Algorithms and Artificial Intelligence play a key role nowadays in many technological systems that control or affect various aspects of our lives. They optimise our driving routes every day according to traffic conditions; they decide whether our mortgage applications get approved; they even recommend us potential life partners. They work silently behind the scene without much of our notice, until they do not. Few of us would probably think much about it when our credit card application is approved in two seconds. Only when it is rejected, do we start to question the decision. Most of the time, the answers we get are not satisfactory, if we get any at all. The spread of such opaque automated decision-making in daily life has been driving the public demand for algorithmic accountability - the obligation to explain and justify automated decisions. The main concern is that it is not right for those algorithms, effectively black boxes, to take in our data and to make decisions affecting us in ways we do not understand. For this reason, the General Data Protection Regulation requires that we, as data subjects, be provided with "meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing." Likewise, consumers should be treated fairly when receiving financial services as per financial services regulations and algorithms should be free of discrimination as per data protection, equality and human rights laws. However, as laws and regulations do not prescribe how to meet such requirements, businesses are left with having to interpret those themselves, employing a variety of means, including reports, interactive websites, or even dedicated call centres, to provide explanations to their customers. Against this background, provenance, and specifically its standard PROV, describes how a piece of information or data was created and what influenced its production. Within recorded provenance trails, we can retrace automated decisions to provide answers to some questions, such as what data were used to support a decision, who or what organisation was responsible for the data, who else might have been impacted. While provenance information is structurally simple, provenance captured from automated systems, however, tends to be overwhelming for human consumption. In addition, simply making provenance available to a person does not necessarily constitute an explanation. It would need to be summarised and its essence extracted to be able to construct an explanation addressing a specific regulatory purpose. How we do this is unknown today. PLEAD brings together an interdisciplinary team of technologists, legal experts, commercial companies and public organisations to investigate how provenance can help explain the logic that underlies automated decision-making to the benefit of data subjects as well as help data controllers to demonstrate compliance with the law. In particular, we will identify various types of meaningful explanations for algorithmic decisions in relation to their purposes, categorise them against the legal requirements applicable to UK businesses relating to data protection, discrimination and financial services. Building on those, we will conceive explanation-generating algorithms that process, summarise and abstract provenance logged by automated decision-making pipelines. An Explanation Assistant tool will be created for data controllers to provision their applications with provenance-based explanations capabilities. Throughout the project, we will engage with partners, data subjects, data controllers, and regulators via interviews and user studies to ensure the explanations are fit for purpose and meaningful. As a result, explanations that are provenance-driven and legally-grounded will allow data subjects to place their trust in automated decisions, and will allow data controllers to ensure compliance with legal requirements placed on their organisations.

Gaps remain in our understanding of how different combinations of business, technology and relationship strategies influence the growth of small and medium-size enterprises (SMEs), especially in the context of networked interactions by SMEs at regional, extra-regional, and sectoral levels. In each new round of economic and technological development, these strategies evolve, highlighting the importance of tracking in close to real-time the nuances of emerging enterprise strategies. In this project, we will probe differential strategies for SME growth and the role of regional clustering in the growth of innovative companies, building on new and unobtrusive methods of web mining to gain timely information about enterprise developmental pathways. Three key research questions will be addressed: (1) What differentiates the business strategies, technology pathways, and relationships of innovative companies that stay in business and grow? (2) How does regional clustering influence the business strategies, technology pathways, and relationships of innovative companies that stay in business and grow? (3) What are the contributions of policy-induced resources in supporting innovative companies that stay in business and grow? These questions will be probed through a mixed-methods approach using both quantitative and qualitative data. We focus our research on the emerging green goods sector (GGS) - comprising firms producing outputs that benefit the environment or conserve natural resources, with an international comparative element involving the UK, the US, and China. We will identify 300 GGS companies in each country established during the time period 2004-2007, for a total study sample of 900 companies. We will apply a stratified sample selection procedure, to match the distribution of the UK GGS sector by broad product classes with the US and Chinese GGS sectors. We will then combine structured and unstructured data sources to track the origins, business, technology, and relationship strategies, and performance outcomes of these firms through to 2011. Structured data will include corporate databases, corporate patents and publication records. Unstructured data will be derived from new methods of web-scraping and data mining the current and archived web-sites of the sample enterprises. Survival analysis will indicate the pathway of firms from the founding period through to the current period. Hierarchical cluster analysis will be applied to explore differential business strategies by the three countries and by types of metropolitan location and product class. Multivariate regression will relationships between high growth (and other) outcomes and business strategies, technological approach, and the role of regional relationships and policy instruments, controlling for country and other factors. Insights from US and Chinese enterprise growth strategies will be compared with those of UK firms. In the subsequent phase, we will undertake case studies with selected UK enterprises, to test and refine propositions about differential strategies, regional and policy interactions, and outcomes. Interviews (60) will be conducted with high growth firms, stable firms, and other key informants in five UK metropolitan areas. The interviews will examine what differentiates the most successful firms, trace the use and benefit of technology-related and other programmes, and probe for wider policy-related locational attractiveness. The project will be led by the University Manchester in partnership with Experian UK, Georgia Institute of Technology (US), and Beijing Institute of Technology (China). An active dissemination and engagement programme will be pursued with the academic and non-academic worlds, including mechanisms for advanced training and outreach to users in the business sector, including start-up firms and business support programmes, to university and other technology transfer managers, and to local and national policymakers.

The NEO-DEM project will use non-standard data and novel methods to impact business efficiency, encourage community collaboration and provide scholarly insights into consumer behaviour. UK businesses can struggle in the developing world, despite excellent track records at home. The following reasons explain a good deal of this failure in retail, service and consumer oriented sectors: * It is not possible to directly transfer domestic business models into emerging economies due to cultural, infrastructural and behavioural differences. Companies need to generate new analytical and strategic models that identify the differing needs of customers based on an understanding of the novel behavioural and consumption patterns exhibited. * In the developed world consumer oriented businesses are increasingly data-driven. They rely on cross-referenced geo-demographic, socio-graphic, and psychographic data as well as transactional data (e.g. Tesco & Boots in the UK); their use is enmeshed within company strategy. In many countries this kind of data are incomplete or non-existent, their absence inhibits growth and means that targeting and resource use is sub-optimal. Replicating the kind of data that is readily available in the UK will often be impossible or expensive and impractical. Even when transactional data is forthcoming (e.g. Tesco Clubcard Malaysia) there is limited scope to cross-reference them with reliable geo-demographic data-sets and models that are taken for granted in the UK (e.g. Experian's Mosaic). Despite lagging behind in infrastructural developments, developing countries have experienced digital revolutions; providing a largely untapped opportunity to generate business intelligence. In 2010 of the 5 billion mobile phones in the world 80% were in developing countries and this proportion is continues to grow. African countries have embraced new financial technologies such as mobile payment: over 17m Kenyans use mobile money; around 25% of the country's GNP flows in this way. Crowd sourcing systems such as Ushahidi lead the way in the aggregation of social factors. The project will create a decision support and market segmentation platform generated via personal data, collaborative aggregation and crowd-sourced feedback, that will allow the generation new models of consumer behaviour to support innovation. Our work will hinge on three case studies in exemplar developing economies (Tanzania, Malaysia and China) where we will develop example behavioural segmentations via novel computational and clustering methods and in partnership with a range of data providers and internationally significant companies including: Alliance Boots, Dairy Farm International, Bakhresa Group, Boots, E-fulusi, Tesco, Marks & Spencer and Experian. Academic research into consumer behaviour patterns will be significantly advanced by the techniques developed, their application in this field is novel. There is scope to exploit advanced forms of computation and clustering that more readily account for market complexities. There is a very high chance that the project will provide insights into consumer behaviour that have hitherto remained obscure. So the contribution to research in this area could be both methodological and empirical and contextual (robust insights into developing world consumers are more rare). This expeditionary collaboration is likely to open the door to and on-going conversation between the fields of business/consumer analytics and computational analysis.

We are living in an era of Big data with the rapid technological developments in information technologies and communications providing an unprecedented amount of data and new forms of data. Big data is now an integral part of our daily lives and are routinely produced by local government and business. In these settings, data production is just a by-product of the activities local government or business are involved in: most often, this information is collected for a specific purpose but very little use is made of these data-sets beyond the original purpose they were designed for. The challenge is how we can make better use of these types of information to improve our quality of life and foster economic growth. If combined together, these datasets can provide valuable information and insights into how businesses and local authorities work, the ways in which improvements to services can be made or businesses become more successful and efficient in their operation. Big data can provide local authorities and businesses additional information which can help them to design better policies and improve their business operations. To date, very little data of this type has been available for social scientific research in a systematic way. The aim of the new Smart Data Analytics (SDA) for Business and Local Government research centre is to utilise this explosion of information for social scientific research to answer questions that affect all our lives. For example, in an era of austerity and belt-tightening for local authorities, how can they make best use of limited resources to deliver the highest quality service to residents including across health and social care provision, education, crime reduction, housing and transport? By using data sources collected by local authorities for their administrative purposes we can start to unravel some of these questions and make relevant and timely policy recommendations. We have partnered with three local councils in Kent, Essex and Norfolk who are keen to work with academic researchers to learn from the information they hold to improve their service delivery but at present do not fully utilise. We have also partnered with businesses who wish to understand how we can foster and support economic growth, particularly for small and medium enterprises and start-ups. What are the barriers these businesses face and how can Big data help us understand the best means of overcoming these? The SDA will establish a secure data facility at the University of Essex where Big data from a variety of sources are stored and matched so to produce new information which can be useful to both local authorities and businesses. At the same time, the facility will give researchers, local authorities and businesses a point of access to Big data and expertise and support in using those data. There are clearly many issues of data privacy and confidentiality to be considered and the Centre will develop safe methods of handling, anonymising and linking data to ensure the confidentiality of businesses and individuals is maintained and respected. The Centre will also carry out research into how Big data can best be analysed as some of the methods used for more standard forms of data such as social surveys may not apply. We have an innovative substantive research programme articulated in a set of research streams designed to focus on key policy issues: (i) Methodological advances in Big Data analysis; (ii) Local economic growth, (iii) Support for vulnerable people; and (iv) the Green Infrastructure. The Centre will also provide training and support to new researchers, businesses and local authorities and engage actively with both businesses and local authorities through tailored knowledge exchange activities which will draw on the expertise built in the Centre. The new Centre promises to be an exciting development that will not only advance knowledge but have a positive impact on our quality of life.

Our 21st century lives will be increasingly connected to our digital identities, representations of ourselves that are defined from trails of personal data and that connect us to commercial and public services, employers, schools, families and friends. The future health of our Digital Economy rests on training a new generation of leaders who can harness the emerging technologies of digital identity for both economic and societal value, but in a fair and transparent manner that accommodates growing public concern over the use of personal data. We will therefore train a community of 80 PhD students with the interdisciplinary skills needed to address the profound challenges of digital identity in the 21st century. Our training programme will equip students with a unique blend of interdisciplinary skills and knowledge across three thematic aspects of digital identity - enabling technologies, global impacts and people and society - while also providing them with the wider research and professional skills to deliver a research project across the intersection of at least two of these. Our students will be situated within Horizon, a leading centre for Digital Economy research and a vibrant environment that draws together a national research Hub, CDT and a network of over 100 industry, academic and international partners. Horizon currently provides access to a large network of over 75 potential supervisors, ranging from from leading Professors to talented early career researchers. Each student will work with an industry, public, third sector or international partner to ensure that their research is grounded in real user needs, to maximise its impact, and also to enhance their employability. These external partners will be involved in co-sponsorship, supervision, providing resources and hosting internships. Our external partners have already committed to co-sponsor 30 students so far, and we expect this number to grow. Our centre also has a strong international perspective, working with international partners to explore the global marketplace for digital identity services as well as the cross-cultural issues that this raises. This will build on our success in exporting the CDT model to China where we have recently established a £17M International Doctoral Innovation Centre to train 50 international students in digital economy research with funding from Chinese partners. We run an integrated four-year training programme that features a bespoke core covering key topics in digital identity, optional advanced specialist modules, practice-led team and individual projects, training in research methods and professional skills, public and external engagement, and cohort building activities including an annual writing retreat and summer school. The first year features a nine month structured process of PhD co-creation in which students, supervisors and external partners iteratively refine an initial PhD topic into a focused research proposal. Building on our experience of running the current Horizon CDT over the past five years, our management structure responds to external, university and student input and manages students through seven key stages of an extended PhD process: recruitment, induction, taught programme, PhD co-creation, PhD research, thesis, and alumni. Students will be recruited onto and managed through three distinct pathways - industry, international and institutional - that reflect the funding, supervision and visiting constraints of working with varied external partners.