Computer Science

Annotation is the labeling of data by human effort. Annotation is critical to modern machine learning, and Bloomberg has developed years of experience of annotation at scale. This report captures a wealth of wisdom for applied annotation projects, collected from more than 30 experienced annotation project managers in Bloomberg's Global Data department.

Personalization should take the human person seriously. This requires a deeper understanding of how recommender systems can shape both our self-understanding and identity. We unpack key European humanistic and philosophical ideas underlying the General Data Protection Regulation (GDPR) and propose a new paradigm of humanistic personalization. Humanistic personalization responds to the IEEE's call for Ethically Aligned Design (EAD) and is based on fundamental human capacities and values. Humanistic personalization focuses on narrative accuracy: the subjective fit between a person's self-narrative and both the input (personal data) and output of a recommender system. In doing so, we re-frame the distinction between implicit and explicit data collection as one of nonconscious ("organismic") behavior and conscious ("reflective") action. This distinction raises important ethical and interpretive issues related to agency, self-understanding, and political participation. Finally, we discuss how an emphasis on narrative accuracy can reduce opportunities for epistemic injustice done to data subjects.

This thesis develops a conceptual framework considering social data as representing the surface layer of a hierarchy of human social behaviours, needs and cognition which is employed to transform social data into representations that preserve social behaviours and their causalities. Based on this framework two platforms were built to capture insights from fast-paced and slow-paced social data. For fast-paced, a self-structuring and incremental learning technique was developed to automatically capture salient topics and corresponding dynamics over time. An event detection technique was developed to automatically monitor those identified topic pathways for significant fluctuations in social behaviours using multiple indicators such as volume and sentiment. This platform is demonstrated using two large datasets with over 1 million tweets. The separated topic pathways were representative of the key topics of each entity and coherent against topic coherence measures. Identified events were validated against contemporary events reported in news. Secondly for the slow-paced social data, a suite of new machine learning and natural language processing techniques were developed to automatically capture self-disclosed information of the individuals such as demographics, emotions and timeline of personal events. This platform was trialled on a large text corpus of over 4 million posts collected from online support groups. This was further extended to transform prostate cancer related online support group discussions into a multidimensional representation and investigated the self-disclosed quality of life of patients (and partners) against time, demographics and clinical factors. The capabilities of this extended platform have been demonstrated using a text corpus collected from 10 prostate cancer online support groups comprising of 609,960 prostate cancer discussions and 22,233 patients.

With the widespread and pervasive use of Artificial Intelligence (AI) for automated decision-making systems, AI bias is becoming more apparent and problematic. One of its negative consequences is discrimination: the unfair, or unequal treatment of individuals based on certain characteristics. However, the relationship between bias and discrimination is not always clear. In this paper, we survey relevant literature about bias and discrimination in AI from an interdisciplinary perspective that embeds technical, legal, social and ethical dimensions. We show that finding solutions to bias and discrimination in AI requires robust cross-disciplinary collaborations.

In recent years, data science has become an indispensable part of our society. Over time, we have become reliant on this technology because of its opportunity to gain value and new insights from data in any field - business, socializing, research and society. At the same time, it raises questions about how justified we are in placing our trust in these technologies. There is a risk that such powers may lead to biased, inappropriate or unintended actions. Therefore, ethical considerations which might occur as the result of data science practices should be carefully considered and these potential problems should be identified during the data science lifecycle and mitigated if possible. However, a typical data scientist has not enough knowledge for identifying these challenges and it is not always possible to include an ethics expert during data science production. The aim of this study is to provide a practical guideline to data scientists and increase their awareness. In this work, we reviewed different sources of biases and grouped them under different stages of the data science lifecycle. The work is still under progress. The aim of early publishing is to collect community feedback and improve the curated knowledge base for bias types and solutions.

This report focuses on safety aspects of connected and automated vehicles (CAVs). The fundamental question to be answered is how can CAVs improve road users' safety? Using advanced data mining and thematic text analytics tools, the goal is to systematically synthesize studies related to Big Data for safety monitoring and improvement. Within this domain, the report systematically compares Big Data initiatives related to transportation initiatives nationally and internationally and provides insights regarding the evolution of Big Data science applications related to CAVs and new challenges. The objectives addressed are: 1-Creating a database of Big Data efforts by acquiring reports, white papers, and journal publications; 2-Applying text analytics tools to extract key concepts, and spot patterns and trends in Big Data initiatives; 3-Understanding the evolution of CAV Big Data in the context of safety by quantifying granular taxonomies and modeling entity relations among contents in CAV Big Data research initiatives, and 4-Developing a foundation for exploring new approaches to tracking and analyzing CAV Big Data and related innovations. The study synthesizes and derives high-quality information from innovative research activities undertaken by various research entities through Big Data initiatives. The results can provide a conceptual foundation for developing new approaches for guiding and tracking the safety implications of Big Data and related innovations.

Since early 2020 the COVID-19 pandemic has had a considerable impact on many aspects of daily life. A range of different measures have been implemented worldwide to reduce the rate of new infections and to manage the pressure on national health services. A primary strategy has been to reduce gatherings and the potential for transmission through the prioritisation of remote working and education. Enhanced hand hygiene and the use of facial masks have decreased the spread of pathogens when gatherings are unavoidable. These particular measures present challenges for reliable biometric recognition, e.g. for facial-, voice- and hand-based biometrics. At the same time, new challenges create new opportunities and research directions, e.g. renewed interest in non-constrained iris or periocular recognition, touch-less fingerprint- and vein-based authentication and the use of biometric characteristics for disease detection. This article presents an overview of the research carried out to address those challenges and emerging opportunities.

This paper unapologetically reflects on the critical role that Black feminism can and should play in abolishing algorithmic oppression. Positioning algorithmic oppression in the broader field of feminist science and technology studies, I draw upon feminist philosophical critiques of science and technology and discuss histories and continuities of scientific oppression against historically marginalized people. Moreover, I examine the concepts of invisibility and hypervisibility in oppressive technologies a lá the canonical double bind. Furthermore, I discuss what it means to call for diversity as a solution to algorithmic violence, and I critique dialectics of the fairness, accountability, and transparency community. I end by inviting you to envision and imagine the struggle to abolish algorithmic oppression by abolishing oppressive systems and shifting algorithmic development practices, including engaging our communities in scientific processes, centering marginalized communities in design, and consensual data and algorithmic practices.

Black Lives Matter has been a major force for social change in the US since 2014, with social media playing a core role in the development and proliferation of the movement. The largest protests in US history occurred in late May and early June 2020, following the death of George Floyd at the hands of Minneapolis police. This incident reignited widespread support for the BLM movement. The protests were notable not only for their size but also that they occurred at a time the US was still struggling to control the spread of the COVID-19 pandemic, with more than 20,000 new cases per day. With protest conditions and police crowd control tactics exacerbating disease spread and with COVID disproportionately affecting minority populations, it was hypothesised that participation in and support for the protests would involve a balancing act between the risks of systemic racism and of disease spread. However, social media data suggest that this was not the case, with discussion of the BLM movement replacing discussion of COVID on US social media. Neither supporters or opposers of the BLM movement or protest action mentioned COVID as a factor. Framings of the movement by BLM supporters largely replicated those of earlier studies, with new frames emerging surrounding the opposition the movement has received from racism, police militarisation and President Donald Trump. Discourse evidenced worrying levels of polarisation, hate, incivility and conspiracy content and bore many similarities to previously studied COVID discourse. This suggests that George Floyd's death, as yet another example of an African American man killed by US police, was largely seen through established, polarised identity positions that made reactions to the incident and resulting protest largely a foregone conclusion, established and articulated without reference to the ongoing pandemic.

Blockchain is a challenging topic since it is novel and fosters potential innovation. The blockchain is attractive for various disciplines, and, because of its cross-cutting nature, needs knowledge stemming from various disciplines. The devised curriculum can be instantiated specifically to meet the needs of students' groups from various disciplines. The pedagogical innovation of the project is the inclusion of interdisciplinary project groups with participant's interaction via online platforms for project-based learning activities. MOOCs and SNOCs allow blended-learning for interdisciplinary and geographically distributed student groups.