Speech technology is now pervasive throughout the world, impacting a variety of socio-technical use-cases. Speech technology is a broad term encompassing capabilities that translate, analyse, transcribe, generate, modify, enhance, or summarise human speech. Many of the technical features and the possibility of speech data misuse are not often revealed to the users of such systems. When combined with the rapid development of AI and the plethora of use-cases where speech-based AI systems are now being applied, the consequence is that researchers, regulators, designers and government policymakers still have little understanding of the public’s perception of speech technology. Our research explores the public’s perceptions of trust in speech technology by asking people about their experiences, awareness of their rights, their susceptibility to being harmed, their expected behaviour, and ethical choices governing behavioural responsibility. We adopt a multidisciplinary lens to our work, in order to present a fuller picture of the United Kingdom (UK) public perspective through a series of socio-technical scenarios in a large-scale survey. We analysed survey responses from 1,000 participants from the UK, where people from different walks of life were asked to reflect on existing, emerging, and hypothetical speech technologies. Our socio-technical scenarios are designed to provoke and stimulate debate and discussion on principles of trust, privacy, responsibility, fairness, and transparency. We found that gender is a statistically significant factor correlated to awareness of rights and trust. We also found that awareness of rights is statistically correlated to perceptions of trust and responsible use of speech technology. By understanding the notions of responsibility in behaviour and differing perspectives of trust, our work encapsulates the current state of public acceptance of speech technology in the UK. Such an understanding has the potential to affect how regulatory and policy frameworks are developed, how the UK invests in its AI research and development ecosystem, and how speech technology that is developed within the UK might be received by global stakeholders.

Telepresence robots have the potential to change our experiences in galleries and museums, allowing for a range of hybrid interactions for visitors and museum professionals, improving accessibility, offering activities or information, and providing a range of practical use cases (e.g. the robots augmenting museum exhibits).We present the results of 3 qualitative studies conducted in the UK exploring the acceptability (1 -interviews with museum professionals with no previous exposure to telepresence), acceptance (2 -focus groups for initial exposure to telepresence robots), and adoption (3 -interviews with museum professionals with long-term exposure to

Large Language Models (LLMs) are seemingly infiltrating every domain, and the legal context is no exception. In this paper, we present the results of three experiments (total N=288) that investigated lay people's willingness to act upon, and their ability to discriminate between, LLM- and lawyer-generated legal advice. In Experiment 1, participants judged their willingness to act on legal advice when the source of the advice was either known or unknown. When the advice source was unknown, participants indicated that they were significantly more willing to act on the LLM-generated advice. This result was replicated in Experiment 2. Intriguingly, despite participants indicating higher willingness to act on LLM-generated advice in Experiments 1 and 2, participants discriminated between the LLM- and lawyer-generated texts significantly above chance-level in Experiment 3. Lastly, we discuss potential explanations and risks of our findings, limitations and future work, and the importance of language complexity and real-world comparability.

Increasingly, robots are adopted for routine tasks such as cleaning and disinfection of public spaces, raising questions about attitudes and trust of professional cleaners who might in future have robots as teammates, and whether the general public feels reassured when disinfection is carried out by robots. In this paper, we present the results of a mixed-methods user study exploring how trust and reassurance by both professional cleaners and members of the public is affected by the use of a UV-C disinfection robot and information about its performance after disinfecting a simulated classroom. The results show a range of insights for those designing and wishing to deploy UV-C robots: we found that trust and reassurance are affected by information about the UV-C robot’s task performance, with more information coinciding with significantly more agreement to be able to judge that the robot is doing a good job. However, care should be taken when designing information about task performance to avoid misinterpretation. Overall, the results suggest a generally positive picture regarding the use of UV-C disinfecting robots and that cleaning professionals would be happy to have them as their teammates; however, there were also some concerns regarding the effect on less-skilled jobs. Taken together, our results provide considerations to make UV-C robots welcomed by cleaning teams as well as to provide reassurance to space users.

In recent times there has been an increase in data availability in continuous data streams and clustering of this data has many advantages in data analysis. It is often the case that these data streams are not stationary, but evolve over time, and also that the clusters are not regular shapes but form arbitrary shapes in the data space. Previous techniques for clustering such data streams are either hybrid online / offline methods, windowed offline methods, or find only hyper-elliptical clusters. In this paper we present a fully online technique for clustering evolving data streams into arbitrary shaped clusters. It is a two stage technique that is accurate, robust to noise, computationally and memory efficient, with a low time penalty as the number of data dimensions increases. The first stage of the technique produces micro-clusters and the second stage combines these micro-clusters into macro-clusters. Dimensional stability and high speed is achieved through keeping the calculations both simple and minimal using hyper-spherical micro-clusters. By maintaining a graph structure, where the micro-clusters are the nodes and the edges are its pairs with intersecting micro-clusters, we minimise the calculations required for macro-cluster maintenance. The micro-clusters themselves are described in such a way that there is no calculation required for the core and shell regions and no separate definition of outer micro-clusters necessary. We demonstrate the ability of the proposed technique to join and separate macro-clusters as they evolve in a fully online manner. There are no other fully online techniques that the authors are aware of and so we compare the technique with popular online / offline hybrid alternatives for accuracy, purity and speed. The technique is then applied to real atmospheric science data streams and used to discover short term, long term and seasonal drift and their effects on anomaly detection. As well as having favourable computational characteristics, the technique can add analytic value over hyper-elliptical methods by characterising the cluster hyper-shape using Euclidean or fractal shape factors. Because the technique records macro-clusters as graphs, further analytic value accrues from characterising the order, degree, and completeness of the cluster-graphs as they evolve over time.