Research

 

You will find a complete range of our peer-reviewed monographs, multi-authored and edited works, including original scholarly research across the social sciences and aligned disciplines. We publish long and short form research and you can browse the Bristol University Press and Policy Press archive.

Policy Press also publishes policy reviews and polemic work which aim to challenge policy and practice in certain fields. These books have a practitioner in mind and are practical, accessible in style, as well as being academically sound and referenced.
 

Books: Research

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This chapter explores how predictive modelling in Human-Aided AI systems fundamentally relies on feedback loops, making them cybernetic in nature. As ‘cybernetic AI’, contemporary AI systems not only learn from human behaviour but also shape and govern it through continuous feedback. The chapter connects present examples such as targeting algorithms to the legacy of cybernetics in military history. By linking AI to the historical development of cybernetics, the chapter argues that predictive AI systems reinforce societal inequalities and exert control through manipulation and discrimination. It concludes by examining the risks of an increasingly pervasive control apparatus, echoing concerns about cybernetic governance from mid-20th-century theorists like Norbert Wiener.

Open access

This chapter explores AI’s predictive capabilities, emphasising predictive analytics as a core aspect of its contemporary impact. Relying on extensive user data, modern AI systems generate models that allow companies to infer unknown attributes or anticipate future behaviour. This power is not only a business model but a central mechanism through which social influence and control are exerted across finance, advertising, employment and beyond. By examining targeted advertising and predictive analytics, the chapter highlights the political risks associated with predictive AI, particularly regarding its impact on privacy and autonomy. The discussion challenges the prevailing ethical frameworks and emphasises the necessity for systemic responses to mitigate prediction power’s influence on individuals and societies.

Open access

This chapter addresses the ethical issue of bias in AI, particularly in cybernetic AI systems, highlighting how biases can emerge and become entrenched through feedback loops between machine learning models and social realities. The concept of bias is explained as a systemic issue, not limited to individual cases, but visible when comparing aggregate outcomes across different groups. The chapter distinguishes between first-degree bias, which is rooted in the inherent purposes of AI systems like the Correctional Offender Management Profiling for Alternative Sanctions system (COMPAS), and second-degree bias, which arises from the biased feasibility of feedback loops that make it harder to correct errors for marginalised groups. The chapter makes the central argument that predictive models are performative.

Open access

This chapter explores the concept of collective responsibility in the context of Human-Aided AI and predictive systems, highlighting how individual behaviours contribute to large-scale societal impacts, such as the power imbalances created by Big Tech. It argues that ethical discussions should move beyond the individual moral agent, focusing instead on collective responsibility for the societal effects of data-sharing practices. Drawing on Iris Marion Young’s theory of political responsibility, the chapter advocates for a forward-looking approach, urging political action and collective regulation to address the ethical challenges posed by digital technologies and AI.

Open access

This chapter presents a manifesto for a power-aware ethics of AI, advocating for a shift from individual-centric ethical approaches to a collective and political perspective. It argues that AI ethics must engage critically with power structures, social inequalities and the economic forces driving AI technologies. The Manifesto calls for an alliance between AI ethics and social philosophies like intersectionality and critical theory, emphasising the need for political regulation, public engagement and a proactive, collective responsibility that confronts the societal impacts of AI.

Open access

This chapter delves into the implications of predictive analytics in terms of the epistemology and philosophy of statistics. The chapter introduces the concept of the ‘prediction gap’, which highlights the ethical issue arising when probabilistic predictions about individuals lead to deterministic decisions that undermine their autonomy. It also contrasts classical frequentist statistics with Bayesian approaches, noting the shift towards subjective, action-oriented knowledge in machine learning. This shift raises fundamental ethical concerns about treating individuals based on statistical predictions, which undermines human dignity and free will. The chapter calls for an urgent ethical debate to protect individuals from the consequences of predictive reasoning.

Open access

This chapter explores the role of Human-Aided AI in digital media culture, where networked infrastructure and interface design have centralised data capture from diverse social interactions. Over two decades, this transformation has paved the way for data-driven, machine learning–based AI, framing AI’s development as a media history shaped by power dynamics and data extraction. The chapter explores interface design’s role in ‘digital counter-enlightenment’, whereby user-centred interfaces, through sealed surfaces, real-time data tracking and subtle behavioural nudges, work to undermine an instrumental understanding of the devices. This design culture – often infantilising and promoting user dependency – reflects a strategy that transforms users into data sources within AI-driven networks. The chapter argues for understanding this culture’s ethical and political impact, highlighting its role in shaping AI’s pervasive influence and economic priorities in modern society.

Open access
Power, Critique, Responsibility

Available open access digitally under CC-BY-NC-ND licence.

In a world where artificial intelligence increasingly influences the fabric of our daily lives, this accessible book offers a critical examination of AI and its deep entanglement with power structures. Rather than focusing on doomsday scenarios, it emphasizes how AI impacts our everyday interactions and social norms in ways that fundamentally reshape society. By examining the different forms of exploitation and manipulation in the relationship between humans and AI, the book advocates for collective responsibility, better regulation and systemic change.

This is a resounding manifesto for rethinking AI ethics through a power-aware lens. With detailed analysis of real-world examples and technological insights, it is an essential reading for anyone invested in the future of AI policy, scholarly critique and societal integration.

Open access

This chapter introduces Human-Aided AI as a framework for understanding AI systems as sociotechnical networks fundamentally reliant on human involvement. Moving beyond the isolated ‘genius AI’ model, Human-Aided AI views these systems as inherently collaborative, requiring human input through data generation, digital labour and user interaction. The chapter emphasises how business models drive continuous data extraction, interface design leverages human cognitive engagement, and global economic inequalities sustain labour exploitation, collectively shaping AI’s core functionality. By situating AI within these broader political contexts, Human-Aided AI advocates for an understanding of AI’s impact that fully accounts for its pervasive reliance on human agency and far-reaching societal implications.

Open access