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Found 32 papers, 4 papers with code
Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?
We hypothesise that this occurs when concept annotations are inaccurate or how input features should relate to concepts is unclear.
Towards a Deeper Understanding of Concept Bottleneck Models Through End-to-End Explanation
Concept Bottleneck Models (CBMs) first map raw input(s) to a vector of human-defined concepts, before using this vector to predict a final classification.
Negativity Spreads Faster: A Large-Scale Multilingual Twitter Analysis on the Role of Sentiment in Political Communication
Social media has become extremely influential when it comes to policy making in modern societies, especially in the western world, where platforms such as Twitter allow users to follow politicians, thus making citizens more involved in political discussion.
AAAI FSS-21: Artificial Intelligence in Government and Public Sector Proceedings
Proceedings of the AAAI Fall Symposium on Artificial Intelligence in Government and Public Sector, Washington, DC, USA, November 4-6, 2021
Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification
Data augmentation techniques are widely used for enhancing the performance of machine learning models by tackling class imbalance issues and data sparsity.
Using DeepProbLog to perform Complex Event Processing on an Audio Stream
We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data.
Deriving Disinformation Insights from Geolocalized Twitter Callouts
In particular, the analysis in centered on Twitter and disinformation for three European languages: English, French and Spanish.
COVID-19 and Misinformation: A Large-Scale Lexical Analysis on Twitter
Social media is often used by individuals and organisations as a platform to spread misinformation.
Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials
Adopting shared data resources requires scientists to place trust in the originators of the data.
A framework for fostering transparency in shared artificial intelligence models by increasing visibility of contributions
Increased adoption of artificial intelligence (AI) systems into scientific workflows will result in an increasing technical debt as the distance between the data scientists and engineers who develop AI system components and scientists, researchers and other users grows.
Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification
Pre-trained language models provide the foundations for state-of-the-art performance across a wide range of natural language processing tasks, including text classification.
AAAI FSS-20: Artificial Intelligence in Government and Public Sector Proceedings
Proceedings of the AAAI Fall Symposium on Artificial Intelligence in Government and Public Sector, Washington, DC, USA, November 13-14, 2020
An Experimentation Platform for Explainable Coalition Situational Understanding
We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML) and integration of symbolic and subsymbolic AI/ML approaches for event processing.
BIG-bench Machine Learning
Explainable artificial intelligence
Predicting Themes within Complex Unstructured Texts: A Case Study on Safeguarding Reports
The task of text and sentence classification is associated with the need for large amounts of labelled training data.
A Hybrid Neuro-Symbolic Approach for Complex Event Processing
We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K.
Explaining Motion Relevance for Activity Recognition in Video Deep Learning Models
Our results show that the selective relevance method can not only provide insight on the role played by motion in the model's decision -- in effect, revealing and quantifying the model's spatial bias -- but the method also simplifies the resulting explanations for human consumption.
Increasing negotiation performance at the edge of the network
In particular, we show our method significantly reduces the number of messages when an agreement is not possible.
The current state of automated negotiation theory: a literature review
Automated negotiation can be an efficient method for resolving conflict and redistributing resources in a coalition setting.
Sanity Checks for Saliency Metrics
Despite a proliferation of such methods, little effort has been made to quantify how good these saliency maps are at capturing the true relevance of the pixels to the classifier output (i. e. their "fidelity").
AAAI FSS-19: Artificial Intelligence in Government and Public Sector Proceedings
Proceedings of the AAAI Fall Symposium on Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA, November 7-8, 2019
Explainable AI for Intelligence Augmentation in Multi-Domain Operations
Central to the concept of multi-domain operations (MDO) is the utilization of an intelligence, surveillance, and reconnaissance (ISR) network consisting of overlapping systems of remote and autonomous sensors, and human intelligence, distributed among multiple partners.
BMVC 2019: Workshop on Interpretable and Explainable Machine Vision
Proceedings of the BMVC 2019 Workshop on Interpretable and Explainable Machine Vision, Cardiff, UK, September 12, 2019.
Explainable Deep Learning for Video Recognition Tasks: A Framework & Recommendations
This paper seeks to highlight the need for explainability methods designed with video deep learning models, and by association spatio-temporal input in mind, by first illustrating the cutting edge for video deep learning, and then noting the scarcity of research into explanations for these methods.
Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition
However, by exploiting a simple technique that removes motion information, we show that it is not the case that this technique is effective as-is for representing relevance in non-image tasks.
Quantifying Transparency of Machine Learning Systems through Analysis of Contributions
Increased adoption and deployment of machine learning (ML) models into business, healthcare and other organisational processes, will result in a growing disconnect between the engineers and researchers who developed the models and the model's users and other stakeholders, such as regulators or auditors.
Deep Q-Learning for Directed Acyclic Graph Generation
We present a method to generate directed acyclic graphs (DAGs) using deep reinforcement learning, specifically deep Q-learning.
AAAI FSS-18: Artificial Intelligence in Government and Public Sector Proceedings
Proceedings of the AAAI Fall Symposium on Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA, October 18-20, 2018
Stakeholders in Explainable AI
There is general consensus that it is important for artificial intelligence (AI) and machine learning systems to be explainable and/or interpretable.
Hows and Whys of Artificial Intelligence for Public Sector Decisions: Explanation and Evaluation
Evaluation has always been a key challenge in the development of artificial intelligence (AI) based software, due to the technical complexity of the software artifact and, often, its embedding in complex sociotechnical processes.
Defining the Collective Intelligence Supply Chain
Organisations are increasingly open to scrutiny, and need to be able to prove that they operate in a fair and ethical way.
Uncertainty Aware AI ML: Why and How
This paper argues the need for research to realize uncertainty-aware artificial intelligence and machine learning (AI\&ML) systems for decision support by describing a number of motivating scenarios.
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable.
BIG-bench Machine Learning
Interpretable Machine Learning
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