Αndreas Theodorou

Expert in AI R&D with over 10 years of experience at the intersection of technical research and global policy. I excel at knowledge transfer: bridging the gap between state-of-the-art research and real-world implementations. My goal is to help organisations generate value through AI solutions that are ethically grounded and legally resilient.

My research focuses on answering two connnected research questions: 1. how to best design and develop transparent intelligent systems that exhibit desired normative behaviours, while also ensuring their high performance; and 2. how to govern AI systems in such a way that we guarantee meaningful human control at all times.

To answer the above questions, I have been managing and participating in projects related to responsible AI with particular focus on governance and safety. My expertise is recognized by my contributions to global policy: from technical standards (IEEE 70xx, ISO/IEC JTC1/SC42) to the EU AI Act.

Recent publications

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

Abs DOI Bib

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}, }
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

ACM Computing Surveys, Apr 2024

Abs DOI Bib Paper

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}, }