“Paul is amazing. Not only is he a true expert in his field, always so highly regarded & recommended by other leaders and everyone who knows him, but he is an absolute pleasure to work with. Other speakers, attendees and my own team/colleagues onsite always comment on how wonderful Paul is. He always delivers insightful, truly thought-provoking, and cutting-edge material at conferences and you can rely on him to come to your rescue in a panel on just about any topic within reason. He has been a cornerstone of our Responsible AI & Generative AI events, and they really wouldn't be the same without him.”
About
As Head of Responsible AI and AI Strategy at NatWest Banking group, I lead the…
Services
Activity
-
We’re excited to share that our amazing Managing Director, Jenna Owen, has been nominated for Inspirational Woman of the Year with the Women's…
We’re excited to share that our amazing Managing Director, Jenna Owen, has been nominated for Inspirational Woman of the Year with the Women's…
Liked by Dr Paul Dongha
-
Looking forward to reading “Governing the Machine” written by Ray Eitel-Porter Dr Paul Dongha and Miriam Vogel which launches this week from…
Looking forward to reading “Governing the Machine” written by Ray Eitel-Porter Dr Paul Dongha and Miriam Vogel which launches this week from…
Liked by Dr Paul Dongha
-
Great to discuss the latest trends in AI and financial services with Tom Mackenzie earlier this month, drawing on insights from the latest Evident AI…
Great to discuss the latest trends in AI and financial services with Tom Mackenzie earlier this month, drawing on insights from the latest Evident AI…
Liked by Dr Paul Dongha
Experience
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Education
-
The University of Manchester
-
-
Created a theory for the behaviour of goal-directed AI agents, and a corresponding mathematical model (based on a branching-time temporal logic) to derive properties of behaviour.
The model is based upon an economic theory of human rationality, and demonstrated how AI agents could reason and plan to achieve goals without some of the flaws inherent in human behaviour. The model demonstrated the conditions under which AI agents should either persist or refrain from pursuing their goals…Created a theory for the behaviour of goal-directed AI agents, and a corresponding mathematical model (based on a branching-time temporal logic) to derive properties of behaviour.
The model is based upon an economic theory of human rationality, and demonstrated how AI agents could reason and plan to achieve goals without some of the flaws inherent in human behaviour. The model demonstrated the conditions under which AI agents should either persist or refrain from pursuing their goals based on a trade-off between sunk costs versus future rewards.
See: https://uomlibrary.access.preservica.com/uncategorized/IO_77b74cc1-92c6-48f9-9468-dd728cb88b93
Published a number of peer-reviewed research papers. -
-
-
-
-
-
Licenses & Certifications
-
-
-
-
-
Machine Learning for Trading Specialization
Google Cloud, New York Institute of Finance
-
-
Fundamentals of Reinforcement Learning
University of Alberta, Alberta Machine Intelligence Institute
-
-
-
Volunteer Experience
-
Student Mentor
Credit Suisse
- 8 months
Children
Mentoring to talented high school students to support them through their A level studies and provide guidance for University admission and student life. The aim is to support young students from disadvantaged backgrounds, who despite their poor upbringing, show aptitude and talent.
-
Case worker Mentor
St Giles Trust
- 1 year 7 months
Social Services
Mentoring adults involved in working with repeat offenders, often victims of drug related crime. Providing career support and guidance to case workers who are considering a more formal career option.
Publications
-
A Theory of Rational Commitment for AI Agents
In DRUMS Windsor Workshop on Practical Reasoning and Rationality, Windsor, UK. Feb 1996
-
Computational Support for the Management of the Social Process within Organisational Teams
Towards the Intelligent Organisation, Springer Verlag CSCW Series
-
Cooperative Requirements Capture
Proceedings of the 1993 JFIT Technical Conference, DTI Publication.
-
Cooperative Requirements Capture: Prototype Evaluation
Chapter in Computer Support for Co-operative Work, John Wiley & Sons.pp169-195., 1994
-
Rational Commitment in Resource Bounded A.I. Agents
ModelAge’96 Workshop on Formal Models of Agents. Sesimbra, Portugal, Jan 1996
-
Rationality In Commitment
In AAAI Fall Symposium on Rational Agency; Concepts, Theories, Architecture & Applications, MIT, Cambridge, Mass, Nov 1995
-
Toward a Formal Model of Commitment for Resource Bounded Agents
In Intelligent Agents: Theories, Architecture and Languages. Proceedings of the ECAI’94 Workshop, volume 890 of Lecture Notes in AI. Springer-Verlag
Recommendations received
21 people have recommended Dr Paul
Join now to viewMore activity by Dr Paul
-
Really looking forward to the launch (http://bit.ly/4hnaS7s) of this foundational book on Wednesday at NatWest. At NatWest Group we are proud to be…
Really looking forward to the launch (http://bit.ly/4hnaS7s) of this foundational book on Wednesday at NatWest. At NatWest Group we are proud to be…
Liked by Dr Paul Dongha
-
Thank you Aparna Saroagi and then IIT Delhi Alumni Association for allowing me to talk about my book and this important topic. I’m very grateful for…
Thank you Aparna Saroagi and then IIT Delhi Alumni Association for allowing me to talk about my book and this important topic. I’m very grateful for…
Shared by Dr Paul Dongha
Other similar profiles
Explore more posts
-
Mayank A.
MCP (Model Context Protocol) has emerged as the standard way for LLMs to connect to external tools, servers, and data sources. The protocol defines how those connections work - a shared schema for exposing resources and tool calls. That’s why it’s being adopted across OpenAI, Anthropic, LangGraph, CrewAI, Composio, and more. But here’s the reality: MCP only gives you the spec. If you’re building an app, you still need to: ▪️ tell your copilot which MCP servers to talk to ▪️ manage how responses flow into conversations ▪️ and build UI to make tool calls usable It works, but it’s a lot of wiring. That’s the gap CopilotKit🪁 (AG-UI)’s MCP Client closes. Check here: https://lnkd.in/dF9R7PrZ Point your copilot at an MCP server, and it automatically: ✔️ discovers tools and resources at runtime ✔️ makes them callable by the LLM like native actions ✔️ streams results directly into the conversation ✔️ renders them with built-in UI components Example: instead of hand-building a dashboard for SQL queries, your copilot can connect to a database MCP server, let the LLM generate queries, and then render the results in-app with a ready-made table component. No extra plumbing required. 👉 MCP defined the protocol. 👉 CopilotKit MCP Client makes it practical in your app. Setup takes one command: npx copilotkit@latest init -m MCP
85 Comments -
David R. Hardoon
This is what AI 'thinks' of my interview with Prof. Kevin Werbach 😅: "David Hardoon delivers a masterclass in Accountable AI! From Singapore’s FEAT principles to Standard Chartered’s AI innovation, he proves governance is a catalyst for trust and progress in fintech. A must-watch for anyone shaping AI’s future—accountability isn’t a burden, it’s a competitive edge! 🚀" I think AI is being far too kind, or maybe its just AI particularly agreeing with the core theme that one can't outsource accountability in AI to AI 😁 Key Takeaways: Justify, Don’t Blindly Trust: AI decisions must be rigorously interrogated to ensure fairness and transparency. Governance Drives Innovation: Embedding accountability in AI processes boosts resilience and value, not bureaucracy. GenAI’s Dual Edge: Harness its potential to fight scams, but only with robust, human-led oversight. Global Lessons: Singapore’s collaborative AI policies (like Veritas) set a benchmark for balancing regulation and innovation. https://lnkd.in/gUVAyrWx
8 Comments -
Denis Burakov
Are you interested in learning how the machine learning scorecard development process differs from traditional scorecards? In 2023, I wrote a paper outlining a blueprint showing how constrained tree-based models can match and outperform traditional scorecard methods in discriminatory power and calibration accuracy, without losing interpretability. As many leading institutions now transition toward explainable ML frameworks, this work provides a blueprint for the responsible integration of advanced methods within regulated environments. 👉 GitHub repo: https://lnkd.in/dVjjSfeJ 🤝 Special thanks go to Paul Edwards, Guillermo Navas Palencia, PhD, and Fabiana Clemente for their valuable feedback during the writing process. #CreditRiskModeling #DataScience #xAI #MachineLearning #ResponsibleAI
4 Comments
Explore collaborative articles
We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
Explore More