Our article AI, cultural heritage, and bias examines the challenges and potential solutions for using machine learning to interpret and classify human memory and cultural heritage artifacts. We argue that bias is inherent in cultural heritage collections (CHCs) and their digital versions and that AI pipelines may amplify this bias. We hypothesise that effective AI methods require vast, well-annotated datasets with structured metadata, which CHCs often lack due to diverse digitisation practices and limited interconnectivity. This paper discusses the definition of bias in CHCs and other datasets, exploring how it stems from training data and insufficient humanities expertise in generative platforms. We conclude that scholarship, guidelines, and policies on AI and CHCs should address bias as both inherent and augmented by AI technologies. We recommend implementing bias mitigation techniques throughout the process, from collection to curation, to support meaningful curation, embrace diversity, and cater to future heritage audiences.

This 2003 text was the first monograph to document and analyse the plays written by Black and Asian women in Britain. The volume explores how Black and Asian women playwrights theatricalize their experiences of migration, displacement, identity, racism and sexism in Britain. Plays by writers such as Tanika Gupta, Winsome Pinnock, Maya Chowdhry and Amrit Wilson, among others - many of whom have had their work produced at key British theatre sites - are discussed in some detail. Other playwrights' work is also briefly explored to suggest the range and scope of contemporary plays. The volume analyses concerns such as geographies of un/belonging, reverse migration (in the form of tourism), sexploitation, arranged marriages, the racialization of sexuality, and asylum seeking as they emerge in the plays, and argues that Black and Asian women playwrights have become constitutive subjects of British theatre.

In this article, we examine the conceptual importance of integrating risk and intersectionality theory for the study of how risk and various forms of inequality intersect and are mutually constitutive. We argue that an intersectional perspective can advance risk research by incorporating more effectively the role of such social categories as gender and race into the analysis of ‘risk’ as an empirical phenomenon. In doing so, the intersectional perspective articulates more clearly the connection between the social construction of risk and, on the one hand, the reproduction of new and complex social inequalities and, on the other, intersections of social class, gender, ethnicity and other social categorisations. We trace the intellectual division between risk and feminist-inspired intersectionality research, showing how these approaches can be aligned to study, for example, risk-based welfare and social policy. We use a discussion of general directions within welfare policy to illustrate how an intersectional perspective can be used to show the ways in which new governance strategies create new divisions and reproduce existing forms of social inequality. We conclude the article with a call for a new research agenda to integrate intersectional frameworks with risk theory in order to provide a more nuanced analysis of the relationship between social inequality and risk.Anna Olofsson, Jens O. Zinn, Gabriele Griffin, Katarina Giritli Nygren, Andreas Cebulla and Kelly Hannah-Moffa

The neoliberalization of higher education in western countries has led to work intensification, projectification, and work-life balance issues for academics. This article draws on interviews with Digital Humanities practitioners in higher education conducted in 2017–2018 in three Nordic countries to introduce the concept of the ‘work-work balance’, an under-researched phenomenon in contemporary academe. The term ‘work-work balance’ refers to the ways in which workers in higher education seek to balance conflicting concurrent work demands made on them. Four such work scenarios emerged from the data: the 50/50 split across different jobs; working across multiple projects simultaneously; occupying multiple functional roles; and conflicting demands within one job. The article argues that work-work balance, or rather imbalance, issues result in the inability of higher education workers to meet the demands put upon them. This raises questions regarding the role of HEIs and research funding regimes in the generation and maintenance of work-work balance scenarios and suggests that work-work balance issues need to be researched further as well as requiring urgent attention from HEIs and research funders.

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.

(2009). Science and the cultural imaginary: the case of Kazuo Ishiguro's Never Let Me Go. Textual Practice: Vol. 23, No. 4, pp. 645-663.