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This paper aims to critically assess approaches to sex and gender in the Human Brain Project (HBP) as a large information and communication technology (ICT) project case study using intersectionality.

The strategy of the HBP is contextualised within the wider context of the representation of women in ICT, and critically reflected upon from an intersectional standpoint.

The policy underpinning the approach deployed by the HBP in response to these issues parallels Horizon 2020 wording and emphasises economic outcomes, productivity and value, which aligns with other “equality” initiatives influenced by neoliberalised versions of feminism.

Limitations include focussing on a single case study, the authors being funded as part of the Ethics and Society Subproject of the HBP, and the limited temporal period under consideration.

The frameworks underpinning the HBP approach to sex and gender issues present risks with regard to the further entrenchment of present disparities in the ICT sector, may fail to acknowledge systemic inequalities and biases and ignore the importance of intersectionality. Shortcomings of the approach employed by the HBP up to March, 2018 included aspects of each of these risks, and replicated problematic understandings of sex, gender and diversity.

This paper is the first to use an intersectional approach to issues of sex and gender in the context of large-scale ICT research. Its value lies in raising awareness, opening a discursive space and presenting opportunities to consider and reflect upon potential, contextualised intersectional solutions to such issues.

Neuroscience data are spread across a variety of sources, typically provisioned through ad-hoc and non-standard approaches and formats and often have no connection to the related data sources. These make it difficult for researchers to understand, integrate and reuse brain-related data. The aim of this study is to show that a graph-based approach offers an effective mean for representing, analysing and accessing brain-related data, which is highly interconnected, evolving over time and often needed in combination.

The authors present an approach for organising brain-related data in a graph model. The approach is exemplified in the case of a unique data set of quantitative neuroanatomical data about the murine basal ganglia––a group of nuclei in the brain essential for processing information related to movement. Specifically, the murine basal ganglia data set is modelled as a graph, integrated with relevant data from third-party repositories, published through a Web-based user interface and API, analysed from exploratory and confirmatory perspectives using popular graph algorithms to extract new insights.

The evaluation of the graph model and the results of the graph data analysis and usability study of the user interface suggest that graph-based data management in the neuroscience domain is a promising approach, since it enables integration of various disparate data sources and improves understanding and usability of data.

The study provides a practical and generic approach for representing, integrating, analysing and provisioning brain-related data and a set of software tools to support the proposed approach.

This chapter discusses the practical challenges and opportunities involved in merging the two fields of cognitive neurosciences and strategic management, starting from the premise that the need to marry them is justified by their complementarities, as opposed to the level of analysis on which they both focus. We discuss the potential benefits and drawbacks of using methods borrowed from cognitive neurosciences for management research. First, we argue that there are clear advantages in deploying techniques that enable researchers to observe processes and variables that are central to management research, with the caveat that neuroscientific methods and techniques are not general-purpose technologies. Second, we identify three core issues that specify the boundaries within which management scholars can usefully deploy such methods. Third, we propose a possible research agenda with various areas of synergy between the complementary capabilities of management and neuroscience scholars, aiming to generate valuable knowledge and insight for both disciplines and also for society as a whole.

Digital contact tracing technologies are critical to the fight against COVID-19 in many countries including the UK. However, a number of ethical, legal and socio-economic concerns that can affect uptake of the app have been raised. The purpose of this research is to explore the perceptions of the UK digital contact tracing app in the Black, Asian and Minority Ethnic (BAME) community in Leicester and how this can affect its deployment and implementation.

Data was collected through virtual focus groups in Leicester, UK. A total of 28 participants were recruited for the study. All participants are members of the BAME community, and data was thematically analysed with NVivo 11.

A majority of the participants were unwilling to download and use the app owing to legal and ethical concerns. A minority were willing to use the app based on the need to protect public health. There was a general understanding that lack of uptake will negatively affect the fight against COVID-19 in BAME communities and an acknowledgement of the need for the government to rebuild trust through transparency and development of regulatory safeguards to enhance privacy and prevent misuse.

To the best of the authors’ knowledge, the research makes original contributions being the first robust study conducted to explore perceptions of marginalised communities, particularly BAME which may be adversely impacted by the deployment of the app. By exploring community-based perceptions, this study further contributes to the emerging citizens’ perceptions on digital contact tracing which is crucial to the effectiveness and the development of an efficient, community-specific response to public attitudes towards the app. The findings can also help the development of responsible innovation approaches that balances the competing interests of digital health interventions with the needs and expectations of the BAME community in the UK.

This purpose of this viewpoint is to address the intended good and unintended bad impacts of artificial intelligence (AI) applications in financial crime.

The paper relied primarily on secondary data resources, business cases and relevant laws and regulations, and it used a legal-economics perspective.

Current AI systems could function as antidotes or accelerator of financial crime, in particular cybercrime. Research suggests criminal law could be applied via three approaches to curb these cybercrimes. However, others considered this to be an inappropriate mechanism to hold AI agents accountable, as present AI systems were not deemed capable of making ethically informed choices. Instead, administrative sanctions would be considered more appropriate for now. While keeping vigilance against AI malicious acts, regulatory authorities in the USA and the UK have opted largely for the innovation-friendly, market-oriented, permissionless approach over the state-interventionist stance so as to maintain their global competitive edge in this domain.

The paper reinforced the growing arguments that AI applications should be deployed more as panacea for financial crimes rather than being abused as crime accelerators. There equally though is the need for both public and private sectors to be mindful of the unintended negative, harmful consequences to society, especially those connected to cybercrime. This implied the further need to beef up attention and resources to help mitigate these risks.