Ethics and Executability: Tracing Decency in Decentralised Knowledge Graph Applications

Increasing interest in decentralisation for data and processing on the Web brings with it the need to re-examine methods for verifying data and behaviour for scalable multi-party interactions. We consider factors previously identified as relevant to verification of processing activity on knowledge graphs in a Trusted Decentralised Web, and use an implementation scenario to identify focused open questions

Annotators’ Perspectives: Exploring the Influence of Identity on Interpreting Misogynoir

Social Networking Sites are home to different forms of hate, including "Misogynoir", which specifically targets Black women through a combination of racism and sexism. Detecting misogynoir presents challenges due to its subjective nature and the varied interpretations of hate speech. Using annotator justifications from four distinct demographic groups; including Black women, Black men, White women and White men, we seek to gain a deeper understanding of the factors that influence annotators' reasoning process and labelling decisions for potential cases of Misogynoir and Allyship. Given the unique experiences of Black women who face both racism and sexism, the study sought to understand how their intersectional identities shape their perspectives compared to other groups. The research employed a qualitative analysis of responses from participants to identify key themes and patterns. Three significant themes emerged from our in-depth qualitative analysis of these annotator justifications: prior knowledge and experience, the language of the social media post, and its context. Our results revealed that annotators historically at risk of abuse demonstrated a nuanced understanding of how their intersecting identities inform their interpretations and judgement of tweets, drawing on their personal encounters with misogyny and racism compared to their non-target counterparts of this type of hate. This study underscores the significance of diverse annotator perspectives and content comprehension in understanding and addressing hate speech, particularly when it intersects with multiple forms of discrimination. Our study contributes to the methodological advancements in social network analysis and mining, highlighting the importance of considering annotator characteristics in the development of tools and approaches for detecting and addressing intersectional hate

Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives

Understanding Misogynoir: A Study of Annotators’ Perspectives

"Misogynoir" is the anti-Black racist misogyny experienced by Black women, which is characterised by components of both racism and sexism. Misogynoir is challenging to detect due to its inherent subjectivity and its intersectional nature, and people's opinions and interpretations of such hate might vary, which adds to the challenges of understanding it. In this paper, we explored how and some potential why's different annotator characteristics influence how they interpret and annotate a dataset for potential cases of Misogynoir and Allyship. We sampled tweets containing public responses to self-reported misogynoir cases by four prominent Black women in technology, designed an online annotation task study, and recruited annotators of diverse ethnicities and genders from the Prolific crowdsourcing platform. We found that participants' sources of evidence in judging and interpreting content for potential cases of Misogynoir and Allyship, even in circumstances where they all agree on a prospective label, vary across different factors, such as different ethnicity, lived experiences and gender. In addition, we present a variety of plausible interpretations influenced by the various annotators' characteristics. This study demonstrates the relevance of different annotator perspectives and content comprehension in hate speech and the need for further efforts to understand intersectional hate better

Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning How the output of AI systems can be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives

2016 Research & Innovation Day Program

A one day showcase of applied research, social innovation, scholarship projects and activities.https://first.fanshawec.ca/cri_cripublications/1003/thumbnail.jp

Decency and Decentralisation: Verifiable Decentralised Knowledge Graph Querying

Increasing interest in decentralisation for data and processing on the Web brings with it the need to re-examine methods for verifying data and behaviour for scalable multi-party interactions. We consider factors relevant to verification of querying activity on knowledge graphs in a Trusted Decentralised Web, and set out ideas for future research in this area

Trusting Decentralised Knowledge Graphs and Web Data at the Web Conference

Knowledge Graphs have become a foundation for sharing data on the web and building intelligent services across many sectors and also within some of the most successful corporations in the world. The over centralisation of data on the web, however, has been raised as a concern by a number of prominent researchers in the field. For example, at the beginning of 2022 a €2.7B civil lawsuit was launched against Meta on the basis that it has abused its market dominance to impose unfair terms and conditions on UK users in order to exploit their personal data. Data centralisation can lead to a number of problems including: lock-in/siloing effects, lack of user control over their personal data, limited incentives and opportunities for interoperability and openness, and the resulting detrimental effects on privacy and innovation. A number of diverse approaches and technologies exist for decentralising data, such as federated querying and distributed ledgers. The main question is, though, what does decentralisation really mean for web data and Knowledge Graphs? What are the main issues and tradeoffs involved? These questions and others are addressed in this workshop

Trust, accountability, and autonomy in knowledge graph-based AI for self-determination

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives

ESWC 2023 Workshops and Tutorials Joint Proceedings: Joint Proceedings of the ESWC 2023 Workshops and Tutorials co-located with the 20th European Semantic Web Conference (ESWC 2023)

Joint Proceedings of the ESWC 2023 Workshops and Tutorials co-located with the 20th European Semantic Web Conference (ESWC 2023