AI Futures of Culture and Memory

Neural Audio is a category of deep learning pipelines which output audio signals directly, in real-time scenarios of action-sound interactions. In this work, we examine how neural audio-based artificial intelligence, when embedded in digital musical instruments (DMIs), shapes embodied musical interaction. While DMIs have long struggled to match the physical immediacy of acoustic instruments, neural audio methods can magnify this challenge, requiring data collection, model training and deep theoretical knowledge that appear to push musicians toward symbolic or conceptual modes of engagement. Paradoxically, these same methods can also foster more embodied practices, by introducing opaque yet expressive behaviors that free performers from rigid technical models and encourage discovery through tactile, real-time experimentation. Drawing on established perspectives in DMI embodiment literature, as well as emerging neural-audio-focused efforts within the community, we highlight two seemingly conflicting aspects of these instruments: on one side, they inherit many “disembodying” traits known from DMIs; on the other, they open pathways reminiscent of acoustic phenomenology and soma, potentially restoring the close physical interplay often missed in digital performance.

This paper introduces principles for the application and challenges of small data ethnography in digital research. It discusses the need to incorporate ethics in every step of the research process. As teachers and researchers within the digital humanities, we argue for the value of a qualitative approach to digital contents, spaces, and phenomena. This article is relevant as a guide for students and researchers whose studies examine digital practices, phenomena, and social communities that occur in, through, or in relation to digital contexts.

The contributions in this book shed light on the complexity of surveillance in a digital age and problematise power relations between the many actors involved in the development and performance of surveillance culture. More and more actors and practices play an increasing role in our contemporary digitalised society, and the chapters show how people negotiate surveillance in their use of digital media, often knowingly leaving digital footprints, and sometimes trying to avoid surveillance. The digital transformation will continue in the foreseeable future. The coordination and analysis of data is viewed by many government agencies, corporations, and other actors as important tools for improving public administration, health, and economic growth. For this development to be legitimate, it is important that hard values, such as technical and legal developments, and soft values, such as ethical and cultural values, are taken into consideration. 

It is becoming more common to replace or augment human-based decisions with algorithmic calculations and evaluations using artificial intelligence (AI). Facial analysis systems (FA) are examples of how AI in particular is intertwined with both the most mundane and the most critical aspects of human life. Exploring images for the purposes of face detection, recognition and/or classification, FA shows an entanglement between human identity, self-presentation and computation. In this chapter, we discuss automated facial analysis technology from a queer theoretical standpoint, focusing on the concerns and risks when systems like FA are used in a binary way to categorize, measure and make decisions based on computerized assumptions about gender and sexuality. Further, we discuss issues of privacy, bias and fairness related to FA technology as well as potential improvements, for example, by using participatory design. Finally, this chapter suggests that a queer perspective on FA can create new ways to relate to technology.

This article investigates the pervasive issue of bias within AI-driven cultural heritage collections, emphasizing how digital technologies both inherit and amplify existing societal and historical prejudices embedded in analogue records. It outlines the multifaceted nature of bias—ranging from data selection and annotation to algorithmic design and user interaction—demonstrating how each stage of the AI pipeline can introduce or perpetuate distortions in representation. Through a critical review of current scholarship and practical case studies, particularly in image classification, the article evaluates technical strategies such as data augmentation, adversarial debiasing, and monitoring plans for bias mitigation. The findings reveal that while methods like noise injection and colour jittering can balance datasets and improve model fairness, effective bias mitigation ultimately depends on interdisciplinary collaboration between heritage professionals, subject experts, and data scientists. The article concludes that addressing bias requires an ongoing, holistic approach, integrating both technical and humanistic perspectives from data collection to model deployment. This ensures more inclusive, accurate, and ethically sound representations of cultural heritage, supporting the sector’s goals of diversity and accessibility for future audiences.

AI and Image illustrates the importance of critical perspectives in the study of AI and its application to image collections in the art and heritage sector. The authors’ approach is that such entanglements of image and AI are neither dystopian or utopian but may amplify, reduce or condense existing societal inequalities depending on how they may be implemented in relation to human expertise and sensibility in terms of diversity and inclusion. The Element further discusses regulations around the use of AI for such cultural datasets as they touch upon legalities, regulations and ethics. In the conclusion they emphasise the importance of the professional expert factor in the entanglements of AI and images and advocate for a continuous and renegotiating professional symbiosis between human and machines. This title is also available as Open Access on Cambridge Core.

Technological advancements in deep learning for speech and voice have contributed to a recent expansion in applications for voice cloning, synthesis and generation. Invisibilised stakeholders in this expansion are numerous absent bodies, whose voices and voice data have been integral to the development and refinement of these speech technologies. This position paper probes current working practices for voice and speech in machine learning and AI, in which the bodies of voices are “invisibilised". We examine the facts and concerns about the voice-Body in applications of AI-voice technology. We do this through probing the wider connections between voice data and Schaefferian listening; speculating on the consequences of missing Bodies in AI-Voice; and by examining how vocalists and artists working with synthetic Bodies and AI-voices are ‘bringing the Body back’ in their own practices. We contribute with a series of considerations for how practitioners and researchers may help to ‘bring the Body back’ into AI-voice technologies.

The explosion of content generated by artificial intelligence (AI) models has initiated a cultural shift in arts, music, and media, whereby roles are changing, values are shifting, and conventions are challenged. The vast, readily available dataset of the Internet has created an environment for AI models to be trained on any content on the Web. With AI models shared openly and used by many globally, how does this new paradigm shift challenge the status quo in artistic practices? What kind of changes will AI technology bring to music, arts, and new media?

In the face of the ever-increasing document volume, libraries around the globe are more and more exploring (semi-) automated approaches to subject indexing. This helps sustain bibliographic objectives, enrich metadata, and establish more connections across documents from various collections, effectively leading to improved information retrieval and access. However, generally accepted automated approaches that are functional in operative systems are lacking. This article aims to provide an overview of basic principles used for automated subject indexing, major approaches in relation to their possible application in actual library systems, existing working examples, as well as related challenges calling for further research.