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Found 11 papers, 1 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.
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
Towards human-agent knowledge fusion (HAKF) in support of distributed coalition teams
Future coalition operations can be substantially augmented through agile teaming between human and machine agents, but in a coalition context these agents may be unfamiliar to the human users and expected to operate in a broad set of scenarios rather than being narrowly defined for particular purposes.
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.
Learning Features of Network Structures Using Graphlets
In this paper, we explore the role of \emph{graphlets} in network classification for both static and temporal networks.
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.
Network Classification in Temporal Networks Using Motifs
Network classification has a variety of applications, such as detecting communities within networks and finding similarities between those representing different aspects of the real world.
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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