IMMENS: Integrated Multi-Manager Environment for Naval Ships

The Royal Netherlands Navy is moving towards more autonomous ships to cope with the increasingly complex naval operations while sailing with a reduced manning. The strong expansion of automation and autonomous systems on board requires new solutions for optimal collaboration between the crew and the extensive set of ship systems. We are...

The influence of interdependence and a transparent or explainable communication style on human-robot teamwork

Humans and robots are increasingly working together in human-robot teams. Teamwork requires communication, especially when interdependence between team members is high. In previous work, we identified a conceptual difference between sharing what you are doing (i.e., being transparent) and why you are doing it (i.e., being explainable). Although...

The FATE System Iterated: Fair, Transparent and Explainable Decision Making in a Juridical Case

Identifying predictors for energy poverty in Europe using machine learning

In this paper we identify drivers for energy poverty in Europe using machine learning. The establishment of predictors for energy poverty valid across countries is a call made by many experts, since it could pro vide a basis to effectively target energy-poor households with adequate policy measures. We apply a ‘‘low income, high expenditure”...

Unraveling Gap Selection Process During Discretionary Lane Changing by Vehicle Class

This paper studies and compares the gap selection process of multiple vehicle classes (passenger cars, delivery vans, and trucks) within their discretionary lane changing activities. Given a trajectory or a sequence of gap selection decisions, we aim to predict whether a vehicle will change or keep a lane. For this purpose, we use a large...

Privacy-Preserving Contrastive Explanations with Local Foil Trees

We present the first algorithm that combines privacy-preserving technologies and state-of-the-art explainable AI to enable privacy-friendly explanations of black-box AI models. We provide a secure algorithm for contrastive explanations of black-box machine learning models that securely trains and uses local foil trees. Our work shows that the...

Human-centered XAI: Developing design patterns for explanations of clinical decision support systems

Much of the research on eXplainable Artificial Intelligence (XAI) has centered on providing transparency of machine learning models. More recently, the focus on human-centered approaches to XAI has increased. Yet, there is a lack of practical methods and examples on the integration of human factors into the development processes of AI-generated...

A Two-Dimensional Explanation Framework to Classify AI as Incomprehensible, Interpretable, or Understandable

Because of recent and rapid developments in Artificial Intelligence (AI), humans and AI-systems increasingly work together in human-agent teams. However, in order to effectively leverage the capabilities of both, AI-systems need to be understandable to their human teammates. The branch of eXplainable AI (XAI) aspires to make AI-systems more...

The FATE System: FAir, Transparent and Explainable Decision making

AI tools are becoming more commonly used in a variety of application domains. In this paper, we describe a system named FATE that combines state of the art AI tools. The goal of the FATE system is decision support with use of ongoing human-AI co-learning, explainable AI and fair, bias-free and secure usage of data. These topics are societally...

Moral Decision Making in Human-Agent Teams: Human Control and the Role of Explanations

With the progress of Artificial Intelligence, intelligent agents are increasingly being deployed in tasks for which ethical guidelines and moral values apply. As artificial agents do not have a legal position, humans should be held accountable if actions do not comply, implying humans need to exercise control. This is often labeled as Meaningful...

Interpretable confidence measures for decision support systems

Decision support systems (DSS) have improved significantly but are more complex due to recent advances in Artificial Intelligence. Current XAI methods generate explanations on model behaviour to facilitate a user's understanding, which incites trust in the DSS. However, little focus has been on the development of methods that establish and...

Six Challenges for Human-AI Co-learning

The increasing use of ever-smarter AI-technology is changing the way individuals and teams learn and perform their tasks. In hybrid teams, people collaborate with artificially intelligent partners. To utilize the different strengths and weaknesses of human and artificial intelligence, a hybrid team should be designed upon the principles that...

The role of normware in trustworthy and explainable AI

For being potentially destructive, in practice incomprehensible and for the most unintelligible, contemporary technology is setting high challenges on our society. New conception methods are urgently required. Reorganizing ideas and discussions presented in AI and related fields, this position paper aims to highlight the importance of normware...

LEAFAGE: Example-based and Feature importance-based Explanations for Black-box ML models

Explainable Artificial Intelligence (XAI) is an emergent research field which tries to cope with the lack of transparency of AI systems, by providing human understandable explanations for the underlying Machine Learning models. This work presents a new explanation extraction method called LEAFAGE. Explanations are provided both in terms of...

Using perceptual and cognitive explanations for enhanced human-agent team performance