The rise and potential of large language model based agents: A survey
For a long time, humanity has pursued artificial intelligence (AI) equivalent to or surpassing
the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are …
the human level, with AI agents considered a promising vehicle for this pursuit. AI agents are …
Improving deep learning with prior knowledge and cognitive models: A survey on enhancing interpretability, adversarial robustness and zero-shot learning
F Mumuni, A Mumuni - Cognitive Systems Research, 2023 - Elsevier
We review current and emerging knowledge-informed and brain-inspired cognitive systems
for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or …
for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or …
Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability
Abstract Concept-based interpretability methods aim to explain a deep neural network
model's components and predictions using a pre-defined set of semantic concepts. These …
model's components and predictions using a pre-defined set of semantic concepts. These …
Explainable predictive maintenance: a survey of current methods, challenges and opportunities
Predictive maintenance is a well studied collection of techniques that aims to prolong the life
of a mechanical system by using artificial intelligence and machine learning to predict the …
of a mechanical system by using artificial intelligence and machine learning to predict the …
In search of verifiability: Explanations rarely enable complementary performance in ai-advised decision making
The current literature on AI-advised decision making--involving explainable AI systems
advising human decision makers--presents a series of inconclusive and confounding …
advising human decision makers--presents a series of inconclusive and confounding …
Humans, ai, and context: Understanding end-users' trust in a real-world computer vision application
Trust is an important factor in people's interactions with AI systems. However, there is a lack
of empirical studies examining how real end-users trust or distrust the AI system they interact …
of empirical studies examining how real end-users trust or distrust the AI system they interact …
How do data analysts respond to ai assistance? a wizard-of-oz study
Data analysis is challenging as analysts must navigate nuanced decisions that may yield
divergent conclusions. AI assistants have the potential to support analysts in planning their …
divergent conclusions. AI assistants have the potential to support analysts in planning their …
Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling
Audits are critical mechanisms for identifying the risks and limitations of deployed artificial
intelligence (AI) systems. However, the effective execution of AI audits remains incredibly …
intelligence (AI) systems. However, the effective execution of AI audits remains incredibly …
Keep the faith: Faithful explanations in convolutional neural networks for case-based reasoning
Explaining predictions of black-box neural networks is crucial when applied to decision-
critical tasks. Thus, attribution maps are commonly used to identify important image regions …
critical tasks. Thus, attribution maps are commonly used to identify important image regions …
Concept-based explainable artificial intelligence: A survey
The field of explainable artificial intelligence emerged in response to the growing need for
more transparent and reliable models. However, using raw features to provide explanations …
more transparent and reliable models. However, using raw features to provide explanations …