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Found 9 papers, 1 papers with code
Should XAI Nudge Human Decisions with Explanation Biasing?
This paper reviews our previous trials of Nudge-XAI, an approach that introduces automatic biases into explanations from explainable AIs (XAIs) with the aim of leading users to better decisions, and it discusses the benefits and challenges.
Dynamic Explanation Emphasis in Human-XAI Interaction with Communication Robot
However, it is not clear how a robot can apply such expressions, or in particular, how we can develop a strategy to adaptively use such expressions depending on the task and user in dynamic interactions.
User Decision Guidance with Selective Explanation Presentation from Explainable-AI
It enables IDSSs to strategically guide users to an AI-suggested decision by predicting the impact of different combinations of explanations on a user's decision and selecting the combination that is expected to minimize the discrepancy between an AI suggestion and a user decision.
Selectively Providing Reliance Calibration Cues With Reliance Prediction
For effective collaboration between humans and intelligent agents that employ machine learning for decision-making, humans must understand what agents can and cannot do to avoid over/under-reliance.
Chat, Shift and Perform: Bridging the Gap between Task-oriented and Non-task-oriented Dialog Systems
We propose CASPER (ChAt, Shift and PERform), a novel dialog system consisting of three types of dialog models: chatter, shifter, and performer.
Unified Questioner Transformer for Descriptive Question Generation in Goal-Oriented Visual Dialogue
In addition, we build a goal-oriented visual dialogue task called CLEVR Ask.
SLAM-Inspired Simultaneous Contextualization and Interpreting for Incremental Conversation Sentences
In many methods, however, only one meaning is considered for one label of a word, and multiple meanings of polysemous words depending on the context are rarely handled.
Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents
In cooperation, the workers must know how co-workers behave.
Bayesian Inference of Self-intention Attributed by Observer
Most of agents that learn policy for tasks with reinforcement learning (RL) lack the ability to communicate with people, which makes human-agent collaboration challenging.
