Introduction

Artificial intelligence (AI) is making waves in the field of literary studies, especially within the genre of speculative fiction. This genre, which often delves into the impacts of future tech and societal shifts, is an apt playground for AI analysis. But it’s not as simple as it sounds. AI tools like Large Language Models (LLMs) are powerful, capable of sifting through mountains of literature, identifying themes, and charting narrative structures faster and wider than any human scholar. Yet, we can’t discount the nuance and depth that human insight brings to the table—picking up on the subtleties of language, culture, and emotion. This leads us to consider how AI and human expertise can work together to unpack speculative texts.

In this context, the lessons learned from the Inclusive Society course have been particularly insightful. The course emphasized data activism, ethical AI usage, and the push for social equity—topics that are incredibly relevant to AI’s role in humanities. By examining how AI can be used ethically and efficiently in literary analysis, we aim to deepen our understanding of speculative fiction and bring to light the broader implications of technology in our society. This dual focus allows us to explore how AI can act as a tool for social justice, spotlighting and questioning the power structures and potential inequalities reflected in speculative futures.

Ethical Considerations and Data Justice

Data Activism and AI

When it comes to literary analysis, using artificial intelligence (AI) responsibly is key, especially from the perspective of data activism. This is all about using data and tech to bring about social change, with a focus on being transparent, accountable, and inclusive. So, in the world of literary studies, this means we should be using AI tools in a way that doesn’t accidentally back up societal biases that are often challenged in speculative fiction.

The idea of data justice is central to the ethical use of AI in literary analysis. This involves ensuring fairness in how data is collected, analyzed, and used. This becomes particularly important when AI systems like Large Language Models (LLMs) are used to process literary works. We need to be careful when designing these systems and keep a close eye on them to make sure they’re not unintentionally reinforcing stereotypes or inequalities through their algorithms. For example, when looking at speculative fiction—a genre that often delves into themes of dystopia, utopia, and social reform—it’s crucial that the AI appreciates the variety of perspectives and cultural contexts that shape these stories.

Also, when we talk about using AI ethically in literary studies, we have to acknowledge what AI can’t do. It’s not so great at understanding complex human experiences and cultural nuances. The Inclusive Society course highlights how important it is to use diverse datasets and perspectives to reduce biases. AI systems need to learn from a range of literary voices and critiques, not just the mainstream ones, to really get a handle on the literature. This way of working is in line with the aims of data activism, using AI not just for analysis, but also to highlight voices and themes in literature that might otherwise be overlooked.

AI and Representation in Literature

AI’s role in examining representation in literature is both promising and intricate. Speculative fiction often mirrors society, showcasing alternate realities that probe and echo the complexity of our world, including aspects of race, gender, and class. AI can pinpoint patterns of representation across large volumes of text—patterns that might be overlooked by even the most meticulous human scholars. For instance, AI can measure the occurrence and context of gendered themes, ethnic backgrounds, and social classes within speculative fiction narratives, offering factual data that aids wider analytical debates.

Yet, the ability of AI to detect and correctly interpret biases in literature is a work in progress. While AI can underscore what is expressly stated, it often grapples with the nuances of implied biases and the deeper cultural meanings embedded in texts. Literary AI tools might tally occurrences and co-occurrences of words and phrases linked to different identities, but grasping how these representations influence and echo societal attitudes calls for a level of analysis that AI alone can’t deliver. This is when human oversight becomes vital—interpreters who can situate AI outcomes within broader societal and cultural frameworks.

The efficacy of AI in managing diverse identities and narratives in speculative fiction largely hinges on the training data it’s fed and the interpretative frameworks applied to its results. As touched upon in the Inclusive Society course, embedding ethical considerations and cultural competencies into AI systems is key. This integration ensures that AI tools used in literary analysis positively contribute to understanding diversity and complexity in speculative fiction, rather than oversimplifying or distorting it.

While AI provides potent tools for broadening our comprehension of representation in speculative fiction, it needs to be steered by stringent ethical norms and a dedication to data justice. By doing so, AI can facilitate a more inclusive and thorough exploration of how literature envisions and questions the potential of human societies.

Interdisciplinary Insights and Public Good

Merging Literary Criticism with AI Technology

The fusion of artificial intelligence (AI) analytics with traditional literary criticism represents a cutting-edge interdisciplinary approach, particularly suited to the analysis of speculative fiction. This genre, with its rich tapestry of alternative worlds and future visions, provides fertile ground for such a synthesis. AI, with its ability to process and analyze large datasets swiftly, can uncover patterns and themes that might escape conventional human analysis due to the sheer volume of data or inherent biases.

By integrating AI tools like text mining and sentiment analysis, researchers can quickly identify prevalent themes across multiple works of speculative fiction, quantifying how often and in what contexts certain societal issues are discussed. This quantitative analysis complements traditional literary criticism, which delves into the qualitative nuances of how these themes are treated in terms of narrative complexity, character development, and stylistic choices. Together, this interdisciplinary approach not only broadens but deepens our understanding of speculative fiction, offering new insights into how literature reflects and critiques societal trends.

Speculative Fiction on Economic and Social Reforms

Speculative fiction often explores radical social and economic reforms, such as Universal Basic Income (UBI), serving as a narrative playground to simulate their potential impacts. Works like Philip K. Dick’s “The Variable Man” and Ursula K. Le Guin’s “The Dispossessed” provide scenarios where economic theories and social policies are pushed to their logical and sometimes extreme conclusions. AI’s role in dissecting these narratives is to highlight and quantify the presence and treatment of these themes across different contexts and authors.

For instance, AI can help trace the evolution of economic themes in speculative fiction over decades, identifying shifts in focus from post-war economic recovery to contemporary concerns about automation and job displacement. This capability allows researchers to correlate these thematic trends with real-world economic changes and technological advancements, enriching the discourse on how speculative fiction not only mirrors but also influences public understanding and sentiment towards these reforms.

Conclusion

The integration of AI into the study of speculative fiction represents a significant advancement in literary studies, marrying the quantitative reach of technology with the qualitative depth of traditional analysis. This interdisciplinary approach has proven particularly effective in expanding our comprehension of societal themes depicted in speculative fiction—themes that resonate deeply with contemporary issues of economic inequality and social justice.

Our exploration underscores the potential of AI to enhance our understanding of literature as a reflection of societal anxieties and aspirations. By leveraging AI, we can uncover patterns and connections that might otherwise remain obscured, offering fresh perspectives on the societal implications of speculative narratives. This research not only sheds light on the literary landscape but also informs broader discussions on the role of technology in shaping society.

Furthermore, this project aligns with the insights gained from the Inclusive Society course, emphasizing the importance of ethical considerations, social equity, and the transformative potential of merging diverse disciplines. As we continue to navigate the complexities of technology and its impact on society, the lessons drawn from speculative fiction through AI-assisted analysis will be invaluable. They provide a roadmap for understanding and navigating the potential futures we might face, guided by the interdisciplinary insights that blend the best of humanistic scholarship and technological innovation.

Intersections of AI and Humanity / Ambrose Brown, Data, Inequality and Society (2023-24) by is licensed under a