papers (2021-03-18T12:57:34Z) - A new interpretable unsupervised anomaly detection method based on
residual explanation [47.187609203210705]
We present RXP, a new interpretability method to deal with the limitations for AE-based AD in large-scale systems.
It stands out for its implementation simplicity, low computational cost and deterministic behavior.
In an experiment using data from a real heavy-haul railway line, the proposed method achieved superior performance compared to SHAP.
arXiv Detail & Related papers (2021-03-14T15:35:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.