20 citations · 74 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…