b'Tim Miller'

LEx: A Framework for Operationalising Layers of Machine Learning Explanations

Several social factors impact how people respond to AI explanations used...
04/15/2021 ∙ by Ronal Singh, et al. ∙ 0 ∙ share

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Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions

As the use of algorithmic systems in high-stakes decision-making increas...
02/23/2021 ∙ by Henrietta Lyons, et al. ∙ 0 ∙ share

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Fair and Responsible AI: A Focus on the Ability to Contest

As the use of artificial intelligence (AI) in high-stakes decision-makin...
02/22/2021 ∙ by Henrietta Lyons, et al. ∙ 0 ∙ share

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Designing for Contestation: Insights from Administrative Law

A paper presented at the Workshop on Contestability in Algorithmic Syste...
02/08/2021 ∙ by Henrietta Lyons, et al. ∙ 0 ∙ share

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Deceptive Reinforcement Learning for Privacy-Preserving Planning

In this paper, we study the problem of deceptive reinforcement learning ...
02/05/2021 ∙ by Zhengshang Liu, et al. ∙ 0 ∙ share

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Directive Explanations for Actionable Explainability in Machine Learning Applications

This paper investigates the prospects of using directive explanations to...
02/03/2021 ∙ by Ronal Singh, et al. ∙ 0 ∙ share

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Good proctor or "Big Brother"? AI Ethics and Online Exam Supervision Technologies

This article philosophically analyzes online exam supervision technologi...
11/15/2020 ∙ by Simon Coghlan, et al. ∙ 0 ∙ share

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Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI

Trust is a central component of the interaction between people and AI, i...
10/15/2020 ∙ by Alon Jacovi, et al. ∙ 0 ∙ share

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Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors

Convolutional neural network (CNN) models for computer vision are powerf...
06/27/2020 ∙ by Ruihan Zhang, et al. ∙ 14 ∙ share

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Federated pretraining and fine tuning of BERT using clinical notes from multiple silos

Large scale contextual representation models, such as BERT, have signifi...
02/20/2020 ∙ by Dianbo Liu, et al. ∙ 0 ∙ share

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Distal Explanations for Explainable Reinforcement Learning Agents

Causal explanations present an intuitive way to understand the course of...
01/28/2020 ∙ by Prashan Madumal, et al. ∙ 14 ∙ share

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Improving Performance of Multiagent Cooperation Using Epistemic Planning

In most multiagent applications, communication is essential among agents...
10/07/2019 ∙ by Abeer Alshehri, et al. ∙ 0 ∙ share

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Let's Make It Personal, A Challenge in Personalizing Medical Inter-Human Communication

Current AI approaches have frequently been used to help personalize many...
07/29/2019 ∙ by Mor Vered, et al. ∙ 0 ∙ share

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Explainable Reinforcement Learning Through a Causal Lens

Prevalent theories in cognitive science propose that humans understand a...
05/27/2019 ∙ by Prashan Madumal, et al. ∙ 0 ∙ share

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What you get is what you see: Decomposing Epistemic Planning using Functional STRIPS

Epistemic planning --- planning with knowledge and belief --- is essenti...
03/28/2019 ∙ by Guang Hu, et al. ∙ 0 ∙ share

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A Grounded Interaction Protocol for Explainable Artificial Intelligence

Explainable Artificial Intelligence (XAI) systems need to include an exp...
03/05/2019 ∙ by Prashan Madumal, et al. ∙ 0 ∙ share

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Contrastive Explanation: A Structural-Model Approach

The topic of causal explanation in artificial intelligence has gathered ...
11/07/2018 ∙ by Tim Miller, et al. ∙ 0 ∙ share

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Emotionalism within People-Oriented Software Design

In designing most software applications, much effort is placed upon the ...
10/30/2018 ∙ by Mohammadhossein Sherkat, et al. ∙ 0 ∙ share

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Towards a Grounded Dialog Model for Explainable Artificial Intelligence

To generate trust with their users, Explainable Artificial Intelligence ...
06/21/2018 ∙ by Prashan Madumal, et al. ∙ 0 ∙ share

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Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences

In his seminal book `The Inmates are Running the Asylum: Why High-Tech P...
12/02/2017 ∙ by Tim Miller, et al. ∙ 0 ∙ share

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Explanation in Artificial Intelligence: Insights from the Social Sciences

There has been a recent resurgence in the area of explainable artificial...
06/22/2017 ∙ by Tim Miller, et al. ∙ 0 ∙ share

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Social planning for social HRI

Making a computational agent 'social' has implications for how it percei...
02/21/2016 ∙ by Liz Sonenberg, et al. ∙ 0 ∙ share

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Tim Miller

Featured Co-authors

Yoav Goldberg

Mark O. Riedl

Dianbo Liu

Yue Yang

Ana Marasović

Frank Dignum

Alon Jacovi

Christian Muise

Liz Sonenberg

Eduardo Velloso

Prashan Madumal

LEx: A Framework for Operationalising Layers of Machine Learning Explanations

Conceptualising Contestability: Perspectives on Contesting Algorithmic Decisions

Fair and Responsible AI: A Focus on the Ability to Contest

Designing for Contestation: Insights from Administrative Law

Deceptive Reinforcement Learning for Privacy-Preserving Planning

Directive Explanations for Actionable Explainability in Machine Learning Applications

Good proctor or "Big Brother"? AI Ethics and Online Exam Supervision Technologies

Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI

Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors

Federated pretraining and fine tuning of BERT using clinical notes from multiple silos

Distal Explanations for Explainable Reinforcement Learning Agents

Improving Performance of Multiagent Cooperation Using Epistemic Planning

Let's Make It Personal, A Challenge in Personalizing Medical Inter-Human Communication

Explainable Reinforcement Learning Through a Causal Lens

What you get is what you see: Decomposing Epistemic Planning using Functional STRIPS

A Grounded Interaction Protocol for Explainable Artificial Intelligence

Contrastive Explanation: A Structural-Model Approach

Emotionalism within People-Oriented Software Design

Towards a Grounded Dialog Model for Explainable Artificial Intelligence

Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences

Explanation in Artificial Intelligence: Insights from the Social Sciences

Social planning for social HRI