Pierre Le Bras

We present a comprehensive, in-depth review of ideation assisted by large language models (LLMs), highlighting emerging trends and identifying unaddressed research gaps. In total, we examined 61 studies investigating the application of LLMs in both group and individual ideation processes. From these studies, we derived the Hourglass Ideation Framew...

Stopword removal is a critical stage in many Machine Learning methods but often receives little consideration, it interferes with the model visualizations and disrupts user confidence. Inappropriately chosen or hastily omitted stopwords not only lead to suboptimal performance but also significantly affect the quality of models, thus reducing the wi...

Robotics opens the possibility for safer operations in remote and hazardous environments, with multiple robots deployed to perform tasks that would otherwise present risks for human operators. However, these missions must be carefully planned and monitored to ensure their successful completion while keeping human supervisors in the loop for account...

Searching large digital repositories can be extremely frustrating, as common list-based formats encourage users to adopt a convenience-sampling approach that favours chance discovery and random search, over meaningful exploration. We have designed a methodology that allows users to visually and thematically explore corpora, while developing persona...

The world has seen in 2020 an unprecedented global outbreak of SARS-CoV-2, a new strain of coronavirus, causing the COVID-19 pandemic, and radically changing our lives and work conditions. Many scientists are working tirelessly to find a treatment and a possible vaccine. Furthermore, governments, scientific institutions and companies are acting qui...

Collaboration between human supervisors and remote teams of robots is highly challenging, particularly in high-stakes, distant, hazardous locations, such as off-shore energy platforms. In order for these teams of robots to truly be beneficial, they need to be trusted to operate autonomously, performing tasks such as inspection and emergency respons...

Data visualisations aim at providing accessible and interpretable information for people. At a strategic level, such representations can be used to stimulate decision making. We have found that users are however hesitant to exploit unfamiliar visualisations, and require more material to be confident about their description of an unbiased representa...

Presentation given by M. Chantler at the Pulse of the Machine: People-Driven AI and Data Innovation workshop, during the Business in the Parliament Conference 2018.

Recent successes of artificial intelligence, machine learning, and deep learning have generated exciting challenges in the area of explainability. For societal, regulatory, and utility reasons, systems that exploit these technologies are increasingly being required to explain their outputs to users. In addition , appropriate and timely explanation...

Presentation talk for published paper: Issues Affecting User Confidence in Explanation Systems

Research materials from a collaborative workshop concerning the following research question: What issues do you think most affect user confidence in explanation systems?

Talk given at the 2018 CHI Conference on Human Factors in Computing Systems, presenting our work on using Data-Driven Explanations to study User Confidence in automatically generated Concept Maps.

Automated tools are increasingly being used to generate highly engaging concept maps as an aid to strategic planning and other decision-making tasks. Unless stakeholders can understand the principles of the underlying layout process, however, we have found that they lack confidence and are therefore reluctant to use these maps. In this paper, we pr...

Previously, we have developed a graphical proof strategy language, called PSGraph [4], to support the development and maintenance of large and complex proof tactics for interactive theorem provers. By using labelled hierarchical graphs this formalisation improves upon tactic composition, analysis and maintenance compared with traditional tactic lan...