My main research interests are focused on complex socio-technical systems underpinned by novel technologies such as IoT, AI, and Blockchains. More specifically I focus on issues related to transparency, accountability, compliance, and trustworthiness within such systems.
Keywords: Provenance, Semantic Web, Intelligent Systems, Agri-food, Internet of Things (IoT), Accountable AI Systems, Healthcare
Enhancing Agri-Food Transparent Sustainability - EATS
JAN 2022 - present
The UK has a legally binding target of 'net zero' greenhouse gas (GHG) emissions for 2050 (Scotland, 2045) and the Food and Drink sector has a vitally important role to play in helping to achieve this. This must be done while also improving nutrition, protection of ecosystems, reduced risks to soil, water and air quality. Delivery against these ambitious targets will require a range of measures to be adopted across the agri-food supply chain - not just primary producers but also processors, retailers and ultimately consumers.
Over the last few decades rapid advances in processes to collect, monitor, disclose, and disseminate information (broadly classified under the concept of 'transparency') have contributed towards the development of entirely new modes of environmental monitoring and governance for supply chains. Unfortunately, existing approaches often suffer from limitations in terms of collection and dissemination of data; over-simplification of supply chains; power dynamics influencing information inclusion/exclusion decisions; and potentially perverse outcomes regarding how the information is used, by whom and to what effect.
Given these issues, we need to consider how best to capture information about supply chains in order to document existing sustainability practices in sufficient detail; this is necessary to not only support monitoring and reporting needs of all stakeholders, but also to promote additional pro-environmental behaviours and even re-configuration of the supply chain.
Our vision is built around an actionable information ecosystem whose purpose is to deliver transparent sustainability - realised via three pillars that we refer to as: SEE-SHARE-ACT. The first of these encompasses the role of sensors and carbon reporting tools in capturing data about agri-food processes (SEE); the second is a trusted digital platform able to manage sustainability data and report it across supply chain actors(SHARE); the third is the use of data-analytics and machine learning to support decision-making and action (ACT).
But what would a trusted infrastructure for transparent sustainability look like, and how would it be framed by (and operate within) its wider environmental, social and economic context?
Also - how would such a framework go beyond simply documenting the elements of a supply chain (actors, processes, inputs, outputs) to enable a holistic approach to monitoring, pro-environmental decision-making and action?
We have assembled an interdisciplinary team of academics and user organisations spanning the livestock, soft-fruit and brewing sectors to investigate transparent sustainability. Together we will explore the following questions:
- What datasets, indicators and decision-making processes are relevant to the different actors participating in supply chains to realize sustainable food futures (in the DE)?
- How do we formulate appropriate vocabularies with which to characterise sustainability practices, their context and rationale, and facilitate data capture and integration?
- Can we realize a provenance-based sustainability solution for supply chains, operating across a range of technologies and organisational boundaries, that is trusted and able to facilitate pro-environmental decision-making and action?
- How do we exploit sustainability data assets and ML/AI technologies to inform decision making towards net-zero, resulting in demonstrable changes to practice and behaviour?
Answers to these (and the many other questions that will certainly emerge) will lead us to develop prototype solutions that will be evaluated with project partners. Our ambition is to create a means by which farmers and other food and drink supply chain stakeholders can create a more sustainable economy built upon trusted data regarding the lifecycle history of products for enhanced environmental and product safety in (therefore more resilient) food supply chains.
SARA: Semi-Automated Risk Assessment of Data Provenance and Clinical Free-Text in TREs
JAN 2023 - present
Data is transforming health and social care, enabling life-changing discoveries, advancing healthcare services and improving lives. Yet health data providers face challenges in extracting and linking this complex data and safeguarding its safe release for research.
Risk assessments are key to ensuring that providing researchers with access to data does not pose privacy risks – such as by containing identifiable information – and that people’s personal records are processed correctly. However, currently, these governance processes are ad-hoc, manual and time consuming, and may prohibit data release, ultimately limiting new health and social care innovation.
The SARA project will focus on delivering semi-automated tools to improve two areas of risk assessment and monitoring:
- data provenance (metadata that describes the origins, actions performed and agents involved in data creation and transformation) by improving the trustworthiness of how we bring data in and then process and link it to ensure it is compliant for research; and
- privacy assessment by minimising the risk of identifiable information in clinical free-text records (for example, GP letters and discharge summaries).
Open, reproducible analysis and reporting of data provenance for high-security health and administrative data
2021 - 2022
Many types of routinely-collected data from the NHS and other government agencies are available for research in the UK. To protect privacy, data governance law requires that only project-specific portions of the data be extracted, filtered and anonymised before release for research. Currently little information is provided to researchers on the methods used to produce their data. This lack of transparency results in an increased risk of propagating undetected error and leaves the resulting research difficult or impossible to evaluate and reproduce.
The project explored how data provenance can be applied in this context as means to document and assess the data linkage process in a Trusted Research Environment context.
IoFT Working Group: Data Sharing and Interoperability for Data Trusts in Agri-Food
Sep 2020 – June 2021
The working group investigated potential technological solutions to the challenges of data sharing within data trusts for the agri-food sector.
Jan 2020 – June 2022
The RAInS project aimed to realise processes by which AI systems can be made accountable, by developing an accountability fabric for use by a variety of stakeholders. The project used computational models of provenance – as a substrate for enabling trust; such a mechanism facilitates transparency and accountability by recording the processes, entities and agents associated with a system and its behaviours-supporting verification and compliance monitoring.
2016 – Dec 2020
Our vision for the Internet of Things is built around the concept of the TrustLens –a future computational infrastructure supporting the three pillars of a user-centred IoT ecosystem: empowerment – providing citizens with insight and meaningful control over their data, and means to access/use data for their own purposes; transparency – presenting individuals with understandable and relevant information on how their data is being acquired and used, and the associated risks and benefits; accountability – means to support principled and enforceable data use, combined with verifiable evidence that appropriate measures are being taken.
May 2019 – Mar 2020
Project descriptionA pilot project exploring the use of IoT, semantic technologies, and blockchains to monitor food delivery.
The Food Sentiment Observatory: Exploiting New Forms of Data to Help Inform Policy on Food Safety & Food Crime Risks
2017 – Aug 2018
The Food Sentiment Observatory is a 12-month pilot study funded by the UK’s Economic & Social Research Council (ESRC). The project is investigating the potential for new forms of data such as social media and other online content to be used as part of the government policy making and policy assessment process. Our team is a partnership between Computer Science researchers at the University of Aberdeen, and Food Standards Scotland – the government agency responsible for food policy across Scotland, including aspects of food safety, food crime and nutrition.
2016 – Dec 2017
Industry led project in collaboration with researchers from the University of Aberdeen. The focus of the project is to explore how the Internet of Things can act as a means of sharing transport data in an efficient and timely way, what the data privacy challenges are, and how the smart phone can deliver more effective means of travel personalisation. For more info follow the project url.
FOOD SAFETY ASSURANCE: COMBINING PROVENANCE & THE INTERNET OF THINGS
Dec 2015 – Mar 2016
This project explores the potential of lightweight, low-cost sensing in a commercial kitchen as a means to aid understanding of food safety compliance issues.
Jan 2015 – Oct 2015
Exploring how social media updates can be combined with existing (open) datasets to further enhance real-time passenger information. There has been a rapid growth in the use of social media in public transport in recent years. Public transport service providers currently communicate with customers via social networks such as Twitter. This benefits transport operators as they can gain insight into customer attitudes and behaviours. It also enables passengers to be alerted to delays and disruption at an early stage through the existing channels they use.
Informed Rural Passenger
Jul 2011 – Oct 2014
The Informed Rural Passenger Project is exploring the creation of a transport information ecosystem with passengers at its centre as both consumers and suppliers of information.
- MR ANDY LI
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Model pruning enables localized and efficient federated learning for yield forecasting and data sharingExpert Systems with Applications, vol. 242, pp. 1-12Contributions to Journals: Articles
TEC: Transparent Emissions Calculation ToolkitChapters in Books, Reports and Conference Proceedings: Conference Proceedings
Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data SharingWorking Papers: Preprint Papers
Designing Physical and Virtual Walkshop Methods for Speculative Internet of Things ResearchDIGICOM 2022: Advances in Design and Digital Communication III. Martins, N., Brandao, D. (eds.). Springer Nature, pp. 392-405, 14 pagesChapters in Books, Reports and Conference Proceedings: Chapters
Using Knowledge Graphs to Unlock Practical Collection, Integration, and Audit of AI Accountability InformationIEEE Access, vol. 10, pp. 74383 - 74411Contributions to Journals: Articles
Participatory IoT Policies: A Case Study of Designing Governance at a Local LevelChapters in Books, Reports and Conference Proceedings: Conference Proceedings
Prototyping an IoT Transparency Toolkit to support Communication, Governance and Policy in the Smart CityThe Design Journal, vol. 25, no. 3, pp. 459-480Contributions to Journals: Articles
From transparency to accountability of intelligent systems: Moving beyond aspirationsData & Policy, vol. 4, e7Contributions to Journals: Articles
The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food SectorComputers and Electronics in Agriculture, vol. 193, 106648Contributions to Journals: Articles
FlyTrap: A Blockchain-based Proxy for Authorisation and Audit of MQTT ConnectionsChapters in Books, Reports and Conference Proceedings: Conference Proceedings