Gavin Doherty

Hiba Arnaout, Anmol Goel, H. Andrew Schwartz et al.

Although artificial intelligence (AI) shows growing promise for mental health care, current approaches to evaluating AI tools in this domain remain fragmented and poorly aligned with clinical practice, social context, and first-hand user experience. This paper argues for a rethinking of responsible evaluation -- what is measured, by whom, and for what purpose -- by introducing an interdisciplinary framework that integrates clinical soundness, social context, and equity, providing a structured basis for evaluation. Through an analysis of 135 recent *CL publications, we identify recurring limitations, including over-reliance on generic metrics that do not capture clinical validity, therapeutic appropriateness, or user experience, limited participation from mental health professionals, and insufficient attention to safety and equity. To address these gaps, we propose a taxonomy of AI mental health support types -- assessment-, intervention-, and information synthesis-oriented -- each with distinct risks and evaluative requirements, and illustrate its use through case studies.

Karina Cortinas-Lorenzo, Gavin Doherty

As Artificial Intelligence (AI) systems continue to grow in size and complexity, so does the difficulty of the quest for AI transparency. In a world of large models and complex AI systems, why do we explain AI and what should we explain? While explanations serve multiple functions, in the face of complexity humans have used and continue to use explanations to foster learning. In this position paper, we discuss how learning theories can be infused in the XAI lifecycle, as well as the key opportunities and challenges when adopting a learner-centered approach to assess, design and evaluate AI explanations. Building on past work, we argue that a learner-centered approach to Explainable AI (XAI) can enhance human agency and ease XAI risks mitigation, helping evolve the practice of human-centered XAI.

Yucheng Jin, Wanling Cai, Li Chen et al.

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.

David Coyle, Mark Matthews, Gavin Doherty et al.

Using the metrics of the World Health Organisation, the Global Burden of Disease Study has found that mental health difficulties are currently the leading cause of disability in developed countries [1]. Projections also indicate that the global burden of mental health difficulties will continue to rise in the coming decades. The human and economic costs of this trend will be substantial. In this paper we discuss how effectively designed interactive systems, developed through collaborative, interdisciplinary efforts, can play a significant role in helping to address this challenge. Our discussion is grounded in a description of four exploratory systems, each of which has undergone initial clinical evaluations. Directions for future research on mental health technologies are also identified.

David Coyle, Gavin Doherty

Effective communication and strong therapeutic relationships are critical to successful mental health interventions. For example, in 1957 Carl Rogers, a pioneer of person-centred therapy, proposed that an empowering relationship could, in and of itself, create the necessary and sufficient conditions for positive therapeutic outcomes [1]. Whilst modern psychological theories no longer favour an exclusive focus on relationships, positive relationships and the dynamics of client-therapist communication remain cornerstones of mental health intervention theories. A more recent meta-review concluded that across all interventions models, irrespective of the theoretical approach, the quality of the relationship between therapists and clients is the second leading determinant of successful clinical outcomes [2]. Over the past ten years we (David Coyle and Gavin Doherty) have designed and evaluated a wide range to systems that provide support for psychological (or talk- based) mental health interventions [3]. Here we briefly consider two recent examples. In each case our aim was to enhance communication and reshape clinical practice in a manner that empowers patients. gNats Island is a computer game that supports face-to-face interventions for adolescents [4]. MindBalance is an online treatment programme for adults experiencing difficulties with depression [5].