Publications in reversed chronological order and by categories. Drafts available upon request.
My research spans upon categories related to Responsible AI: governance, safety, human control, transparency, and XAI. In addition, I have published in topics such as agents, cognitive architectures, games AI, and multi-agent systems. You may use the search function to search based on keywords or any other bib attribute.
2025
Proceedings
Governance
Contestability
Contesting Black-Box AI Decisions
Virginia Dignum, Loizos Michael, Juan Carlos Nieves, Marija Slavkovik, Julliett Suarez, and Andreas Theodorou
In 24th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS), 2025
The ”right to contest” decisions that have consequences on individuals or the society is a well-established democratic right. Contesting a decision is not a matter of simply providing an explanation, but rather of assessing whether the decision and the explanation are permissible against an organization’s governance framework. Yet, albeit the popularity of adjacent fields, little work has been explicitly done on contesting AI decisions. In this paper, we propose that formal argumentation can be used to formulate contestations of decisions made by artificial agents. We extend the discourse on socio-ethical values in AI by conceptualizing our argumentation framework as a formal dialogue, enabling the interaction between humans and agents as decisions are being contested.
@inproceedings{DignumEtAl2025AAMAS,author={Dignum, Virginia and Michael, Loizos and Nieves, Juan Carlos and Slavkovik, Marija and Suarez, Julliett and Theodorou, Andreas},title={Contesting Black-Box AI Decisions},year={2025},booktitle={24th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS)},doi={10.65109/RWZD3386},}
Book Chapter
Governance
Safety
Navigating the sociotechnical labyrinth: dynamic certification for responsible embodied AI
Georgios Bakirtzis, Andrea Aler Tubella, Andreas Theodorou, David Danks, and Ufuk Topcu
In Bi-directionality in Human-AI Collaborative Systems, 2025
Sociotechnical requirements shape the governance of artificially intelligent (AI) systems. In an era where embodied AI technologies are rapidly reshaping various facets of contemporary society, their inherent dynamic adaptability presents a unique blend of opportunities and challenges. Traditional regulatory mechanisms, often designed for static—or slower-paced—technologies, find themselves at a crossroads when faced with the fluid and evolving nature of AI systems. Moreover, typical problems in AI, for example, the frequent opacity and unpredictability of the behavior of the systems, add additional sociotechnical challenges. To address these interconnected issues, we introduce the concept of dynamic certification, an adaptive regulatory framework specifically crafted to keep pace with the continuous evolution of AI systems. The complexity of these challenges requires common progress in multiple domains: technical, socio-governmental, and regulatory. Our proposed transdisciplinary approach is designed to ensure the safe, ethical, and practical deployment of AI systems, aligning them bidirectionally with the real-world contexts in which they operate. By doing so, we aim to bridge the gap between rapid technological advancement and effective regulatory oversight, ensuring that AI systems not only achieve their intended goals but also adhere to ethical standards and societal values.
@incollection{Bakirtzis2025DynamicCert,author={Bakirtzis, Georgios and {Aler Tubella}, Andrea and Theodorou, Andreas and Danks, David and Topcu, Ufuk},title={Navigating the sociotechnical labyrinth: dynamic certification for responsible embodied AI},booktitle={Bi-directionality in Human-AI Collaborative Systems},publisher={Academic Press},year={2025},doi={10.1016/B978-0-44-340553-2.00019-8},}
Journal
XAI
MLOPs
MLOps for Cyberphysical Production Systems: Challenges and Solutions
Leonhard Faubel, Thomas Woudsma, Benjamin Kloepper, Holger Eichelberger, Fabian Buelow, Klaus Schmid, and 4 more authors
While machine learning operations (MLOps) have received significant interest, much less work has been published addressing MLOps in industrial production settings lately, particularly if solutions are not cloud based. This article addresses this shortcoming based on our and our partner’s real industrial experience across different application domains.
@article{FaubelEtAl2025MLOps,author={Faubel, Leonhard and Woudsma, Thomas and Kloepper, Benjamin and Eichelberger, Holger and Buelow, Fabian and Schmid, Klaus and Ghezeljehmeidan, Amir Ghorbani and Methnani, Leila and Theodorou, Andreas and Bång, Magnus},journal={IEEE Software},title={MLOps for Cyberphysical Production Systems: Challenges and Solutions},year={2025},volume={42},number={1},pages={65-73},doi={10.1109/MS.2024.3441101},}
Georgios Bakirtzis, Manolis Chiou, and Andreas Theodorou
In Artificial Intelligence Research and Development - Proceedings of the 26th International Conference of the Catalan Association for Artificial Intelligence, 2024
26th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2024
Variable autonomy equips a system, such as a robot, with mixed initiatives such that it can adjust its independence level based on the task’s complexity and the surrounding environment. Variable autonomy solves two main problems in robotic planning: the first is the problem of humans being unable to keep focus in monitoring and intervening during robotic tasks without appropriate human factor indicators, and the second is achieving mission success in unpredictable and uncertain environments in the face of static reward structures. An open problem in variable autonomy is developing robust methods to dynamically balance autonomy and human intervention in real-time, ensuring optimal performance and safety in unpredictable and evolving environments. We posit that addressing unpredictable and evolving environments through an addition of rule-based symbolic logic has the potential to make autonomy adjustments more contextually reliable and adding feedback to reinforcement learning through data from mixed-initiative control further increases efficacy and safety of autonomous behavior.
@inproceedings{Bakirtzis2024NegotiatingCN,title={Negotiating Control: Neurosymbolic Variable Autonomy},author={Bakirtzis, Georgios and Chiou, Manolis and Theodorou, Andreas},note={26th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2024},year={2024},doi={10.3233/FAIA240432},language={English},series={Frontiers in Artificial Intelligence and Applications},publisher={IOS Press BV},pages={178-181},editor={Alsinet, Teresa and Vilasis--Cardona, Xavier and Garcia-Costa, Daniel and Alvarez-Garcia, Elena},booktitle={Artificial Intelligence Research and Development - Proceedings of the 26th International Conference of the Catalan Association for Artificial Intelligence},}
Journal
Human Control
Transparency
Who’s in Charge Here? A Survey on Trustworthy AI in Variable Autonomy Robotic Systems
Leila Methnani, Manolis Chiou, Virginia Dignum, and Andreas Theodorou
This article surveys the Variable Autonomy (VA) robotics literature that considers two contributory elements to Trustworthy AI: transparency and explainability. These elements should play a crucial role when designing and adopting robotic systems, especially in VA where poor or untimely adjustments of the system’s level of autonomy can lead to errors, control conflicts, user frustration, and ultimate disuse of the system. Despite this need, transparency and explainability is, to the best of our knowledge, mostly overlooked in VA robotics literature or is not considered explicitly. In this article, we aim to present and examine the most recent contributions to the VA literature concerning transparency and explainability. In addition, we propose a way of thinking about VA by breaking these two concepts down based on: the mission of the human-robot team; who the stakeholder is; what needs to be made transparent or explained; why they need it; and how it can be achieved. Last, we provide insights and propose ways to move VA research forward. Our goal with this article is to raise awareness and inter-community discussions among the Trustworthy AI and the VA robotics communities.
@article{MethnaniEtAl2024WhoIsInChargeVASurvey,author={Methnani, Leila and Chiou, Manolis and Dignum, Virginia and Theodorou, Andreas},title={Who's in Charge Here? A Survey on Trustworthy AI in Variable Autonomy Robotic Systems},year={2024},issue_date={July 2024},publisher={Association for Computing Machinery},address={New York, NY, USA},volume={56},number={7},issn={0360-0300},url={https://doi.org/10.1145/3645090},doi={10.1145/3645090},journal={ACM Computing Surveys},month=apr,articleno={184},numpages={32},keyword={Human Control},}
Proceedings
H-AI Int.
XAI
Clash of the explainers : argumentation for context-appropriate explanations
Leila Methnani, Virginia Dignum, and Andreas Theodorou
In Artificial Intelligence. ECAI 2023 : XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part I, 2024
Understanding when and why to apply any given eXplainable Artificial Intelligence (XAI) technique is not a straightforward task. There is no single approach that is best suited for a given context. This paper aims to address the challenge of selecting the most appropriate explainer given the context in which an explanation is required. For AI explainability to be effective, explanations and how they are presented needs to be oriented towards the stakeholder receiving the explanation. If—in general—no single explanation technique surpasses the rest, then reasoning over the available methods is required in order to select one that is context-appropriate. Due to the transparency they afford, we propose employing argumentation techniques to reach an agreement over the most suitable explainers from a given set of possible explainers. In this paper, we propose a modular reasoning system consisting of a given mental model of the relevant stakeholder, a reasoner component that solves the argumentation problem generated by a multi-explainer component, and an AI model that is to be explained suitably to the stakeholder of interest. By formalizing supporting premises—and inferences—we can map stakeholder characteristics to those of explanation techniques. This allows us to reason over the techniques and prioritise the best one for the given context, while also offering transparency into the selection decision.
@inproceedings{MethnaniEtAl2024ClashOfExplainers,author={Methnani, Leila and Dignum, Virginia and Theodorou, Andreas},booktitle={Artificial Intelligence. ECAI 2023 : XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part I},institution={Umeå University, Department of Computing Science},pages={7--23},title={Clash of the explainers : argumentation for context-appropriate explanations},series={Communications in Computer and Information Science},doi={10.1007/978-3-031-50396-2_1},isbn={978-3-031-50396-2},year={2024},}
Proceedings
XAI
A MLOps Architecture for XAI in Industrial Applications
Leonhard Faubel, Thomas Woudsma, Leila Methnani, Amir Ghorbani Ghezeljhemeidan, Fabian Buelow, Klaus Schmid, and 5 more authors
In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA), 2024
Machine learning (ML) has become popular in the industrial sector as it helps to improve operations, increase efficiency, and reduce costs. However, deploying and managing ML models in production environments can be complex. This is where Machine Learning Operations (MLOps) comes in. MLOps aims to facilitate this deployment and management process. One of the MLOps challenges is understanding how ML models reason, which is key to trust and acceptance. Here, explainable AI (XAI) can help. Better error identification and improved model accuracy are only two resulting advantages. An often neglected fact is that deployed models are bypassed when model performance or explanations do not meet user expectations. In this paper, we provide a novel reference architecture to address the challenge of integrating explanations and feedback capabilities into MLOps. Our architecture is implemented in a series of industrial use cases in the project EXPLAIN. The proposed MLOps software architecture has several advantages. It provides an efficient way to manage ML models in production environments. Further, it allows for integrating explanations into the development and deployment processes.
@inproceedings{FaubelEtAl2024MLOPs,author={Faubel, Leonhard and Woudsma, Thomas and Methnani, Leila and Ghezeljhemeidan, Amir Ghorbani and Buelow, Fabian and Schmid, Klaus and van Driel, Willem D. and Kloepper, Benjamin and Theodorou, Andreas and Nosratinia, Mohsen and Bång, Magnus},booktitle={2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)},title={A MLOps Architecture for XAI in Industrial Applications},year={2024},pages={1-4},doi={10.1109/ETFA61755.2024.10711084},}
Book Chapter
Governance
Responsible AI at work : incorporating human values
Andreas Theodorou and Andrea Aler Tubella
In Handbook of artificial intelligence at work : interconnections and policy implications, 2024
@incollection{TheodorouAler2024RAIAtWork,author={Theodorou, Andreas and Aler Tubella, Andrea},booktitle={Handbook of artificial intelligence at work : interconnections and policy implications},institution={Umeå University, Department of Computing Science},pages={32--46},title={Responsible AI at work : incorporating human values},url={https://www.e-elgar.com/shop/gbp/handbook-of-artificial-intelligence-at-work-9781800889965.html},isbn={9781800889965},doi={10.4337/9781800889972.00010},year={2024},}
2023
Proceedings
Governance
Pedagogy
Operationalising AI ethics : conducting socio-technical assessment
Leila Methnani, Mattias Brännström, and Andreas Theodorou
In Human-Centered Artificial Intelligence : Advanced Lectures, 2023
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)Conference series: ACAI: ECCAI Advanced Course on Artificial Intelligence
Several high profile incidents that involve Artificial Intelligence (AI) have captured public attention and increased demand for regulation. Low public trust and attitudes towards AI reinforce the need for concrete policy around its development and use. However, current guidelines and standards rolled out by institutions globally are considered by many as high-level and open to interpretation, making them difficult to put into practice. This paper presents ongoing research in the field of Responsible AI and explores numerous methods of operationalising AI ethics. If AI is to be effectively regulated, it must not be considered as a technology alone—AI is embedded in the fabric of our societies and should thus be treated as a socio-technical system, requiring multi-stakeholder involvement and employment of continuous value-based methods of assessment. When putting guidelines and standards into practice, context is of critical importance. The methods and frameworks presented in this paper emphasise this need and pave the way towards operational AI ethics.
@inproceedings{MethnaniEtAl2023ACAI,author={Methnani, Leila and Br{\"a}nnstr{\"o}m, Mattias and Theodorou, Andreas},booktitle={Human-Centered Artificial Intelligence : Advanced Lectures},institution={Umeå University, Department of Computing Science},note={Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)Conference series: ACAI: ECCAI Advanced Course on Artificial Intelligence},pages={304--321},title={Operationalising AI ethics : conducting socio-technical assessment},series={Lecture Notes in Computer Science},number={13500},doi={10.1007/978-3-031-24349-3_16},isbn={9783031243486},year={2023},}
Preprint
MLOPs
XAI
Towards an MLOps Architecture for XAI in Industrial Applications
Leonhard Faubel, Thomas Woudsma, Leila Methnani, Amir Ghorbani Ghezeljhemeidan, Fabian Buelow, Klaus Schmid, and 5 more authors
@misc{faubel2023mlopsarchitecturexaiindustrial,title={Towards an MLOps Architecture for XAI in Industrial Applications},author={Faubel, Leonhard and Woudsma, Thomas and Methnani, Leila and Ghezeljhemeidan, Amir Ghorbani and Buelow, Fabian and Schmid, Klaus and van Driel, Willem D. and Kloepper, Benjamin and Theodorou, Andreas and Nosratinia, Mohsen and Bång, Magnus},year={2023},archiveprefix={arXiv},primaryclass={cs.SE},url={https://arxiv.org/abs/2309.12756},doi={10.48550/arXiv.2309.12756},}
Proceedings
Human Control
Variable Autonomy for Human-Robot Teaming (VAT)
Manolis Chiou, Serena Booth, Bruno Lacerda, Andreas Theodorou, and Simon Rothfuß
In HRI ’23 : Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, 2023
As robots are introduced to various domains and applications, Human-Robot Teaming (HRT) capabilities are essential. Such capabilities involve teaming with humans in/on/out-the-loop at different levels of abstraction, leveraging the complementing capabilities of humans and robots. This requires robotic systems with the ability to dynamically vary their level or degree of autonomy to collaborate with the human(s) efficiently and overcome various challenging circumstances. Variable Autonomy (VA) is an umbrella term encompassing such research, including but not limited to shared control and shared autonomy, mixed-initiative, adjustable autonomy, and sliding autonomy. This workshop is driven by the timely need to bring together VA-related research and practices that are often disconnected across different communities as the field is relatively young. The workshop’s goal is to consolidate research in VA. To this end, and given the complexity and span of Human-Robot systems, this workshop will adopt a holistic trans-disciplinary approach aiming to a) identify and classify related common challenges and opportunities; b) identify the disciplines that need to come together to tackle the challenges; c) identify and define common terminology, approaches, methodologies, benchmarks, and metrics; d) define short- and longterm research goals for the community. To achieve these objectives, this workshop aims to bring together industry stakeholders, researchers from fields under the banner of VA, and specialists from other highly related fields such as human factors and psychology. The workshop will consist of a mix of invited talks, contributed papers, and an interactive discussion panel, toward a shared vision for VA.
@inproceedings{ChiouEtAl2023VAT,author={Chiou, Manolis and Booth, Serena and Lacerda, Bruno and Theodorou, Andreas and Rothfuß, Simon},booktitle={HRI '23 : Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction},institution={Karlsruhe Institute of Technology, Karlsruhe, Germany},pages={932--932},title={Variable Autonomy for Human-Robot Teaming (VAT)},series={ACM/IEEE International Conference on Human-Robot Interaction},doi={10.1145/3568294.3579957},isbn={978-1-4503-9970-8},year={2023},}
Preprint
Human Control
Transparency
Robot Health Indicator: A Visual Cue to Improve Level of Autonomy Switching Systems
Aniketh Ramesh, Madeleine Englund, Andreas Theodorou, Rustam Stolkin, and Manolis Chiou
@misc{ramesh2023robothealthindicatorvisual,title={Robot Health Indicator: A Visual Cue to Improve Level of Autonomy Switching Systems},author={Ramesh, Aniketh and Englund, Madeleine and Theodorou, Andreas and Stolkin, Rustam and Chiou, Manolis},year={2023},archiveprefix={arXiv},primaryclass={cs.RO},url={https://arxiv.org/abs/2303.06776},}
Editorial
Governance
From fear to action : AI governance and opportunities for all
Kevin Baum, Joanna Bryson, Frank Dignum, Virginia Dignum, Marko Grobelnik, Holger Hoos, and 7 more authors
@article{BaumEtAl2023FromFearToAction,author={Baum, Kevin and Bryson, Joanna and Dignum, Frank and Dignum, Virginia and Grobelnik, Marko and Hoos, Holger and Irgens, Morten and Lukowicz, Paul and Muller, Catelijne and Rossi, Francesca and Shawe-Taylor, John and Theodorou, Andreas and Vinuesa, Ricardo},journal={Frontiers in Computer Science},eid={1210421},title={From fear to action : AI governance and opportunities for all},volume={5},doi={10.3389/fcomp.2023.1210421},year={2023},}
Journal
Governance
Garbage in, toxic data out : a proposal for ethical artificial intelligence sustainability impact statements
Ronny Bogani, Andreas Theodorou, Luca Arnaboldi, and Robert H. Wortham
Data and autonomous systems are taking over our lives, from healthcare to smart homes very few aspects of our day to day are not permeated by them. The technological advances enabled by these technologies are limitless. However, with advantages so too come challenges. As these technologies encompass more and more aspects of our lives, we are forgetting the ethical, legal, safety and moral concerns that arise as an outcome of integrating our lives with technology. In this work, we study the lifecycle of artificial intelligence from data gathering to deployment, providing a structured analytical assessment of the potential ethical, safety and legal concerns. The paper then presents the foundations for the first ethical artificial intelligence sustainability statement to guide future development of AI in a safe and sustainable manner.
@article{Bogani1706385,author={Bogani, Ronny and Theodorou, Andreas and Arnaboldi, Luca and Wortham, Robert H.},institution={Department of Electrical Engineering, University of Bath, Bath, UK},journal={AI and Ethics},note={Published online: 20 October 2022},pages={1135--1142},title={Garbage in, toxic data out : a proposal for ethical artificial intelligence sustainability impact statements},volume={3},doi={10.1007/s43681-022-00221-0},year={2023},}
2022
Proceedings
Governance
Safety
Let it RAIN for Social Good
Mattias Brännström, Andreas Theodorou, and Virginia Dignum
Artificial Intelligence (AI) as a highly transformative technology take on a special role as both an enabler and a threat to UN Sustainable Development Goals (SDGs). AI Ethics and emerging high-level policy efforts stand at the pivot point between these outcomes but is barred from effect due the abstraction gap between high-level values and responsible action. In this paper the Responsible Norms (RAIN) framework is presented, bridging this gap thereby enabling effective high-level control of AI impact. With effective and operationalized AI Ethics, AI technologies can be directed towards global sustainable development.
@inproceedings{BrannstromEtAl2022AISafety,author={Brännström, Mattias and Theodorou, Andreas and Dignum, Virginia},title={Let it {RAIN} for Social Good},booktitle={IJCAI 2022 Workshop on AISafety},year={2022},note={Best paper award.},archiveprefix={arXiv},}
Proceedings
Games AI
MAS
Embracing AWKWARD! Real-time Adjustment of Reactive Planning Using Social Norms
Leila Methnani, Andreas Antoniades, and Andreas Theodorou
In Proceedings of the Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems (COINE) 2022, 2022
This paper presents the AWKWARD architecture for the development of hybrid agents in Multi-Agent Systems. AWKWARD agents can have their plans re-configured in real time to align with social role requirements under changing environmental and social circumstances. The proposed hybrid architecture makes use of Behaviour Oriented Design (BOD) to develop agents with reactive planning and of the well-established OperA framework to provide organisational, social, and interaction definitions in order to validate and adjust agents’ behaviours. Together, OperA and BOD can achieve real-time adjustment of agent plans for evolving social roles, while providing the additional benefit of transparency into the interactions that drive this behavioural change in individual agents. We present this architecture to motivate the bridging between traditional symbolic- and behaviour-based AI communities, where such combined solutions can help MAS researchers in their pursuit of building stronger, more robust intelligent agent teams. We use DOTA2—a game where success is heavily dependent on social interactions—as a medium to demonstrate a sample implementation of our proposed hybrid architecture.
@inproceedings{MethnaniEtAl2022COINE,author={Methnani, Leila and Antoniades, Andreas and Theodorou, Andreas},title={Embracing {AWKWARD}! Real-time Adjustment of Reactive Planning Using Social Norms},booktitle={Proceedings of the Coordination, Organizations, Institutions, Norms, and Ethics for Governance of Multi-Agent Systems (COINE) 2022},year={2022},doi={10.1007/978-3-031-20845-4_4},url={https://dl.acm.org/doi/10.1007/978-3-031-20845-4_4},}
Proceedings
Governance
Good AI for Good : How the AI strategies of the Nordic countries address the sustainable development goals
Andreas Theodorou, Juan Carlos Nieves, and Virginia Dignum
In Proceedings of the 2nd Workshop on Adverse Impacts and Collateral Effects of AI Technologies, 2022
Developed and used responsibly Artificial Intelligence (AI) is a force for global sustainable development. Given this opportunity, we expect that the many of the existing guidelines and recommendations for trustworthy or responsible AI will provide explicit guidance on how AI can contribute to the achievement of United Nations’ Sustainable Development Goals (SDGs). This would in particular be the case for the AI strategies of the Nordic countries, at least given their high ranking and overall political focus when it comes to the achievement of the SDGs. In this paper, we present an analysis of existing AI recommendations from 10 different countries or organisations based on topic modelling techniques to identify how much these strategy documents refer to the SDGs. The analysis shows no significant difference on how much these documents refer to SDGs. Moreover, the Nordic countries are not different from the others albeit their long-term commitment to SDGs. More importantly, references to gender equality (SDG 5) and inequality (SDG 10), as well as references to environmental impact of AI development and use, and in particular the consequences for life on earth, are notably missing from the guidelines.
@inproceedings{TheodorouEtAl2022GoodAIforGood,author={Theodorou, Andreas and Nieves, {Juan Carlos} and Dignum, Virginia},booktitle={Proceedings of the 2nd Workshop on Adverse Impacts and Collateral Effects of AI Technologies},pages={46--53},publisher={CEUR-WS},title={Good AI for Good : How the AI strategies of the Nordic countries address the sustainable development goals},series={CEUR Workshop Proceedings},number={3275},url={https://ceur-ws.org/Vol-3275/},year={2022},}
Journal
Governance
Fairness
A sociotechnical perspective for the future of AI: narratives, inequalities, and human control
Different people have different perceptions about artificial intelligence (AI). It is extremely important to bring together all the alternative frames of thinking—from the various communities of developers, researchers, business leaders, policymakers, and citizens—to properly start acknowledging AI. This article highlights the ‘fruitful collaboration’ that sociology and AI could develop in both social and technical terms. We discuss how biases and unfairness are among the major challenges to be addressed in such a sociotechnical perspective. First, as intelligent machines reveal their nature of ‘magnifying glasses’ in the automation of existing inequalities, we show how the AI technical community is calling for transparency and explainability, accountability and contestability. Not to be considered as panaceas, they all contribute to ensuring human control in novel practices that include requirement, design and development methodologies for a fairer AI. Second, we elaborate on the mounting attention for technological narratives as technology is recognized as a social practice within a specific institutional context. Not only do narratives reflect organizing visions for society, but they also are a tangible sign of the traditional lines of social, economic, and political inequalities. We conclude with a call for a diverse approach within the AI community and a richer knowledge about narratives as they help in better addressing future technical developments, public debate, and policy. AI practice is interdisciplinary by nature and it will benefit from a socio-technical perspective.
@article{SartoriTheodorou2022,author={Sartori, Laura and Theodorou, Andreas},title={A sociotechnical perspective for the future of AI: narratives, inequalities, and human control},journal={Ethics of Information Technology},year={2022},doi={10.1007/s10676-022-09624-3},url={https://link.springer.com/article/10.1007/s10676-022-09624-3},volume={24},}
2021
Journal
Human Control
Governance
Let Me Take Over: Variable Autonomy For Meaningful Human Control
Leila Methnani, Andrea Aler Tubella, Virginia Dignum, and Andreas Theodorou
As Artificial Intelligence (AI) continues to expand its reach, the demand for human control and the development of AI systems that adhere to our legal, ethical, and social values also grows. Many (international and national) institutions have taken steps in this direction and published guidelines for the development and deployment of responsible AI systems. These guidelines, however, rely heavily on high-level statements that provide no clear criteria for system assessment, making the effective control over systems a challenge. “Human oversight” is one of the requirements being put forward as a means to support human autonomy and agency. In this paper, we argue that human presence alone does not meet this requirement and that such a misconception may limit the use of automation where it can otherwise provide so much benefit across industries. We therefore propose the development of systems with variable autonomy—dynamically adjustable levels of autonomy—as a means of ensuring meaningful human control over an artefact by satisfying all three core values commonly advocated in ethical guidelines: accountability, responsibility, and transparency.
@article{Methnani2021Frontiers,author={Methnani, Leila and {Aler Tubella}, Andrea and Dignum, Virginia and Theodorou, Andreas},title={Let Me Take Over: Variable Autonomy For Meaningful Human Control},journal={Frontiers in Artificial Intelligence},year={2021},volume={4},pages={133},url={https://www.frontiersin.org/article/10.3389/frai.2021.737072},doi={10.3389/frai.2021.737072},issn={2624-8212},}
Journal
Governance
Transparency
IEEE P7001: A Proposed Standard on Transparency
Alan F. T. Winfield, Serena Booth, Louise A. Dennis, Takashi Egawa, Helen Hastie, Naomi Jacobs, and 7 more authors
This paper describes IEEE P7001, a new draft standard on transparency of autonomous systems1. In the paper, we outline the development and structure of the draft standard. We present the rationale for transparency as a measurable, testable property. We outline five stakeholder groups: users, the general public and bystanders, safety certification agencies, incident/accident investigators and lawyers/expert witnesses, and explain the thinking behind the normative definitions of “levels” of transparency for each stakeholder group in P7001. The paper illustrates the application of P7001 through worked examples of both specification and assessment of fictional autonomous systems.
@article{WinfieldEtAl2021Frontiers,author={Winfield, {Alan F. T}. and Booth, {Serena} and Dennis, {Louise A.} and Egawa, Takashi and Hastie, Helen and Jacobs, Naomi and Muttram, {Roderick I.} and Olszewska, {Joanna I.} and Rajabiyazdi, Fahimeh and Theodorou, Andreas and Underwood, {Mark A.} and Wortham, {Robert H.} and Watson, Eleanor},title={IEEE P7001: A Proposed Standard on Transparency},journal={Frontiers in Robotics and AI},volume={8},issue={665729},year={2021},url={https://www.frontiersin.org/article/10.3389/frobt.2021.665729},doi={10.3389/frobt.2021.665729},issn={2296-9144},}
Proceedings
Governance
Transparency
Interrogating the Black Box: Transparency through Information-Seeking Dialogues
Andrea Aler Tubella, Andreas Theodorou, and Juan Carlos Nieves
In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, 2021
This paper is preoccupied with the following question: given a (possibly opaque) learning system, how can we understand whether its behaviour adheres to governance constraints? The answer can be quite simple: we just need to "ask" the system about it. We propose to construct an investigator agent to query a learning agent– the suspect agent– to investigate its adherence to a given ethical policy in the context of an information-seeking dialogue, modeled in formal argumentation settings. This formal dialogue framework is the main contribution of this paper. Through it, we break down compliance checking mechanisms into three modular components, each of which can be tailored to various needs in a vast amount of ways: an investigator agent, a suspect agent, and an acceptance protocol determining whether the responses of the suspect agent comply with the policy. This acceptance protocol presents a fundamentally different approach to aggregation: rather than using quantitative methods to deal with the non-determinism of a learning system, we leverage the use of argumentation semantics to investigate the notion of properties holding consistently. Overall, we argue that the introduced formal dialogue framework opens many avenues both in the area of compliance checking and in the analysis of properties of opaque systems.
2020
Report
Governance
Final Anaysis on the EU Whitepaper on AI
Catelijne Muller, Virginia Dignum, and Andreas Theodorou
The right to contest a decision with consequences on individuals or the society is a well-established democratic right. Despite this right also being explicitly included in GDPR in reference to automated decision-making, its study seems to have received much less attention in the AI literature compared, for example, to the right for explanation. This paper investigates the type of assurances that are needed in the contesting process when algorithmic black-boxes are involved, opening new questions about the interplay of contestability and explainability. We argue that specialised complementary methodologies to evaluate automated decision-making in the case of a particular decision being contested need to be developed. Further, we propose a combination of well-established software engineering and rule-based approaches as a possible socio-technical solution to the issue of contestability, one of the new democratic challenges posed by the automation of decision making.
@inproceedings{AlerTubellaEtAl2020RULEML,author={{Aler Tubella}, Andrea and Theodorou, Andreas and Dignum, Virginia and Michael, Loizos},booktitle={4th International Joint Conference on Rules and Reasoning (RuleML)},month=jun,address={Olso, Norway},title={{Contestable Black Boxes}},doi={10.1007/978-3-030-57977-7_12},year={2020},}
Journal
Governance
Safety
A socio-technical framework for digital contact tracing
Ricardo Vinuesa, Andreas Theodorou, Manuela Battaglini, and Virginia Dignum
In their efforts to tackle the COVID-19 crisis, decision makers are considering the development and use of smartphone applications for contact tracing. Even though these applications differ in technology and methods, there is an increasing concern about their implications for privacy and human rights. Here we propose a framework to evaluate their suitability in terms of impact on the users, employed technology and governance methods. We illustrate its usage with three applications, and with the European Data Protection Board (EDPB) guidelines, highlighting their limitations.
@article{VinuesaEtAl2020SocioTechnical,title={A socio-technical framework for digital contact tracing},author={Vinuesa, Ricardo and Theodorou, Andreas and Battaglini, Manuela and Dignum, Virginia},year={2020},journal={Results in Engineering},doi={10.1016/j.rineng.2020.100163},}
Many high-level ethics guidelines for AI have been produced in the past few years. It is time to work towards concrete policies within the context of existing moral, legal and cultural values, say Andreas Theodorou and Virginia Dignum...
@article{TheodorouDignum2020NMI,author={Theodorou, Andreas and Dignum, Virginia},year={2020},doi={10.1038/s42256-019-0136-y},journal={Nature Machine Intelligence},title={{Towards ethical and socio-legal governance in AI}},volume={2},issue={1},url={{https://www.nature.com/articles/s42256-019-0136-y.epdf?shared_access_token=tLQjOyEeJJBLNYBpYAddZ9RgN0jAjWel9jnR3ZoTv0OKielulWtfIKdoTkc7o23A4ag4RzhLocCFIkMqRYeFumYGAnLqPSfK_tQ3761isKFC32POZ17DGXFsQMNDEcD8X2AnDXspfKQtpudOtnxcvQ%3D%3D}},}
Book Chapter
Governance
Why Artificial Intelligence is a matter of Design
Andreas Theodorou
In Artificial Intelligence: Reflections in Philosophy, Theology, and Social Sciences, 2020
Unlike other human-made objects, the ability for intelligent systems to exhibit agency, and even appear anthropomorphic, leads to moral confusion about their status in society. As Himma states “If something walks, talks, and behaves enough like me, I might not be justified in thinking that it has a mind, but I surely have an obligation, if our ordinary reactions regarding other people are correct, to treat them as if they are moral agents.” Here, I present an evaluation of the requirements for moral agency and moral patiency. I examine human morality through a presentation of a high-level ontology of the human action-selection system. Then, drawing parallels between natural and artificial intelligence, I discuss the limitations and bottlenecks of intelligence, demonstrating how an ‘all-powerful’ Artificial General Intelligence would not only entail omniscience, but also be impossible. I demonstrate throughout this Chapter how culture determines the moral status of all entities, as morality and law are human-made ‘fictions’ that help us guide our actions. This means that our moral spectrum can be altered to include machines. However, there are both descriptive and normative arguments for why such a move is not only avoidable, but also should be avoided.
@incollection{Theodorou2020AIReflections,author={Theodorou, Andreas},booktitle={Artificial Intelligence: Reflections in Philosophy, Theology, and Social Sciences},editor={Goecke, Benedikt Paul and Rosenthal-von der P{\"{u}}tten, Astrid Marieke},publisher={Brill},title={{Why Artificial Intelligence is a matter of Design}},doi={10.30965/9783957437488_009},year={2020},}
2019
Book Chapter
Governance
Cogn. Arch.
How Society Can Maintain Human-Centric Artificial Intelligence
Joanna J Bryson and Andreas Theodorou
In Human-Centered Digitalization and Services, 2019
Although not a universally held goal, maintaining human-centric artificial intelligence is necessary for society’s long-term stability. Fortunately, the legal and technological problems of maintaining control are actually fairly well understood and amenable to engineering. The real problem is establishing the social and political will for assigning and maintaining accountability for artifacts when these artefacts are generated or used. In this chapter we review the necessity and tractability of maintaining human control, and the mechanisms by which this can be achieved. What makes the problem both most interesting and most threatening is that achieving consensus around such an approach requires at least some measure of agreement on broad existential concerns.
@incollection{BrysonTheodorou2019,author={Bryson, Joanna J and Theodorou, Andreas},booktitle={Human-Centered Digitalization and Services},doi={10.1007/978-981-13-7725-9_16},editor={Toivonen-Noro, Marja and Saari, Evelina and Melkas, Helin{\"{a}} and Hasu, Mervin},pages={305--323},publisher={Springer},title={{How Society Can Maintain Human-Centric Artificial Intelligence}},url={http://www.cs.bath.ac.uk/{~}jjb/web/publications.html http://link.springer.com/10.1007/978-981-13-7725-9{\_}16},year={2019},}
Proceedings
H-AI Int.
Transparency
Improving Robot Transparency: An Investigation With Mobile Augmented Reality
Alexandros Rotsidis, Andreas Theodorou, Joanna J. Bryson, and Robert H. Wortham
In 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Oct 2019
@inproceedings{RotsidisEtAl2019ROMAN,address={New Delhi, India},author={Rotsidis, Alexandros and Theodorou, Andreas and Bryson, Joanna J. and Wortham, Robert H.},booktitle={2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},doi={10.1109/RO-MAN46459.2019.8956390},isbn={978-1-7281-2622-7},month=oct,pages={1--8},publisher={IEEE},title={{Improving Robot Transparency: An Investigation With Mobile Augmented Reality}},url={https://ieeexplore.ieee.org/document/8956390/},year={2019},}
Transparency is a key consideration for the ethical design and use of Artificial Intelligence, and has recently become a topic of considerable public interest and debate. We frequently use philosophical, mathematical, and biologically inspired techniques for building artificial, interactive, intelligent agents. Yet despite these well-motivated inspirations, the resulting intelligence is often developed as a black box, communicating no understanding of how the underlying real-time decision making functions. This compromises both the safety of such systems and fair attribution of moral responsibility and legal accountability when incidents occur. This dissertation provides the knowledge and software tools to make artificially intelligent agents more transparent, allowing a direct understanding of the action-selection system of such a system. The use of transparency, as demonstrated in this document, helps not only with the debugging of intelligent agents, but also with the public’s understanding of Artificial Intelligence (AI) by removing the ’scary’ mystery around "why is it behaving like that". In the research described in this document I investigate and compare the perception we have of intelligent systems, such as robots and autonomous vehicles, when they are treated as black boxes compared to when we make their action-selection systems transparent. Finally, I make normative and descriptive arguments for the moral status of intelligent systems and contribute to regulatory policy regarding such systems.
@phdthesis{Theodorou2019Thesis,author={Theodorou, Andreas},url={{https://github.com/RecklessCoding/recklesscoding.github.io/raw/master/files/andreasTheodorouThesis.pdf}},school={University of Bath},title={{AI Governance Through a Transparency Lens}},year={2019},}
Proceedings
Games AI
MAS
The Sustainability Game : AI Technology as an Intervention for Public Understanding of Cooperative Investment
Andreas Theodorou, Bryn Bandt-law, and Joanna J Bryson
Cooperative behaviour is a fundamental strategy for survival; it positively affects economies, social relationships, and makes larger societal structures possible. People vary, however, in their willingness to engage in cooperative behaviour in a particular context. Here we examine whether AI can be effectively used to to alter individuals’ implicit understanding of cooperative dynamics, and hence increase cooperation and participation in public goods projects. We developed an intervention—the Sustainability Game (SG)—to allow players to experience the consequences of individual investment strategies on a sustainable society. Results show that the intervention significantly increases individuals’ cooperative behaviour in partially anonymised public goods contexts, but enhances competition one-on-one. This indicates our intervention does improve transparency of the systemic consequences of individual cooperative behaviour.
@inproceedings{Theodorou2019COG,author={Theodorou, Andreas and Bandt-law, Bryn and Bryson, Joanna J},booktitle={IEEE Conference on Games},title={{The Sustainability Game : AI Technology as an Intervention for Public Understanding of Cooperative Investment}},year={2019},doi={10.1109/CIG.2019.8848058},url={https://researchportal.bath.ac.uk/files/196356314/TheodorouBandtLawBrysonCoG19.pdf},}
Proceedings
Governance
Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour
Andrea Aler Tubella, Andreas Theodorou, Virginia Dignum, and Frank Dignum
In International Joint Conference on Artificial Intellignece (IJCAI), 2019
Artificial Intelligence (AI) applications are being used to predict and assess behaviour in multiple domains, such as criminal justice and consumer finance, which directly affect human well-being. However, if AI is to improve people’s lives, then people must be able to trust AI, which means being able to understand what the system is doing and why. Even though transparency is often seen as the requirement in this case, realistically it might not always be possible or desirable, whereas the need to ensure that the system operates within set moral bounds remains. In this paper, we present an approach to evaluate the moral bounds of an AI system based on the monitoring of its inputs and outputs. We place a "glass box" around the system by mapping moral values into explicit verifiable norms that constrain inputs and outputs, in such a way that if these remain within the box we can guarantee that the system adheres to the value. The focus on inputs and outputs allows for the verification and comparison of vastly different intelligent systems; from deep neural networks to agent-based systems. The explicit transformation of abstract moral values into concrete norms brings great benefits in terms of explainability; stakeholders know exactly how the system is interpreting and employing relevant abstract moral human values and calibrate their trust accordingly. Moreover, by operating at a higher level we can check the compliance of the system with different interpretations of the same value. These advantages will have an impact on the well-being of AI systems users at large, building their trust and providing them with concrete knowledge on how systems adhere to moral values.
@inproceedings{AlerTubellaEtAl2019IJCAI,archiveprefix={arXiv},author={{Aler Tubella}, Andrea and Theodorou, Andreas and Dignum, Virginia and Dignum, Frank},booktitle={International Joint Conference on Artificial Intellignece (IJCAI)},title={{Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour}},url={https://www.ijcai.org/proceedings/2019/0802.pdf},year={2019},doi={10.24963/ijcai.2019/802},}
Proceedings
H-AI Int.
Transparency
Slam the Brakes: Perceptions of Moral Decisions in Driving Dilemmas
Artificially intelligent agents are increasingly used for morally-salient decisions of high societal impact. Yet, the decision-making algorithms of such agents are rarely transparent. Further, our perception of, and response to, morally-salient decisions may depend on agent type; artificial or natural (human). We developed a Virtual Reality (VR) simulation involving an autonomous vehicle to investigate our perceptions of a morally-salient decision; first moderated by agent type, and second, by an implementation of transparency. Participants in our user study took the role of a passenger in an autonomous vehicle (AV) which makes a moral choice: crash into one of two human-looking Non-Playable Characters (NPC). Experimental subjects were exposed to one of three conditions: (1) participants were led to believe that the car was controlled by a human, (2) the artificial nature of AV was made explicitly clear in the pre-study briefing, but its decisionmaking system was kept opaque, and (3) a transparent AV that reported back the characteristics of the NPCs that influenced its decision-making process. In this paper, we discuss our results, including the distress expressed by our participants at exposing them to a system that makes decisions based on socio-demographic attributes, and their implications.
@inproceedings{WilsonTheodorou2019,address={Macao, China},author={Wilson, Holly and Theodorou, Andreas},booktitle={IJCAI 2019 Workshop on AISafety},title={{Slam the Brakes: Perceptions of Moral Decisions in Driving Dilemmas}},url={{http://ceur-ws.org/Vol-2419/paper_14.pdf}},year={2019},}
Proceedings
H-AI Int.
Transparency
Robots that make sense: Transparent intelligence through augmented reality
Alexandros Rotsidis, Andreas Theodorou, and Robert H. Wortham
In IUI’19 Workshop on Intelligent User Interfaces for Algorithmic Transparency in Emerging Technologies, 2019
Autonomous robots can be difficult to understand by their develop-ers, let alone by end users. Yet, as they become increasingly integralparts of our societies, the need for affordable easy to use tools toprovide transparency grows. The rise of the smartphone and theimprovements in mobile computing performance have graduallyallowed Augmented Reality (AR) to become more mobile and afford-able. In this paper we review relevant robot systems architectureand propose a new software tool to provide robot transparencythrough the use of AR technology. Our new tool, ABOD3-AR pro-vides real-time graphical visualisation and debugging of a robot’sgoals and priorities as a means for both designers and end usersto gain a better mental model of the internal state and decisionmaking processes taking place within a robot. We also report onour on-going research programme and planned studies to furtherunderstand the effects of transparency to naive users and experts.
@inproceedings{RotsidisEtAl2019IUI,title={Robots that make sense: Transparent intelligence through augmented reality},year={2019},volume={2327},address={Los Angeles, CA USA},author={Rotsidis, Alexandros and Theodorou, Andreas and Wortham, Robert H.},booktitle={IUI'19 Workshop on Intelligent User Interfaces for Algorithmic Transparency in Emerging Technologies},url={https://ceur-ws.org/Vol-2327/IUI19WS-IUIATEC-3.pdf},}
2017
Journal
Governance
Transparency
Designing and implementing transparency for real time inspection of autonomous robots
Andreas Theodorou, Robert H. Wortham, and Joanna J. Bryson
The EPSRC’s Principles of Robotics advises the implementation of transparency in robotic systems, however research related to AI transparency is in its infancy. This paper introduces the reader of the importance of having transparent inspection of intelligent agents and provides guidance for good practice when developing such agents. By considering and expanding upon other prominent definitions found in literature, we provide a robust definition of transparency as a mechanism to expose the decision-making of a robot. The paper continues by addressing potential design decisions developers need to consider when designing and developing transparent systems. Finally, we describe our new interactive intelligence editor, designed to visualise, develop and debug real-time intelligence.
@article{Theodorou2017ConnectionScience,author={Theodorou, Andreas and Wortham, Robert H. and Bryson, Joanna J.},doi={10.1080/09540091.2017.1310182},issn={13600494},journal={Connection Science},number={3},pages={230--241},title={{Designing and implementing transparency for real time inspection of autonomous robots}},url={https://researchportal.bath.ac.uk/files/154473870/TheodorouDesigningAndImplementingTransparency.pdf},volume={29},year={2017},}
As robot reasoning becomes more complex, debugging becomes increasingly hard based solely on observable behaviour, even for robot designers and technical specialists. Similarly, non-specialist users find it hard to create useful mental models of robot reasoning solely from observed behaviour. The EPSRC Principles of Robotics mandate that our artefacts should be transparent, but what does this mean in practice, and how does transparency affect both trust and utility? We investigate this relationship in the literature and find it to be complex, particularly in non industrial environments where transparency may have a wider range of effects on trust and utility depending on the application and purpose of the robot. We outline our programme of research to support our assertion that it is nevertheless possible to create transparent agents that are emotion-ally engaging despite having a transparent machine nature.
@article{Wortham2017ConnScience,author={Wortham, Robert H. and Theodorou, Andreas},doi={10.1080/09540091.2017.1313816},issn={13600494},journal={Connection Science},number={3},pages={242--248},title={{Robot transparency, trust and utility}},url={https://researchportal.bath.ac.uk/files/154037267/transparency.pdf},volume={29},year={2017},google_scholar={d1gkVwhDpl0C}}
Proceedings
H-AI Int.
Transparency
Improving robot transparency: Real-Time visualisation of robot AI substantially improves understanding in naive observers
Robert H. Wortham, Andreas Theodorou, and Joanna J. Bryson
In RO-MAN 2017 - 26th IEEE International Symposium on Robot and Human Interactive Communication, 2017
@inproceedings{Wortham2017ROMAN,author={Wortham, Robert H. and Theodorou, Andreas and Bryson, Joanna J.},booktitle={RO-MAN 2017 - 26th IEEE International Symposium on Robot and Human Interactive Communication},doi={10.1109/ROMAN.2017.8172491},isbn={9781538635186},issn={1528-1132 0009-921X},pages={1424--1431},publisher={IEEE},title={{Improving robot transparency: Real-Time visualisation of robot AI substantially improves understanding in naive observers}},url={https://researchportal.bath.ac.uk/files/155724003/robot{\_}transparency{\_}experiment.pdf},volume={2017-Janua},year={2017},}
Proceedings
H-AI Int.
Transparency
Robot transparency: Improving understanding of intelligent behaviour for designers and users
Robert H. Wortham, Andreas Theodorou, and Joanna J. Bryson
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017
Autonomous robots can be difficult to design and understand. Designers have difficulty decoding the behaviour of their own robots simply by observing them. Naive users of robots similarly have diculty decoding robot behaviour simply through observation. In this paper we review relevant robot systems architecture, design and transparency literature, and report on a programme of research to investigate practical approaches to improve robot transparency. We report on the investigation of real-time graphical and vocalised outputs as a means for both designers and end users to gain a better mental model of the internal state and decision making processes taking place within a robot. This approach, combined with a graphical approach to behaviour design, o ers improved transparency for robot designers. We also report on studies of users’ understanding, where significant improvement has been achieved using both graphical and vocalisation transparency approaches.
@article{WorthamEtAl2017TAROS,author={Wortham, Robert H. and Theodorou, Andreas and Bryson, Joanna J.},doi={10.1007/978-3-319-64107-2_22},isbn={9783319641065},issn={16113349},journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},pages={274--289},title={{Robot transparency: Improving understanding of intelligent behaviour for designers and users}},volume={10454},year={2017},}
2016
Proceedings
Governance
Transparency
ABOD3: A graphical visualisation and real-time debugging tool for bod agents
Andreas Theodorou
In Proceedings of the EUCognition Meeeting on Cognitive Robot Architectures, 2016
Current software for AI development requires the use of programming languages to develop intelligent agents. This can be disadvantageous for AI designers, as their work needs to be debugged and treated as a generic piece of software code. Moreover, such approaches are designed for experts; often requiring a steep initial learning curve, as they are tailored for programmers. This can be also disadvantageous for implementing transparency to agents, an important ethical consideration [1], [2], as additional work is needed to expose and represent information to end users. We are working towards the development of a new editor, ABOD3. It allows the graphical visualisation of Behaviour Oriented Design based plans [3], including its two major derivatives: Parallel-rooted, Ordered Slip-stack Hierarchical (POSH) and Instinct [4]. The new editor is designed to allow not only the development of reactive plans, but also to debug such plans in real time to reduce the time required to develop an agent. This allows the development and testing of plans from a same application.
@inproceedings{Theodorou2016EUCog,author={Theodorou, Andreas},booktitle={Proceedings of the EUCognition Meeeting on Cognitive Robot Architectures},issn={16130073},pages={60--61},title={{ABOD3: A graphical visualisation and real-time debugging tool for bod agents}},volume={1855},year={2016},url={https://ceur-ws.org/Vol-1855/EUCognition_2016_Part19.pdf},}
Proceedings
H-AI Int.
Transparency
What Does the Robot Think? Transparency as a Fundamental Design Requirement for Intelligent Systems
Robert H. Wortham, Andreas Theodorou, and Joanna J. Bryson
In IJCAI 2016 Ethics for Artificial Intelligence Workshop, 2016
Deciphering the behaviour of intelligent others is a fundamental characteristic of our own intelligence. As we interact with complex intelligent artefacts, humans inevitably construct mental models to understand and predict their behaviour. If these models are incorrect or inadequate, we run the risk of self deception or even harm. This paper reports progress on a programme of work investigating approaches for implementing robot transparency, and the effects of these approaches on utility, trust and the perception of agency. Preliminary findings indicate that building transparency into robot action selection can help users build a more accurate understanding of the robot.
@inproceedings{WorthamEtAl2016IJCAI,author={Wortham, Robert H. and Theodorou, Andreas and Bryson, Joanna J.},booktitle={IJCAI 2016 Ethics for Artificial Intelligence Workshop},title={{What Does the Robot Think? Transparency as a Fundamental Design Requirement for Intelligent Systems}},url={https://researchportal.bath.ac.uk/files/145736287/WorthamTheodorouBryson_EFAI16.pdf},year={2016},address={New York, NY, US},}
