Author name cluster

Loïs Vanhée

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10 papers

2 author rows

IJCAI Conference 2023 Conference Paper

Ethical By Designer - How to Grow Ethical Designers of Artificial Intelligence (Extended Abstract)

Loïs Vanhée
Melania Borit

Ethical concerns regarding Artificial Intelligence technology have fueled discussions around the ethics training received by its designers. Training designers for ethical behaviour, understood as habitual application of ethical principles in any situation, can make a significant difference in the practice of research, development, and application of AI systems. Building on interdisciplinary knowledge and practical experience from computer science, moral psychology, and pedagogy, we propose a functional way to provide this training.

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IJCAI Conference 2023 Conference Paper

Get Out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula (Extended Abstract)

Rana Tallal Javed
Osama Nasir
Melania Borit
Loïs Vanhée
Elias Zea
Shivam Gupta
Ricardo Vinuesa
Junaid Qadir

This study explores the topics and trends of teaching AI ethics in higher education, using Latent Dirichlet Allocation as the analysis tool. The analyses included 166 courses from 105 universities around the world. Building on the uncovered patterns, we distil a model of current pedagogical practice, the BAG model (Build, Assess, and Govern), that combines cognitive levels, course content, and disciplines. The study critically assesses the implications of this teaching paradigm and challenges practitioners to reflect on their practices and move beyond stereotypes and biases.

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AAMAS Conference 2023 Conference Paper

Models of Anxiety for Agent Deliberation: The Benefits of Anxiety-Sensitive Agents

Arvid Horned
Loïs Vanhée

Anxiety is one of the most critical sources of harm to psychological wellbeing, tied to an array of issues, from discomfort and maladaptive coping to severe pathological disorders –making of anxiety one of the largest economic and social healthcare expenses. AI systems are not neutral to the exposure of individuals and societies to anxiety, and the current emphasis on performance-optimization of current AI systems arguably sets a pathway for a systemic rise of anxiety. As a response to this trend, towards further increasing the human-centeredness of existing applications, this paper is dedicated to depicting the landscape of open challenges, high-impact applications, and promising solutions for designing anxiety-sensitive agents. This paper first circumvents the key components of anxiety through a summary of the extensive psychology literature on anxiety; then shows the feasibility of building agent-based models by putting forward an example of a logical model of anxiety; and last, examines current research fields through the lens of anxiety, highlighting categories of prospective applications and techniques which stand to benefit from anxiety-sensitive agents.

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ICAPS Conference 2022 Conference Paper

Anxiety-Sensitive Planning: From Formal Foundations to Algorithms and Applications

Loïs Vanhée
Laurent Jeanpierre
Abdel-Illah Mouaddib

Anxiety is the most prominent source of stress, harmful behaviours, and psychological disorders. AI systems, usually built for maximizing performance, increase the worldwide exposition to anxiety. This foundational paper introduces Anxiety-Aware Markov Decision Processes (AA-MDPs), the first formalism rooted in fundamental psychology research for modelling the anxiety tied to policies. In addition, this paper formalizes models and practical polynomial algorithms for generating anxiety-sensitive policies. Empirical validation demonstrates that AA-MDPs policies replicate the influence of anxiety on human decision-making observed by fundamental psychology research. Last, this paper demonstrates that AA-MDPs are directly applicable for social good, through a real-world use case (Anxiety-Sensitive Itinerary Planning), the immediate applicability for augmenting any formerly-defined MDP model with anxiety-awareness, and direct tracks developing future high-impact models.

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JAIR Journal 2022 Journal Article

Get out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula

Rana Tallal Javed
Osama Nasir
Melania Borit
Loïs Vanhée
Elias Zea
Shivam Gupta
Ricardo Vinuesa
Junaid Qadir

The domain of Artificial Intelligence (AI) ethics is not new, with discussions going back at least 40 years. Teaching the principles and requirements of ethical AI to students is considered an essential part of this domain, with an increasing number of technical AI courses taught at several higher-education institutions around the globe including content related to ethics. By using Latent Dirichlet Allocation (LDA), a generative probabilistic topic model, this study uncovers topics in teaching ethics in AI courses and their trends related to where the courses are taught, by whom, and at what level of cognitive complexity and specificity according to Bloom’s taxonomy. In this exploratory study based on unsupervised machine learning, we analyzed a total of 166 courses: 116 from North American universities, 11 from Asia, 36 from Europe, and 10 from other regions. Based on this analysis, we were able to synthesize a model of teaching approaches, which we call BAG (Build, Assess, and Govern), that combines specific cognitive levels, course content topics, and disciplines affiliated with the department(s) in charge of the course. We critically assess the implications of this teaching paradigm and provide suggestions about how to move away from these practices. We challenge teaching practitioners and program coordinators to reflect on their usual procedures so that they may expand their methodology beyond the confines of stereotypical thought and traditional biases regarding what disciplines should teach and how. This article appears in the AI & Society track.

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JAIR Journal 2022 Journal Article

Viewpoint: Ethical By Designer - How to Grow Ethical Designers of Artificial Intelligence

Loïs Vanhée
Melania Borit

Ethical concerns regarding Artificial Intelligence (AI) technology have fueled discussions around the ethics training received by AI designers. We claim that training designers for ethical behaviour, understood as habitual application of ethical principles in any situation, can make a significant difference in the practice of research, development, and application of AI systems. Building on interdisciplinary knowledge and practical experience from computer science, moral psychology and development, and pedagogy, we propose a functional way to provide this training. This article appears in the special track on AI & Society.

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IROS Conference 2021 Conference Paper

Optimizing Requests for Support in Context-Restricted Autonomy

Loïs Vanhée
Laurent Jeanpierre
Abdel-Illah Mouaddib

Adjustable Autonomy is gaining interest as it alleviates robot management costs, which often restrain non-routine applications. Whereas it seems straightforward to account for the availability of helpers when making plans that involve being granted for support in the future, no existing research covers this issue. As a solution, we formalize the first human-centric model that accounts for operator support dynamics when generating adjustable-autonomy plans. We formalize Restricted Autonomy Levels (RAL) within a Markov-based framework for representing when and what level of support the robot should ask for. This model is combined with a formalization of usual aspects of man-machine collaboration: operator availability, risk of denial and withdrawal, effect of teleoperation, risks and consequences for violating RAL restrictions and backup procedures, should violations occur. We empirically demonstrate, through a detailed example and the deployment on a professional-grade security robot, that the generated plans deeply combine the problem-solving activities of the robot with the management of requested human support, leading to improved performance and decreased operator effort. We also analyse the computational costs of computing policies that ensure a zero-chance of RAL violation.

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AAAI Conference 2019 Conference Paper

Augmenting Markov Decision Processes with Advising

Loïs Vanhée
Laurent Jeanpierre
Abdel-illah Mouaddib

This paper introduces Advice-MDPs, an expansion of Markov Decision Processes for generating policies that take into consideration advising on the desirability, undesirability, and prohibition of certain states and actions. Advice- MDPs enable the design of designing semi-autonomous systems (systems that require operator support for at least handling certain situations) that can efficiently handle unexpected complex environments. Operators, through advising, can augment the planning model for covering unexpected real-world irregularities. This advising can swiftly augment the degree of autonomy of the system, so it can work without subsequent human intervention. This paper details the Advice-MDP formalism, a fast Advice- MDP resolution algorithm, and its applicability for real-world tasks, via the design of a professional-class semi-autonomous robot system ready to be deployed in a wide range of unexpected environments and capable of efficiently integrating operator advising.

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AAMAS Conference 2013 Conference Paper

Agent-Based Evolving Societies

Loïs Vanhée
Jacques Ferber
Frank Dignum

This paper describes a method to build artificial societies that can dynamically expand themselves from bottom-up, in order to cope with environmental changes. This method is then applied to model the evolutions through the first stages of human societies, inspired by social science theories.

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AAMAS Conference 2013 Conference Paper

Artificial Culture in Artificial Societies

Loïs Vanhée

In this article, I present the state of my research on modeling and simulating the impact of a culture on artificial organizations and artificial societies. In particular, I aim at replicating the effect of culture at the individual level in order to observe the consequences at the collective level. Thus, I expect to reproduce organizational structural and performance (efficiency, robustness, flexibility) changes in different countries as well as cultural clashes in cross-cultural settings. On a different time scale, such a model of culture will be used used to investigate the link between the level of development of a society and the individual conception of the world carried on by its culture.

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