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DIS Research Seminars



Archive for the 'Knowledge Information and Data Science' Category

Research Talk by Ann Borda

By Antonios Bikakis, on 21 March 2024

The Imitation Game: Advancing guidance on AI ethics and governance in practice

The talk was delivered on 19 March 2024 by Dr. Ann Borda, an Ethics Fellow in the Public Policy Programme at The Alan Turing Institute. and an Honorary Senior Research Associate in the Department of Information Studies, as part of the DIS research seminars series.

AI is having a significant impact on public policies and services around the world, but government use of AI has a steep learning curve, and the purpose of AI within government and public sector contexts present numerous challenges. To help UK civil servants learn about and explore AI in an effective and ethical way, the Alan Turing Institute’s Public Policy Programme developed a series of workbooks that promotes the understanding of the UK Government’s official Public Sector Guidance on AI Ethics and Safety published in 2019, in collaboration with the UK’s Office for Artificial Intelligence and the Government Digital Service. Co-developed with public sector groups, this guidance outlines how AI project teams in the public sector can put ethical values and practical principles into practice across the AI project lifecycle, ensuring that AI is produced and used ethically, safely, and responsibly. Complementary to this initiative, the Turing is growing a societal Readiness, Skills, and Knowledge platform which further includes guidance on AI ethics and skills tracks for early career researchers and community audiences, supported by a publicly accessible repository of resources for those seeking to explore and apply the ethical and responsible use of data. These initiatives, including the underlying ethical values and frameworks which underpin them, are the key focus of this seminar. Challenges of the evolving AI landscape are also touched on, particularly in the development and deployment of guidance for multiple stakeholders.

Research Talk by Abul Hasan

By Antonios Bikakis, on 25 January 2024

Incorporating Dictionaries into a Neural Network Architecture to Extract COVID-19 Medical Concepts From Social Media

The talk was delivered on 24 January 2024 by Dr. Abul Hasan, a postdoctoral research fellow at the UCL Institute of Health Informatics, as part of the DIS research seminars series.

We investigate the potential benefit of incorporating dictionary information into a neural network architecture for natural language processing. In particular, we make use of this architecture to extract several concepts related to COVID-19 from an on-line medical forum. We use a sample from the forum to manually curate one dictionary for each concept. In addition, we use MetaMap, which is a tool for extracting biomedical concepts, to identify a small number of semantic concepts. For a supervised concept extraction task on the forum data, our best model achieved a macro F1 score of 90%. A major difficulty in medical concept extraction is obtaining labelled data from which to build supervised models. We investigate the utility of our models to transfer to data derived from a different source in two ways. First for producing labels via weak learning and second to perform concept extraction. The dataset we use in this case comprises COVID-19 related tweets and we achieve an F1 score 81% for symptom concept extraction trained on weakly labelled data. The utility of our dictionaries is compared with a COVID-19 symptom dictionary that was constructed directly from Twitter. Further experiments that incorporate BERT and a COVID-19 version of BERTweet demonstrate that the dictionaries provide a commensurate result. Our results show that incorporating small domain dictionaries to deep learning models can improve concept extraction tasks. Moreover, models built using dictionaries generalize well and are transferable to different datasets on a similar task.

Research Talk by Anthony Hunter

By Antonios Bikakis, on 19 January 2024

Towards Computational Persuasion for Behaviour Change Applications.

The talk was delivered on 22 November 2024 by Prof. Anthony Hunter, Professor of Artificial Intelligence in the Department of Computer Science, University College London, as part of the DIS research seminars series.

The aim of behaviour change is to help people to change aspects of their behaviour for the better (e.g., to decrease calorie intake, to drink in moderation, to take more exercise, to complete a course of antibiotics once started, etc.). Recent developments in computational modelling of argument (a subfield of AI) are leading to technology for persuasion that can potentially be harnessed in behaviour change applications. Using this technology, a software system and a user can exchange arguments in a dialogue. So the system gains information about the user’s perspective, provides arguments to fill gaps in the user’s knowledge, and attempts to overturn misconceptions held by the user. Our work has focused on modelling the beliefs and concerns of the user, and harnessing these to make the best choices of move during the dialogue for persuading the user to change their behaviour. We have also been investigating how we can harness recent developments in large language models to provide a natural language interface to this technology. In this talk, I will provide an overview of our approach together with some promising preliminary results with participants.