Search Results for author:
Found 17 papers, 2 papers with code
Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification
Data management research is showing an increasing presence and interest in topics related to data and algorithmic fairness, including the topic of fair classification.
A Framework for Inferring Causality from Multi-Relational Observational Data using Conditional Independence
The study of causality or causal inference - how much a given treatment causally affects a given outcome in a population - goes way beyond correlation or association analysis of variables, and is critical in making sound data driven decisions and policies in a multitude of applications.
Causes for Query Answers from Databases: Datalog Abduction, View-Updates, and Integrity Constraints
In this work we establish precise connections between QA-causality and both abductive diagnosis and the view-update problem in databases, allowing us to obtain new algorithmic and complexity results for QA-causality.
From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back
In this work we establish and investigate connections between causes for query answers in databases, database repairs wrt.
ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data
In this paper we describe a suite of techniques for expressing causal inference tasks from observational data in SQL.
Causes for Query Answers from Databases, Datalog Abduction and View-Updates: The Presence of Integrity Constraints
In this work we further investigate connections between query-answer causality and abductive diagnosis and the view-update problem.
Query-Answer Causality in Databases: Abductive Diagnosis and View-Updates
Causality has been recently introduced in databases, to model, characterize and possibly compute causes for query results (answers).
Capuchin: Causal Database Repair for Algorithmic Fairness
However, it is the underlying data on which these systems are trained that often reflect discrimination, suggesting a database repair problem.
Data Management for Causal Algorithmic Fairness
Fairness is increasingly recognized as a critical component of machine learning systems.
Causal Relational Learning
Causal inference is at the heart of empirical research in natural and social sciences and is critical for scientific discovery and informed decision making.
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals
There has been a recent resurgence of interest in explainable artificial intelligence (XAI) that aims to reduce the opaqueness of AI-based decision-making systems, allowing humans to scrutinize and trust them.
Interpretable Data-Based Explanations for Fairness Debugging
We introduce Gopher, a system that produces compact, interpretable and causal explanations for bias or unexpected model behavior by identifying coherent subsets of the training data that are root-causes for this behavior.
BIG-bench Machine Learning
Explainable artificial intelligence
+2
Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces
Despite their high accuracies, modern complex image classifiers cannot be trusted for sensitive tasks due to their unknown decision-making process and potential biases.
Combining Counterfactuals With Shapley Values To Explain Image Models
With the widespread use of sophisticated machine learning models in sensitive applications, understanding their decision-making has become an essential task.
Consistent Range Approximation for Fair Predictive Modeling
This paper proposes a novel framework for certifying the fairness of predictive models trained on biased data.
OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport
Ensuring Conditional Independence (CI) constraints is pivotal for the development of fair and trustworthy machine learning models.
Graph Neural Network based Double Machine Learning Estimator of Network Causal Effects
Our paper addresses the challenge of inferring causal effects in social network data, characterized by complex interdependencies among individuals resulting in challenges such as non-independence of units, interference (where a unit's outcome is affected by neighbors' treatments), and introduction of additional confounding factors from neighboring units.
