Search Results for author:
Found 11 papers, 1 papers with code
QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
In this context, we introduce Quantified Uncertainty Counterfactual Explanations (QUCE), a method designed to mitigate out-of-distribution traversal by minimizing path uncertainty.
Embrace Opportunities and Face Challenges: Using ChatGPT in Undergraduate Students' Collaborative Interdisciplinary Learning
Weekly surveys were conducted on collaborative interdisciplinary problem-solving, physical and cognitive engagement, and individual reflections on ChatGPT use.
Stock Price Predictability and the Business Cycle via Machine Learning
We study the impacts of business cycles on machine learning (ML) predictions.
Probabilistic Deduction: an Approach to Probabilistic Structured Argumentation
As rules in classical structured argumentation frameworks, p-rules form deduction systems.
On Understanding the Influence of Controllable Factors with a Feature Attribution Algorithm: a Medical Case Study
Feature attribution XAI algorithms enable their users to gain insight into the underlying patterns of large datasets through their feature importance calculation.
Explainable Artificial Intelligence (XAI)
Feature Importance
Explainable Decision Making with Lean and Argumentative Explanations
It is widely acknowledged that transparency of automated decision making is crucial for deployability of intelligent systems, and explaining the reasons why some decisions are "good" and some are not is a way to achieving this transparency.
Towards a Shapley Value Graph Framework for Medical peer-influence
In other words, although contribution towards a certain prediction is highlighted by feature attribution methods, the relation between features and the consequence of intervention is not studied.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
An Investigation of the Impact of COVID-19 Non-Pharmaceutical Interventions and Economic Support Policies on Foreign Exchange Markets with Explainable AI Techniques
Since the onset of the the COVID-19 pandemic, many countries across the world have implemented various non-pharmaceutical interventions (NPIs) to contain the spread of virus, as well as economic support policies (ESPs) to save their economies.
Evaluating the Correctness of Explainable AI Algorithms for Classification
In this work, we develop a method to quantitatively evaluate the correctness of XAI algorithms by creating datasets with known explanation ground truth.
An Investigation of COVID-19 Spreading Factors with Explainable AI Techniques
We compute the top one and two measures that are most effective for the countries and regions studied during the period.
Explainable AI for Classification using Probabilistic Logic Inference
The overarching goal of Explainable AI is to develop systems that not only exhibit intelligent behaviours, but also are able to explain their rationale and reveal insights.
