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Transport makes an impact across SDGs, encompassing climate change, health, inequality and sustainability. It is also an area in which individuals are able to make decisions which have potential to collectively contribute to significant and wide-ranging benefits. Governments and authorities need citizens to make changes towards adopting sustainable transport behaviours and behaviour change interventions are being used as tools to foster changes in travel choices, towards more sustainable modes. Blockchain technology has the potential to bring new levels of scale to transport behaviour change interventions, but a rigorous approach to token design is required. This paper uses a survey of research projects and use cases to analyse current applications of blockchain technology in transport behaviour change interventions, and identifies barriers and limitations to achieving targeted change at scale. The paper draws upon these findings to outline a research agenda that brings a focus on correlating specific Behaviour Change Techniques (BCTs) to token design, and defines processes for standardising token designs in behaviour change tools. The paper further outlines architecture and operational considerations for blockchain-based platforms in behaviour change interventions, such that design choices do not compromise opportunities or wider environmental goals.

Self-sovereign Identity promises to give users control of their own data, and has the potential to foster advancements in terms of personal data privacy. Self-sovereign concepts can also be applied to other entities, such as datasets and devices. Systems adopting this paradigm will be decentralised, with messages passing between multiple actors, both human and representing other entities, in order to issue and request credentials necessary to meet individual and collective goals. Such systems are complex, and build upon social and technical interactions and behaviours. Modelling self-sovereign identity systems seeks to provide stakeholders and software architects with tools to enable them to communicate effectively, and lead to effective and well-regarded system designs and implementations. This paper draws upon research from Actor-based Modelling to guide a way forward in modelling self-sovereign systems, and reports early success in utilising the iStar 2.0 framework to provide a representation of a birth registration case study.

In order to increase the value of scientific datasets and improve research outcomes, it is important that only trustworthy data is used. This paper presents mechanisms by which scientists and the organisations they represent can certify the authenticity of characteristics and provenance of any datasets they publish so that secondary users can inspect and gain confidence in the qualities of data they source. By drawing on data models and protocols used to provide self-sovereign ownership of identity and personal data to individuals, we conclude that providing self-sovereignty to data assets offers a viable approach for institutions to certify qualities of their datasets in a cryptography secure manner, and enables secondary data users to efficiently perform verification of the authenticity of such certifications. By building upon emerging standards for decentralized identification and cryptographically verifiable credentials, we envisage an infrastructure of tools being developed to foster adoption of metadata certification schemes, and improving the quality of information provided in support of shared data assets., Submitted to 12th International Workshop on Science Gateways (IWSG 2020), 10-12 June 2020

Increased adoption and deployment of machine learning (ML) models into business, healthcare and other organisational processes, will result in a growing disconnect between the engineers and researchers who developed the models and the model's users and other stakeholders, such as regulators or auditors. This disconnect is inevitable, as models begin to be used over a number of years or are shared among third parties through user communities or via commercial marketplaces, and it will become increasingly difficult for users to maintain ongoing insight into the suitability of the parties who created the model, or the data that was used to train it. This could become problematic, particularly where regulations change and once-acceptable standards become outdated, or where data sources are discredited, perhaps judged to be biased or corrupted, either deliberately or unwittingly. In this paper we present a method for arriving at a quantifiable metric capable of ranking the transparency of the process pipelines used to generate ML models and other data assets, such that users, auditors and other stakeholders can gain confidence that they will be able to validate and trust the data sources and human contributors in the systems that they rely on for their business operations. The methodology for calculating the transparency metric, and the type of criteria that could be used to make judgements on the visibility of contributions to systems are explained and illustrated through an example scenario.

Organisations are increasingly open to scrutiny, and need to be able to prove that they operate in a fair and ethical way. Accountability should extend to the production and use of the data and knowledge assets used in AI systems, as it would for any raw material or process used in production of physical goods. This paper considers collective intelligence, comprising data and knowledge generated by crowd-sourced workforces, which can be used as core components of AI systems. A proposal is made for the development of a supply chain model for tracking the creation and use of crowdsourced collective intelligence assets, with a blockchain based decentralised architecture identified as an appropriate means of providing validation, accountability and fairness., Comment: Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, USA

Researchers and scientists use aggregations of data from a diverse combination of sources, including partners, open data providers and commercial data suppliers. As the complexity of such data ecosystems increases, and in turn leads to the generation of new reusable assets, it becomes ever more difficult to track data usage, and to maintain a clear view on where data in a system has originated and makes onward contributions. Reliable traceability on data usage is needed for accountability, both in demonstrating the right to use data, and having assurance that the data is as it is claimed to be. Society is demanding more accountability in data-driven and artificial intelligence systems deployed and used commercially and in the public sector. This paper introduces the conceptual design of a model for data traceability based on a Bill of Materials scheme, widely used for supply chain traceability in manufacturing industries, and presents details of the architecture and implementation of a gateway built upon the model. Use of the gateway is illustrated through a case study, which demonstrates how data and artifacts used in an experiment would be defined and instantiated to achieve the desired traceability goals, and how blockchain technology can facilitate accurate recordings of transactions between contributors., Comment: arXiv admin note: text overlap with arXiv:1904.03045