Search Results for author:
Found 7 papers, 2 papers with code
Streamlining models with explanations in the learning loop
Several explainable AI methods allow a Machine Learning user to get insights on the classification process of a black-box model in the form of local linear explanations.
Learning Multiscale Non-stationary Causal Structures
Our model allows sampling an MN-DAG according to user-specified priors on the time-dependence and multiscale properties of the causal graph.
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs
Decoupling the feature space (edges) from a desired high-level explanation language (such as motifs) is thus a major challenge towards developing actionable explanations for graph classification tasks.
The Evolving Causal Structure of Equity Risk Factors
They link the evolution of the causal structure of equity risk factors with market volatility and a worsening macroeconomic environment, and show that, in times of financial crisis, exposure to different factors boils down to exposure to the market risk factor.
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods
This highlights the need to have standard and unbiased evaluation procedures for Local Linear Explanations in the XAI field.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Patterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions
We aimed to find a large cohort of Reddit users interested in discussing the use of opioids, trace the temporal evolution of their interest, and extensively characterize patterns of the nonmedical consumption of opioids, with a focus on routes of administration and drug tampering.
Information Retrieval
Computers and Society
FairLens: Auditing Black-box Clinical Decision Support Systems
The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare.
