Outputs (135)

TrustScapes: A visualisation tool to capture stakeholders' concerns and recommendations about data protection, algorithmic bias, and online safety (2023)
Journal Article
Ito-Jaeger, S., Lane, G., Dowthwaite, L., Webb, H., Patel, M., Rawsthorne, M., …Perez Vallejos, E. (2023). TrustScapes: A visualisation tool to capture stakeholders' concerns and recommendations about data protection, algorithmic bias, and online safety. International Journal of Qualitative Methods, 22, 1-10. https://doi.org/10.1177/1609406923118696

This paper presents a new methodological approach, TrustScapes, an open access tool designed to identify and visualise stakeholders’ concerns and policy recommendations on data protection, algorithmic bias, and online safety for a fairer and more tru... Read More about TrustScapes: A visualisation tool to capture stakeholders' concerns and recommendations about data protection, algorithmic bias, and online safety.

TrustScapes: A Visualisation Tool to Capture Stakeholders’ Concerns and Recommendations About Data Protection, Algorithmic Bias, and Online Safety (2023)
Journal Article
Ito-Jaeger, S., Lane, G., Dowthwaite, L., Webb, H., Patel, M., Rawsthorne, M., …Perez Vallejos, E. (2023). TrustScapes: A Visualisation Tool to Capture Stakeholders’ Concerns and Recommendations About Data Protection, Algorithmic Bias, and Online Safety. International Journal of Qualitative Methods, 22, https://doi.org/10.1177/16094069231186965

This paper presents a new methodological approach, TrustScapes, an open access tool designed to identify and visualise stakeholders’ concerns and policy recommendations on data protection, algorithmic bias, and online safety for a fairer and more tru... Read More about TrustScapes: A Visualisation Tool to Capture Stakeholders’ Concerns and Recommendations About Data Protection, Algorithmic Bias, and Online Safety.

Thinking Like a Machine: Alan Turing, Computation and the Praxeological Foundations of AI (2023)
Journal Article
Saha, D., Brooker, P., Mair, M., & Reeves, S. (2023). Thinking Like a Machine: Alan Turing, Computation and the Praxeological Foundations of AI. Science & Technology Studies, https://doi.org/10.23987/sts.122892

As part of ongoing research bridging ethnomethodology and computer science, in this article we offer an alternate reading of Alan Turing’s 1936 paper, “On Computable Numbers”. Following through Turing’s machinic respecification of computation, we hop... Read More about Thinking Like a Machine: Alan Turing, Computation and the Praxeological Foundations of AI.

Combinatory logic and lambda calculus are equal, algebraically (2023)
Journal Article
Altenkirch, T., Kaposi, A., Šinkarovs, A., & Végh, T. (2023). Combinatory logic and lambda calculus are equal, algebraically. LIPIcs, Article 24

It is well-known that extensional lambda calculus is equivalent to extensional combinatory logic. In this paper we describe a formalisation of this fact in Cubical Agda. The distinguishing features of our formalisation are the following: (i) Both lan... Read More about Combinatory logic and lambda calculus are equal, algebraically.

Crafting Interactive Experiences with Non-programmers (2023)
Conference Proceeding
Greenhalgh, C. (2023). Crafting Interactive Experiences with Non-programmers. In EICS '23 Companion: Companion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems (1-4). https://doi.org/10.1145/3596454.3597174

The Mixed Reality Lab has a long history of creating public interactive experiences in collaboration with creative practitioners. Looking across four such experiences, this keynote explores the role of code (i.e., bespoke software) in making them pos... Read More about Crafting Interactive Experiences with Non-programmers.

Explaining time series classifiers through meaningful perturbation and optimisation (2023)
Journal Article
Meng, H., Wagner, C., & Triguero, I. (2023). Explaining time series classifiers through meaningful perturbation and optimisation. Information Sciences, 645, Article 119334. https://doi.org/10.1016/j.ins.2023.119334

Machine learning approaches have enabled increasingly powerful time series classifiers. While performance has improved drastically, the resulting classifiers generally suffer from poor explainability, limiting their applicability in critical areas. S... Read More about Explaining time series classifiers through meaningful perturbation and optimisation.

A categorical account of composition methods in logic (2023)
Conference Proceeding
Marsden, D., Shah, N., & Jakl, T. (2023). A categorical account of composition methods in logic. In Proceedings of the 38th Annual Symposium on Logic in Computer Science (LICS 2023)

We present a categorical theory of the composition methods in finite model theory -- a key technique enabling modular reasoning about complex structures by building them out of simpler components. The crucial results required by the composition meth... Read More about A categorical account of composition methods in logic.

Explainable AI for the Arts: XAIxArts (2023)
Conference Proceeding
Bryan-Kinns, N., Ford, C., Chamberlain, A., Benford, S. D., Kennedy, H., Li, Z., …Rezwana, J. (2023). Explainable AI for the Arts: XAIxArts. In C&C '23: Proceedings of the 15th Conference on Creativity and Cognition. https://doi.org/10.1145/3591196.3593517

This first workshop on explainable AI for the Arts (XAIxArts) brings together a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, explainable AI (XAI), and Digital Arts to explore the rol... Read More about Explainable AI for the Arts: XAIxArts.

Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset (2023)
Conference Proceeding
Wu, Z., Moemeni, A., Caleb-Solly, P., & Castle-Green, S. (2023). Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset. In 2023 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn54540.2023.10191368

A large number of deep learning based object detection algorithms have been proposed and applied in a wide range of domains such as security, autonomous driving and robotics. In practical usage, objects being occluded are common, and can result in re... Read More about Robustness of Deep Learning Methods for Occluded Object Detection - A Study Introducing a Novel Occlusion Dataset.

Reflections on Responsible Research and Innovation (RRI) for Trustworthy Autonomous Systems (TAS): A message from Journal of Responsible Technology Special Issue's editors (2023)
Journal Article
Vallejos, E. P., Dowthwaite, L., Barnard, P., & Coomber, B. (2023). Reflections on Responsible Research and Innovation (RRI) for Trustworthy Autonomous Systems (TAS): A message from Journal of Responsible Technology Special Issue's editors. Journal of Responsible Technology, 14, Article 100059. https://doi.org/10.1016/j.jrt.2023.100059