20 citations · 74 across the 13 of their papers we have counts for
13 papers
Realistic Counterfactual Explanations via Denial Constraints
Avia Asael, Nave Frost, Amir Gilad +1
In the realm of Explainable AI, classification results are often explained via counterfactuals (CFs for short), which are (ideally small) perturbations to an instance that lead to…
Position: Explainability Research Must Prioritize Foundations over Ad-hoc Methods
Michal Moshkovitz, Suraj Srinivas, Lesia Semenova +7
Despite the proliferation of Explainable AI (XAI) techniques -- from feature attributions to sparse autoencoders -- explanations rarely influence real-world workflows. In practice,…
Advancing Fact Attribution for Query Answering: Aggregate Queries and Novel Algorithms
Omer Abramovich, Daniel Deutch, Nave Frost +2
In this paper, we introduce a novel approach to computing the contribution of input tuples to the result of the query, quantified by the Banzhaf and Shapley values. In contrast to…
CaFA: Cost-aware, Feasible Attacks With Database Constraints Against Neural Tabular Classifiers
Matan Ben-Tov, Daniel Deutch, Nave Frost +1
This work presents CaFA, a system for Cost-aware Feasible Attacks for assessing the robustness of neural tabular classifiers against adversarial examples realizable in the problem…
Banzhaf Values for Facts in Query Answering
Omer Abramovich, Daniel Deutch, Nave Frost +2
Quantifying the contribution of database facts to query answers has been studied as means of explanation. The Banzhaf value, originally developed in Game Theory, is a natural measu…
Framework for Evaluating Faithfulness of Local Explanations
Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz
We study the faithfulness of an explanation system to the underlying prediction model. We show that this can be captured by two properties, consistency and sufficiency, and introdu…
Computing the Shapley Value of Facts in Query Answering
Daniel Deutch, Nave Frost, Benny Kimelfeld +1
The Shapley value is a game-theoretic notion for wealth distribution that is nowadays extensively used to explain complex data-intensive computation, for instance, in network analy…
SmartTriage: A system for personalized patient data capture, documentation generation, and decision support
Ilya Valmianski, Nave Frost, Navdeep Sood +6
Symptom checkers have emerged as an important tool for collecting symptoms and diagnosing patients, minimizing the involvement of clinical personnel. We developed a machine-learnin…
Explaining Natural Language Query Results
Daniel Deutch, Nave Frost, Amir Gilad
Multiple lines of research have developed Natural Language (NL) interfaces for formulating database queries. We build upon this work, but focus on presenting a highly detailed form…
Just in Time: Personal Temporal Insights for Altering Model Decisions
Naama Boer, Daniel Deutch, Nave Frost +1
The interpretability of complex Machine Learning models is coming to be a critical social concern, as they are increasingly used in human-related decision-making processes such as…
T-REx: Table Repair Explanations
Daniel Deutch, Nave Frost, Amir Gilad +1
Data repair is a common and crucial step in many frameworks today, as applications may use data from different sources and of different levels of credibility. Thus, this step has b…
ExKMC: Expanding Explainable -Means Clustering
Nave Frost, Michal Moshkovitz, Cyrus Rashtchian
Despite the popularity of explainable AI, there is limited work on effective methods for unsupervised learning. We study algorithms for -means clustering, focusing on a trade-of…
Explainable -Means and -Medians Clustering
Sanjoy Dasgupta, Nave Frost, Michal Moshkovitz +1
Clustering is a popular form of unsupervised learning for geometric data. Unfortunately, many clustering algorithms lead to cluster assignments that are hard to explain, partially…