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Found 32 papers, 4 papers with code
A Closer Look at Generalisation in RAVEN
Humans have a remarkable capacity to draw parallels between concepts, generalising their experience to new domains.
Towards the new XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence
Prior research on AI-assisted human decision-making has explored several different explainable AI (XAI) approaches.
Diverse, Top-k, and Top-Quality Planning Over Simulators
Diverse, top-k, and top-quality planning are concerned with the generation of sets of solutions to sequential decision problems.
Deceptive Reinforcement Learning in Model-Free Domains
We propose the deceptive exploration ambiguity model (DEAM), which learns using the deceptive policy during training, leading to targeted exploration of the state space.
Explaining Model Confidence Using Counterfactuals
In this paper, we show that counterfactual explanations of confidence scores help study participants to better understand and better trust a machine learning model's prediction.
Explainable Goal Recognition: A Framework Based on Weight of Evidence
We introduce and evaluate an eXplainable Goal Recognition (XGR) model that uses the Weight of Evidence (WoE) framework to explain goal recognition problems.
Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support
In this paper, we argue for a paradigm shift from the current model of explainable artificial intelligence (XAI), which may be counter-productive to better human decision making.
Unicode Analogies: An Anti-Objectivist Visual Reasoning Challenge
While progressive-matrix problems (PMPs) are becoming popular for the development and evaluation of analogical reasoning in computer vision, we argue that the dominant methodology in this area struggles to expose the lack of meaningful generalisation in solvers, and reinforces an objectivist stance on perception -- that objects can only be seen one way -- which we believe to be counter-productive.
Concept-based Explanations using Non-negative Concept Activation Vectors and Decision Tree for CNN Models
This work features how important to have more understandable explanations when interpretability is crucial.
Are we measuring trust correctly in explainability, interpretability, and transparency research?
Trust should surely decrease if a model is of poor quality.
Improving Model Understanding and Trust with Counterfactual Explanations of Model Confidence
In this paper, we show that counterfactual explanations of confidence scores help users better understand and better trust an AI model's prediction in human-subject studies.
Efficient Multi-agent Epistemic Planning: Teaching Planners About Nested Belief
Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents.
Collaborative Human-Agent Planning for Resilience
Results show that participants' constraints improved the expected return of the plans by 10% ($p < 0. 05$) relative to baseline plans, demonstrating that human insight can be used in collaborative planning for resilience.
LEx: A Framework for Operationalising Layers of Machine Learning Explanations
Several social factors impact how people respond to AI explanations used to justify AI decisions affecting them personally.
Deceptive Reinforcement Learning for Privacy-Preserving Planning
However, in some situations, we may want to keep a reward function private; that is, to make it difficult for an observer to determine the reward function used.
Directive Explanations for Actionable Explainability in Machine Learning Applications
This paper investigates the prospects of using directive explanations to assist people in achieving recourse of machine learning decisions.
Good proctor or "Big Brother"? AI Ethics and Online Exam Supervision Technologies
This article philosophically analyzes online exam supervision technologies, which have been thrust into the public spotlight due to campus lockdowns during the COVID-19 pandemic and the growing demand for online courses.
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI
We discuss a model of trust inspired by, but not identical to, sociology's interpersonal trust (i. e., trust between people).
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors
Based on the requirements of fidelity (approximate models to target models) and interpretability (being meaningful to people), we design measurements and evaluate a range of matrix factorization methods with our framework.
Federated pretraining and fine tuning of BERT using clinical notes from multiple silos
Large scale contextual representation models, such as BERT, have significantly advanced natural language processing (NLP) in recently years.
Distal Explanations for Model-free Explainable Reinforcement Learning
In this paper we introduce and evaluate a distal explanation model for model-free reinforcement learning agents that can generate explanations for `why' and `why not' questions.
Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence
We proposed and evaluated a confederated learning to training machine learning model to stratify the risk of several diseases among when data are horizontally separated by individual, vertically separated by data type, and separated by identity without patient ID matching.
Let's Make It Personal, A Challenge in Personalizing Medical Inter-Human Communication
Current AI approaches have frequently been used to help personalize many aspects of medical experiences and tailor them to a specific individuals' needs.
Explainable Reinforcement Learning Through a Causal Lens
In this paper, we use causal models to derive causal explanations of behaviour of reinforcement learning agents.
What you get is what you see: Decomposing Epistemic Planning using Functional STRIPS
Epistemic planning --- planning with knowledge and belief --- is essential in many multi-agent and human-agent interaction domains.
A Grounded Interaction Protocol for Explainable Artificial Intelligence
Explainable Artificial Intelligence (XAI) systems need to include an explanation model to communicate the internal decisions, behaviours and actions to the interacting humans.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Contrastive Explanation: A Structural-Model Approach
In this paper, we extend the structural causal model approach to define two complementary notions of contrastive explanation, and demonstrate them on two classical problems in artificial intelligence: classification and planning.
Towards a Grounded Dialog Model for Explainable Artificial Intelligence
We carry out further analysis to identify the relationships between components and sequences and cycles that occur in a dialog.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences
As a result, programmers design software for themselves, rather than for their target audience, a phenomenon he refers to as the `inmates running the asylum'.
Explanation in Artificial Intelligence: Insights from the Social Sciences
There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable.
Social planning for social HRI
Making a computational agent 'social' has implications for how it perceives itself and the environment in which it is situated, including the ability to recognise the behaviours of others.
