Gavin Doherty

Expressive arts therapy supports creative expression through visual art, music, dance, and drama, fostering emotional awareness, regulation, and personal growth. Recent advances in generative AI, particularly multimodal models that produce images, music, text, and soundscapes, create new opportunities to scaffold creative exploration, provide adaptive prompts, and support reflective dialogue in therapeutic contexts. At the same time, integrating AI into expressive arts therapy raises important challenges around agency, authorship, emotional alignment, ethics, data privacy, and preserving therapeutic relationships. This workshop brings together researchers, designers, therapists, and practitioners to explore how human-centered AI can support creativity in art and music therapy while respecting therapeutic values. We focus on insights from AI-enabled therapy systems, design approaches balancing creativity and emotional well-being, and evaluation methods that extend beyond usability and traditional clinical outcomes. Through interdisciplinary discussion, the workshop aims to surface open research questions and design considerations for AI use in expressive arts therapy.

As AI systems become increasingly integrated into our lives, the need to support appropriate human understanding of AI continues to grow. With new AI capabilities being deployed in different contexts, human-centered explainability is crucial to ensure people can interact with novel AI systems safely and effectively. To address evolving explainability needs, the field of Explainable AI (XAI) has produced numerous frameworks. But what do these frameworks entail and how can they be used in practice? What drives their development? As AI systems continue to grow in complexity, it is important to understand and reflect upon the value of these frameworks and their potential to address upcoming human-centered needs for XAI. Towards this, we performed a scoping review following the PRISMA-ScR procedure, gathering and analyzing a corpus of 73 papers to understand how XAI frameworks can support different stages of human-centered XAI design. We present a unified model and a set of guiding questions to help identify, compare and select relevant XAI frameworks across various design stages, making it easier for designers and researchers to apply human-centered approaches in real-world XAI contexts. We also analyze how frameworks are developed and evaluated, highlighting gaps and opportunities to improve both methodological as well as existing HCXAI practices.

Security attacks are rising, as evidenced by the number of reported vulnerabilities. Among them, unknown attacks, including new variants of existing attacks, technical blind spots or previously undiscovered attacks, challenge enduring security. This is due to the limited number of techniques that diagnose these attacks and enable the selection of adequate security controls. In this paper, we propose an automated technique that detects and diagnoses unknown attacks by identifying the class of attack and the violated security requirements, enabling the selection of adequate security controls. Our technique combines anomaly detection to detect unknown attacks with abductive reasoning to diagnose them. We first model the behaviour of the smart home and its requirements as a logic program in Answer Set Programming (ASP). We then apply Z-Score thresholding to the anomaly scores of an Isolation Forest trained using unlabeled data to simulate unknown attack scenarios. Finally, we encode the network anomaly in the logic program and perform abduction by refutation to identify the class of attack and the security requirements that this anomaly may violate. We demonstrate our technique using a smart home scenario, where we detect and diagnose anomalies in network traffic.We evaluate the precision, recall and F1-score of the anomaly detector and the diagnosis technique against 18 attacks from the ground truth labels provided by two datasets, CICIoT2023 and IoT-23. Our experiments show that the anomaly detector effectively identifies anomalies when the network traces are strong indicators of an attack. When provided with sufficient contextual data, the diagnosis logic effectively identifies true anomalies, and reduces the number of false positives reported by anomaly detectors. Finally, we discuss how our technique can support the selection of adequate security controls.

Machine learning-augmented applications have the potential to be powerful tools for decision-making in healthcare. However, healthcare is a complex domain that presents many challenges. These challenges, such as medical errors, clinician-patient relationships, and treatment preferences, must be addressed to ensure fairness in ML-augmented healthcare applications. To better understand the influence these challenges have on fairness, 16 experienced engineers and designers with domain knowledge in healthcare technology were interviewed about how they would prioritise fairness in three healthcare scenarios (well-being improvement, chronic illness management, acute illness treatment). Using a template analysis, this work identifies the key considerations in the creation of fair ML for healthcare. These considerations clustered into categories related to technology, healthcare context, and user perspectives. To explore these categories, we propose the stakeholder fairness conceptual model. This framework aids designers and developers in understanding the complex considerations that stem from the building, management, and evaluation of ML-augmented healthcare applications, and how they affect the expectations of fairness. This work then discusses how this model may be applied when the health technology is directly provisioned to users, without a healthcare provider managing its use or adoption. This paper contributes to the understanding of fairness requirements in healthcare, including the effect of healthcare errors, clinician-application collaboration, and how the evaluation of healthcare technology becomes part of the fairness design process.

In this column, we illustrate real-world scenarios in which modern systems cannot preserve security during operation. We examine the notion of sustainable security and discuss the challenges to engineering sustainably secure systems.

Mood logging, where people track mood-related data, is commonly used to support mental healthcare. Speech agents could prove beneficial in supporting mood logging for clients. Yet we know little about how Mental Healthcare Practitioners (MHPs) view speech as a tool to support current care practices. Through a thematic analysis of semi-structured interviews with 15 MHPs, we show that MHPs see opportunities in the convenience, and the data richness that speech agents could afford. However, MHPs also saw this richness as noisy, with using speech potentially diminishing a client’s focus on mood logging as an activity. MHPs were wary of overusing AI-based tools, expressing concerns around data ownership, access and privacy. We discuss the role of speech agents within blended care, outlining key considerations when using speech for mood logging in a blended mental healthcare context.

Music-based reminiscence has the potential to positively impact the psychological well-being of older adults. However, the aging process and physiological changes, such as memory decline and limited verbal communication, may impede the ability of older adults to recall their memories and life experiences. Given the advanced capabilities of generative artificial intelligence (AI) systems, such as generated conversations and images, and their potential to facilitate the reminiscing process, this study aims to explore the design of generative AI to support music-based reminiscence in older adults. This study follows a user-centered design approach incorporating various stages, including detailed interviews with two social workers and two design workshops (involving ten older adults). Our work contributes to an in-depth understanding of older adults’ attitudes toward utilizing generative AI for supporting music-based reminiscence and identifies concrete design considerations for the future design of generative AI to enhance the reminiscence experience of older adults.