TAI-RM 2022

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).

Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

Mihaela is personally credited as inventor on 35 USA patents, (the majority of which are listed here), many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.

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Xiaowei Huang is Professor of Computer Science, and Director of the Trustworthy Autonomous Cyber-Physical Systems lab, at the University of Liverpool, UK. His research is concerned with the development of automated verification techniques that ensure the correctness and reliability of intelligent systems. He is leading the research direction on the verification and validation of deep neural networks and co-chairing the AAAI and IJCAI workshop series on Artificial Intelligence Safety. His research has been supported by Dstl (Ministry of Defence, UK), EPSRC, and the EU.

Stefan Larsson is a senior lecturer and Associate Professor in Technology and Social Change at Lund University, Sweden, Department of Technology and Society. He is a lawyer (LLM) and socio-legal researcher that holds a PhD in Sociology of Law as well as a PhD in Spatial Planning. His multidisciplinary research group focuses on issues of trust and transparency on digital, data-driven markets, and the socio-legal impact of autonomous and AI-driven technologies.

Luis Magdalena is Full Professor of Computer Science and Artificial Intelligence at Universidad Politécnica de Madrid, Spain. He is a Fellow of the International Fuzzy Systems Association and an Honorary Member of the European Society for Fuzzy Logic and Technology. He is Vice-president for Technical Activities of the IEEE Computational Intelligence Society and has been Director General of the European Centre for Soft Computing. Prof. Magdalena has recently coauthored the book “Explainable Fuzzy Systems - Paving the Way from Interpretable Fuzzy Systems to Explainable AI Systems” (Springer. 2021).

Raymond has research interests within human-computer systems which has mainly included applications of biomedical and healthcare informatics (digital health). Raymond's work has involved the application of human-computer interaction and data science/machine learning techniques to healthcare research. His work has involved health data analytics as well as the modelling, processing and visualisation of medical data to enhance clinical decision-making, including the creation of interactive decision support systems. He also has research interests in computerised simulation-based training in healthcare, usability engineering/UX data analysis methods to improve medical devices, eye-gaze analytics in clinical decision making, and is also involved in designing and evaluating digital health and wellbeing interventions. Raymond has over 350 research outputs and has chaired/co-chaired a number of conferences: 1) 32nd International BCS Human Computer Interaction conference, 2) the 45th/ 46th Annual Conference of the International Society for Computerised Electrocardiology, and 3) the 31st Annual European Conference of Cognitive Ergonomics).

Dr Vaishak Belle is a Chancellor's Fellow and Faculty at the School of Informatics, University of Edinburgh, and an Alan Turing Institute Faculty Fellow. He directs a research lab on artificial intelligence, specialising in the unification of logic and learning. He has co-authored over 60 scientific articles on AI, and along with his co-authors, he has won four best paper awards, including the Microsoft best paper award. Recently, he has also consulted with major banks on explainable AI.

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Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in diverse areas such as computational biology, law and finance. However, such a highly positive impact is coupled with significant challenges: how do we understand the decisions suggested by these systems in order that we can trust them?

In the first part of the talk, we survey and distill results and observations from the literature, focusing primarily on machine learning models. We build a narrative around a putative data scientist, and discuss how she might go about explaining her models by asking the right questions.

In the second part of the talk, we look at recent results on inherently transparent models that are also often computational attractive, mainly stemming from the integration of logic & learning.

Haitham leads the reinforcement learning team at Huawei technologies Research & Development UK and is an Honorary Lecturer at UCL. Prior to Huawei, Haitham led the reinforcement learning and tuneable AI team at PROWLER.io, where he contributed to their technology in finance and logistics. Prior to joining PROWLER.io, Haitham was an Assistant Professor in the Computer Science Department at the American University of Beirut (AUB). Before joining the AUB, Haitham was a postdoctoral research associate in the Department of Operational Research and Financial Engineering (ORFE) at Princeton University. Prior to Princeton, he conducted researcher in lifelong machine learning while being employed as a postdoctoral researcher at the University of Pennsylvania. Being a former member of the General Robotics Automation Sensing and Perception (GRASP) lab, he also contributed to the application of machine learning to robotics. His primary research interests lie in the field of statistical machine learning and artificial intelligence, focusing on Bayesian optimisation, probabilistic modelling and reinforcement learning. He is also interested in learning using massive amounts of data over extended time horizons - a property common to "Big-Data" problems. His research also spans different areas of control theory and nonlinear dynamical systems, as well as social networks and distributed optimization.

Gerlinde Weger is a business consultant with over 27 years’ experience in advanced ICT (Information-Technology Communications). She is part of the core team creating the ethical criteria of the IEEE ethical certification program for AIS, CertifiAIEd and is co-chair of 3 of the normative sections of the IEEE algorithmic bias considerations standard as well as a member of the organizational governance of AI and the digital literacy standards that are in development.

Being often at the forefront of technological innovation, Gerlinde led the creation of the first North American data-driven financial service web-site, the first North American financial and health insurance portal with customer profiling and phase 1 of the first virtual bank in Europe.

IEEE P7003 is the development of a global standard that integrates the consideration of algorithmic bias in the life-cycle of an algorithmic system. The standard is relevant to individuals and organizations who follow sociotechnical best practices in the design of AI/S and have a commitment towards the betterment of humanity.

The standard considers the aspects of bias as related to the stakeholders and the consideration of how the algorithmic system, including data is impacting them and the risks associated. The model evaluates from the perspective that a system bias may be intentional so as to equalize ethical dimensions across stakeholder groups.

P7003 is a model that aligns with existing engineering processes and supports requirements for ethical AI/S certification through CertifAIEd. Ethical Bias requirements are an integral component of the certification. Receiving the CertifAIEd mark affirms an organization's commitment to upholding human values, dignity, and well-being, and to respecting, protecting and preserving fundamental human rights.

Ben Bland serves as Chair of the Institute of Electrical and Electronics Engineers (IEEE) P7014 working group, developing a global standard for the ethics of empathic technology. He is also a steering committee member for the IEEE Society on Social Implications of Technology.

Ben works at the nexus of innovation, operations and product strategies, technology ethics, and digital media. For five years he led operations and marketing at Sensum - a startup developing AI-based solutions that measure and respond to human emotions from a wide range of biometric data types, in realistic and challenging environments, for some of the world's biggest brands.

Ben's background covers a broad range of digital ventures, from online marketing, e-commerce and content production, through digital strategy consulting, to wider tech startup consulting & entrepreneurship. He has provided strategic consultation to dozens of startups, charities and industry organisations, has written and produced multimedia content for various platforms, and speaks on technology ethics and innovation around the world.

The Institute of Electrical and Electronics Engineers (IEEE) has taken on the challenge of publishing the first set of standards for AI ethics. Hear some of the lessons learned, and ongoing debates, from the moral maze of designing one of these standards. Ben Bland chairs the group writing the IEEE 7014 standard for ethics in empathic AI (e.g. affective computing, emotion AI, etc.). Ben provides a candid overview of the hopes and challenges of this ambitious but thorny project.

Dr Clara Crivellaro is Senior Research Fellow in Digital Local Democracy at Newcastle University's School of Computing Open Lab. Her research sits at the intersection of civic technology, social justice, and social innovation. Her work explores how the design of tools and participatory design processes can help co-create digital social innovations that better responds to societal issues and communities' concerns and aspirations. She was Principal Investigator of Not-Equal, the EPSRC Network+ on Social Justice through the Digital Economy (2018-2022) and Co-Investigator of the H2020 Generative European Urban Commons, gE.CO(2019-2022). She is Co-Investigator on the EPSRC Centre for Digital Citizens and a member of OLAthens, a non-for profit organization that supports social innovation in Greece.

Artificial intelligence and new and emergent technologies are transforming the service economy and social relationships. While there is potential for this technology to benefit societies, AI is also understood to be reproducing and exacerbating inequality and creating new forms of social division and exclusion. In this talk, I reflect on a UKRI network project on social justice and the digital economy and explore the benefits of foregrounding issues of structural inequality and taking holistic approaches to the design and configuration of AI and broader digital innovations in societies.

Giovanni Acampora is Full Professor in Artificial Intelligence and Quantum Computing at the University of Naples Federico II. Previously, he was Reader in Computational Intelligence (Sept. 2012 - June 2016) at the School of Science and Technology, Nottingham Trent University, Nottingham, U.K. From July 2011 to August 2012, he was in a Hoofddocent Tenure Track in Process Intelligence with the School of Industrial Engineering, Information Systems, Eindhoven University of Technology, Eindhoven, the Netherlands. He is the Chair of IEEE-SA 1855WG and he serves as an Editor in Chief of Springer Quantum Machine Intelligence, Associate Editor of Springer Soft Computing and Editorial Board Member of several international journals. In 2017, he acted as General Chair of IEEE International Conference on Fuzzy Systems, the top leading conference in fuzzy logic. He is member of the scientific board of the Interdepartmental Center for Advanced RObotics in Surgery (ICAROS). His main research interests include computational intelligence, fuzzy modeling, evolutionary computation, and ambient intelligence. Prof. Giovanni Acampora was a recipient of three prestigious awards, the IEEE-SA Emerging Technology Award in 2016, the 2019 Canada-Italy Innovation Award for Emerging Technologies and the IBM Quantum Experience Academic Research Program Award, as well as two best papers, the first one at the United Kingdom Workshop on Computational Intelligence, UKCI 2012 (Edinburgh, Scotland, UK), and the second one at the 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021).

Quantum computers can be a revolutionary devices to implement inference engines for fuzzy rule-based systems. The use of quantum mechanical principles can enable parallel execution of fuzzy rules and allow them to be used efficiently in complex contexts such as distributed and big data environments. This talk introduces the very first quantum-based fuzzy inference engine that is capable of providing exponential acceleration in fuzzy rule execution compared to its classical counterpart, and allows a quantum computer to be programmed by fuzzy linguistic rules. The proposed inference engine was implemented using a quantum algorithm design scheme based on the oracle notion. This scheme allows the modelling of a fuzzy rule-based system as a Boolean function, the oracle, which is able to reconstruct the relationships between the antecedent and consequent parts of fuzzy rules, and can be efficiently computed on a quantum computer. The proposed approach is used jointly with the standard IEEE 1855 to enable a transparent implementation of fuzzy systems on quantum computers.

Dr. Youcheng Sun is Lecturer in Cyber Security at The University of Manchester. Before joining Manchester, Youcheng was a Lecturer at Queen’s University Belfast and he was a postdoctoral researcher in the verification group at the University of Oxford. He is an expert in AI security and safety. In particular, he pioneered several techniques on the testing of complex intelligent software systems. Youcheng’s research has been supported by grants from Google, Dstl (Defence Science and Technology Laboratory, UK) and NCSC (National Cyber Security Centre, UK). In the past, Youcheng led the source code testing and verification work for two UK domestic airborne software projects, SECT-AIR and AUTOSAC. He was a member of the EU H2020 project SAFURE investigating safety and security assurance in the design of mixed-critical cyber-physical systems. Youcheng has a strong track record of publications on software engineering, formal verification, embedded systems, robotics and AI safety in top-tier academic conferences and journals, including IEEE S&P (Oakland), ICSE, ASE, CAV, TACAS, NeurIPS, ICCV, ECCV, IJCAI, ICRA, IROS, RTSS, ACM TECS, IEEE TR.

Deep learning models, especially those large-scale and high-performance ones, can be very costly to train, demanding a considerable amount of data and computational resources. As a result, deep learning models have become one of the most valuable assets in modern artificial intelligence. For instance, it can cost $1.6 million to train a BERT model on Wikipedia and Book corpora. It is thus of utmost importance to protect deep learning models from unauthorized duplication or reproduction. This talk will be about threats to deep learning copyright and corresponding defence techniques, especially the latest software testing method, on copyright protection of deep learning models.

Andrew Bolster is a technologist, an award winning researcher, community leader, and an occasional public speaker. As Head of Data Science at WhiteHat Security, he uses advanced data mining and machine learning techniques to provide adaptive cybersecurity for global brands at scale, and is building a community of practice around Data Science in Belfast. Bolster's academic works focused on the applications of Autonomous and Intelligent systems in defence and marine contexts, as well as emergent behaviours in distributed and decentralised systems, particularly focused on the application of social contagion theories of trust to make autonomous marine communication networks resilient to state-level interference.

He is a member of multiple AI Ethics groups and has lectured on related subjects, including recent seminars on AI explainability and the responsible use of machine learning in industry.

And when he's not teaching machines how to think, he is also a founding Director of Farset Labs, Northern Ireland's Hackerspace, and a co-organiser of the InfosecNI, PyBelfast, and DataArtNI Meetup groups.