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Welcome to Vivek Nallur’s (mostly) academic site on the web. I am an academic who is interested in machine ethics, complex systems, emergence,  and decentralized mechanisms of adaptation.

I’m very interested in complex self-adaptive systems, engineering emergent feedback loops, predicting and controlling emergence in humano-tech systems (where technical systems interact heavily with human desires/abilities), engineering robust systems from non-robust parts. If you’re interested in collaborating, or just want to chat about a specific topic, get in touch.

Projects I’m Involved In

  –   Crop Optimisation through Sensing Understanding and Visualization – Digital, precision agriculture and crop science

COMBAT   –  COvid-19 Modelling through Agent-Based Techniques

 

Professional Affiliations/Service

IEEE P7008 Working Group on Standard for Ethically Driven Nudging for Robotic, Intelligent and Autonomous Systems

ELSI Panel Member, Open Ambient Assisted Living – The OpenAAL project targets the fast co-creation of scalable and affordable solutions to support the care of vulnerable people

 

PhD Students

One of the privileges of being an academic is that I get to work with wonderful PhD students. These are mine (in order of starting):

Harshani Nagahamulla (2019): Harshani is a part of the CONSUS project and her work focusses on intelligent decision support with a focus on providing counter-factual analysis (what-if/what-if-not scenarios)

Rajitha Ramanayake (2020): Rajitha is a part of the Machine Ethics Research Group. He has started on his PhD very recently, and is focussed on investigating the creation of ethical models in autonomous agents

Labhaoise NíFhaoláin (2020): Labhaoise is also a part of the Machine Ethics Research Group. Labhaoise is funded by ML-Labs and her research focusses on Regulation and Governance in Trustworthy AI

Recent Publications

  • Assessing the Appetite for Trustworthiness and the Regulation of Artificial Intelligence in Europe (online version) Proceedings of the 28th Irish Conference on Artificial Intelligence and Cognitive Science. Vol:2771. Pages: 133-144.
  • Landscape of Machine Implemented Ethics (preprint) (online version via Springer Nature Sharedit) Journal of Science and Engineering Ethics. DOI: 10.1007/s11948-020-00236-y
  • “EHLO WORLD” – Checking if your conversational AI knows right from wrong (preprint) Accepted at SoCAI, AISB (postponed due to Covid-19)

Recent Talks

  • AI, Society & Media – March’2020 Dublin City University, Dublin
  • Intelligence & Ethics in Machines: Utopia or Dystopia – August’2019 CERN, Geneva
  • Machine Ethics Landscape – March’2019 University of Helsinki, Helsinki

Machine Ethics

Can machines can be programmed to be ethical? Answering this question requires interrogating ourselves to understand what ethics are, and how they develop. Computer Science is not well-positioned to answer these critical questions by itself, and therefore needs to collaborate with other disciplines, such as philosophy, sociology, law and even literature! Depending on how we collectively approach the problem will determine if a machine can be programmed to make ethical choices and adapt to evolving situations, or if it can only be programmed to follow specific rules. Increasingly computers are taking on roles where they might have to prioritize actions based on ethical judgements, for example care robots. Machine Ethics explores if human intervention will always be required in order to ensure the machine’s ethical behaviour, or if an ethical framework can be designed and implemented in a way that is socially acceptable. The Machine Ethics Research Group has organized two inter-disciplinary workshops, on Implementing Machine Ethics which were well-attended and resulted in robust discussion across multiple disciplines.

Multi-Agent Systems

Multi-Agent Systems (MAS) are my preferred tool for approaching problems in self-adaptation, complexity, emergence, etc. They lend themselves to extensive forms of experimentation: having all agents follow simple rules, implementing complex machine-learning algorithms, investigating the interplay of different algorithms being used at the same time, are all possible with relatively simple conceptual structures. Decoding the end result and teasing out the real factor(s) responsible for a particular behaviour is considerably more difficult :-). But, that’s a part of the fun!

To know more about my professional activities, take a look at my research and teaching pages. If you are interested in doing a PhD with me, take a look at the doing a PhD , go through the research section, and try to come up with a 1-page proposal that conveys the gist of your idea and how it dovetails with my research interests.