Prof Yulan He

Title: Machine Reasoning for Natural Language Understanding

Abstract: Enabling machines to understand human languages requires equipping AI systems with reasoning capabilities. Reasoning is not necessarily achieved by making logical inferences. In our context, it refers to being able to manipulate previously acquired knowledge in order to enhance the understanding of new information encountered. In my talk, I will outline our recent progress in machine reasoning, including incorporating commonsense knowledge for emotion detection in dialogues, event semantic reasoning for answering questions relating to real-world events reported in news, and evidence reasoning for rumour veracity assessment.

Dr Ruba Abu-Salma

Title: Participatory Threat Modelling with Migrant Domestic Workers.

Abstract: The needs of marginalised groups like migrant domestic workers (MDWs) are often ignored in digital privacy and security research. If considered, MDWs are treated as 'bystanders' or even as threats rather than as targets of surveillance and legitimate security subjects in their own right. Using participatory threat modelling (PTM) as a method of incorporating marginalised populations' experiences, we designed and conducted five workshops with MDWs (n=32) in the UK to identify threats to their privacy and security. We found that MDWs named government surveillance, scams and harassment, and employer monitoring (in this order) as the primary threats to their privacy and security. We also examined the methods MDWs used to stay safe online, such as configuring the privacy settings of their online accounts and creating on- and offline community support networks. Based on our findings, we developed and disseminated a digital privacy and security guide with links to further resources that MDWs can refer to. We conclude by arguing that security research must consider broader social structures like gendered work and racialised border policy that foster insecurity in the lives of MDWs. We also present the key lessons of our work, including considering data sharing from the perspective of stakeholders who do not own technology devices but are affected by them, and reflecting on how security research can stop enabling harmful forms of surveillance.

Dr Jie Zhang

Title: Fairness in ML-based Software: Survey and Practices

Abstract: ML-based software is widely used for critical decision making applications, such as to select job applicants, to evaluate employees' performance, and to predict medical treatment. However, the decisions made by machine learning software can be unfair, affecting the benefit of people in minority groups or historically disadvantageous groups. Fairness has drawn substantial attention in both the software engineering community and machine learning community. In this talk, I will first overview the literature of fairness in ML-based software, then introduce several of my recent research publications on fairness testing and improvement.

Dr Seray Ibrahim

Title: Investigating and designing for in-situ parenting support with technology

Abstract: Parenting is one of the strongest predictors of child well-being and social-emotional competence. However prior research has shown that parents find it difficult to implement behavioural and emotion-focused parenting strategies within the context of their daily lives. Across HCI and psychology, little is known about what aspects of ‘evidence-based’ advice parents struggle with, or how technology-enabled interventions might be designed to support parent-child interactions in-situ. In this talk, I will describe and reflect on a series of studies we carried out as part of the Positive Parenting case study strand of Dr Petr Slovak’s UKRI Future Leaders Fellowship. Our goal, to date, has been to co-develop a set of methods that parents perceive as useful and interesting when self-reflecting on and sharing their parenting experiences. Our broader goal is to utilise insights from these methods to design novel, situated supports that can augment and build on the delivery of formal and informal parenting support.

Sara Tandon

Title: Visual Task Performance and Spatial Abilities: An Investigation of Artists and Mathematicians

Abstract: This talk presents a domain-specific empirical investigation of visual artists and math & computer scientists on their respective relationships to, perceptions of, and interactions with data visualization. We conducted a three-phase study utilizing mixed-methods to investigate performance on visual and text representations of data between domains. Our findings evidenced how math & computer scientists are proficient utilizing text representations of data while artists benefit more from visual chart representations comparatively. Finally, we present perspectives from artists to gain an understanding of their approach to visual and mathematical tasks. Our findings indicate that artists are especially adept at statistical visual tasks and that development of cognitive skills could be fostered by individuals to potentially benefit visualization task performance. This research is in the publication process as part of CHI 2023.