A New Class of Explanations for Classifiers with Non-Binary Features
- URL: http://arxiv.org/abs/2304.14760v2
- Date: Sat, 22 Jul 2023 08:30:47 GMT
- Title: A New Class of Explanations for Classifiers with Non-Binary Features
- Authors: Chunxi Ji and Adnan Darwiche
- Abstract summary: Two types of explanations have been receiving increased attention in the literature when analyzing the decisions made by classifiers.
We show that these explanations can be significantly improved in the presence of non-binary features.
Necessary and sufficient reasons were also shown to be the prime implicates and implicants of the complete reason for a decision.
- Score: 11.358487655918676
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Two types of explanations have been receiving increased attention in the
literature when analyzing the decisions made by classifiers. The first type
explains why a decision was made and is known as a sufficient reason for the
decision, also an abductive explanation or a PI-explanation. The second type
explains why some other decision was not made and is known as a necessary
reason for the decision, also a contrastive or counterfactual explanation.
These explanations were defined for classifiers with binary, discrete and, in
some cases, continuous features. We show that these explanations can be
significantly improved in the presence of non-binary features, leading to a new
class of explanations that relay more information about decisions and the
underlying classifiers. Necessary and sufficient reasons were also shown to be
the prime implicates and implicants of the complete reason for a decision,
which can be obtained using a quantification operator. We show that our
improved notions of necessary and sufficient reasons are also prime implicates
and implicants but for an improved notion of complete reason obtained by a new
quantification operator that we also define and study.
Related papers
- Delivering Inflated Explanations [17.646704122091087]
A formal approach to explainability builds a formal model of the AI system.
A formal abductive explanation is a set of features, such that if they take the given value will always lead to the same decision.
In this paper we define inflated explanations which is a set of features, and for each feature of set of values, such that the decision will remain unchanged.
arXiv Detail & Related papers (2023-06-27T07:54:18Z) - Explanation Selection Using Unlabeled Data for Chain-of-Thought
Prompting [80.9896041501715]
Explanations that have not been "tuned" for a task, such as off-the-shelf explanations written by nonexperts, may lead to mediocre performance.
This paper tackles the problem of how to optimize explanation-infused prompts in a blackbox fashion.
arXiv Detail & Related papers (2023-02-09T18:02:34Z) - Complementary Explanations for Effective In-Context Learning [77.83124315634386]
Large language models (LLMs) have exhibited remarkable capabilities in learning from explanations in prompts.
This work aims to better understand the mechanisms by which explanations are used for in-context learning.
arXiv Detail & Related papers (2022-11-25T04:40:47Z) - On the Computation of Necessary and Sufficient Explanations [11.358487655918676]
The complete reason behind a decision is a Boolean formula that characterizes why the decision was made.
In this paper, we refer to the prime implicants of a complete reason as necessary reasons for the decision.
We provide an algorithm which can enumerate their shortest necessary reasons in output time.
arXiv Detail & Related papers (2022-03-20T04:39:41Z) - Detection Accuracy for Evaluating Compositional Explanations of Units [5.220940151628734]
Two examples of methods that use this approach are Network Dissection and Compositional explanations.
While intuitively, logical forms are more informative than atomic concepts, it is not clear how to quantify this improvement.
We propose to use as evaluation metric the Detection Accuracy, which measures units' consistency of detection of their assigned explanations.
arXiv Detail & Related papers (2021-09-16T08:47:34Z) - Contrastive Explanations for Model Interpretability [77.92370750072831]
We propose a methodology to produce contrastive explanations for classification models.
Our method is based on projecting model representation to a latent space.
Our findings shed light on the ability of label-contrastive explanations to provide a more accurate and finer-grained interpretability of a model's decision.
arXiv Detail & Related papers (2021-03-02T00:36:45Z) - The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal
Sufficient Subsets [61.66584140190247]
We show that feature-based explanations pose problems even for explaining trivial models.
We show that two popular classes of explainers, Shapley explainers and minimal sufficient subsets explainers, target fundamentally different types of ground-truth explanations.
arXiv Detail & Related papers (2020-09-23T09:45:23Z) - Evaluations and Methods for Explanation through Robustness Analysis [117.7235152610957]
We establish a novel set of evaluation criteria for such feature based explanations by analysis.
We obtain new explanations that are loosely necessary and sufficient for a prediction.
We extend the explanation to extract the set of features that would move the current prediction to a target class.
arXiv Detail & Related papers (2020-05-31T05:52:05Z) - SCOUT: Self-aware Discriminant Counterfactual Explanations [78.79534272979305]
The problem of counterfactual visual explanations is considered.
A new family of discriminant explanations is introduced.
The resulting counterfactual explanations are optimization free and thus much faster than previous methods.
arXiv Detail & Related papers (2020-04-16T17:05:49Z) - Explaining Data-Driven Decisions made by AI Systems: The Counterfactual
Approach [11.871523410051527]
We consider an explanation as a set of the system's data inputs that causally drives the decision.
We show that features that have a large importance weight for a model prediction may not affect the corresponding decision.
arXiv Detail & Related papers (2020-01-21T09:58:58Z)
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.