Introduction

Ethical Data Futures—an interdisciplinary course designed to equip students with the foundational skills necessary for navigating the complex moral terrains of modern data practices. As we look into various aspects of data ethics, the course challenges us to move beyond traditional notions of right and wrong, pushing towards a proactive engagement with ethical dilemmas in digital and data-driven environments. This approach is not just academic; it has practical implications for every aspect of our data-saturated lives, from individual privacy to global economic policies.

Parallel to this academic endeavor is my dissertation project, which seeks to explore and apply these ethical concepts within a specific context: the systematic analysis of literature using AI. The goal of this project is not only to contribute to scholarly discussions but also to propose pragmatic solutions to pressing ethical issues identified during my research. By integrating the critical data skills fostered in the Ethical Data Futures course, the project addresses real-world problems, demonstrating the role of ethics in shaping data-driven futures.

The integration of critical data skills is pivotal in both academic and practical contexts. In academia, these skills deepen our understanding and critique of ethical frameworks, enhancing the quality and impact of research outputs. Practically, they guide the responsible development and implementation of technologies, ensuring that data practices contribute positively to society. This blog post aims to link together the theoretical insights from the Ethical Data Futures course with the practical applications observed in my dissertation project, showcasing the indispensable value of ethical foresight in data science.

Overview of the Six Critical Data Skills

1. Ethical Reflection: This skill involves introspection about one’s values and assumptions when interacting with data. It encourages considering the broader impacts of data-related decisions on individuals and communities. Ethical reflection is essential for recognizing our biases and the potential harm they can cause in data interpretation and usage.
2. Ethical Analysis: This is the systematic examination of the moral aspects of data practices. It involves dissecting complex scenarios to identify ethical concerns and stakeholders affected by data decisions. Ethical analysis helps in understanding the implications of data collection, processing, and distribution, ensuring that these processes uphold core ethical standards.
3. Ethical Deliberation: Involving critical discussion and reasoning about the right course of action in data practices, ethical deliberation promotes a collective examination of ethical dilemmas. It supports the development of consensus or informed disagreement, providing a platform for diverse viewpoints to refine ethical decision-making processes.
4. Ethical Evaluation: This skill focuses on assessing the moral dimensions of actions or policies related to data practices. Ethical evaluation is crucial for determining the appropriateness of certain data uses, helping stakeholders weigh the benefits against potential ethical costs.
5. Ethical Contestation: Often in ethical discussions, existing norms and practices are taken for granted. Ethical contestation challenges these norms by questioning their validity and advocating for change where necessary. This skill is vital for driving progress in data ethics, especially in areas where traditional approaches no longer suffice.
6. Ethical Decision-making: The culmination of the ethical process involves making choices that align with one’s ethical conclusions. This skill integrates reflection, analysis, and deliberation to make informed and morally sound decisions in data practices. It ensures that actions taken are not only technically sound but also ethically justified.

These six skills form the backbone of ethical competence in data-related fields, offering a robust framework for addressing the challenges that arise in our increasingly digital world. As we explore these skills in the context of both theoretical coursework and practical dissertation research, their relevance and utility in fostering more just and sustainable data futures become evident.

Application of Critical Data Skills in the Dissertation Project

Ethical Reflection:

My dissertation project explores the implications of AI in the speculative fiction genre, a topic chosen not just for its academic relevance but also for its broader ethical implications in literature and AI interaction. Ethical reflection is crucial in acknowledging my own biases towards technology’s role in narrative creation and its potential societal impact. This ongoing reflection is vital in examining the broader implications of AI on narrative autonomy and cultural representation, a theme prominently discussed in the context of coloniality and data’s role in shaping narratives. As I navigate through the project, continuous ethical reflection ensures the research methodologies and conclusions drawn are conscientiously aligned with equitable and just practices.

Ethical Analysis:

In the dissertation, the ethical analysis extends to scrutinizing the AI tools and data sets employed in the analysis of speculative fiction texts. The project will employ AI to dissect and interpret complex narrative structures—a process examined against ethical considerations discussed in my blog post on AI’s role in literature. The ethical dimension is critical when deploying AI to ensure that the technology does not perpetuate existing biases or misinterpret its creative outputs. A case study from my coursework involves the use of AI in social contexts, reminding us of the technology’s potential biases and the ethical necessity to critically assess AI outputs for any inadvertent perpetuation of stereotypes or cultural misrepresentations.

Ethical Deliberation:

The methodology section of the dissertation, as outlined in the preliminary methodology blog post, reflects ethical deliberation in choosing appropriate AI tools and frameworks that respect the integrity of the literary works analyzed. This process involves comparing different AI models to determine which most respectfully and accurately handles the texts without imposing or omitting significant thematic elements. The course discussions on ethical AI design highlighted the importance of transparency and accountability, guiding the selection and customization of AI tools used in the project.

Ethical Evaluation:

The evaluation phase of the dissertation assesses the moral implications of using AI to interpret human-created narratives. It is here that the project connects with broader ethical discussions from the course, such as those around data colonialism and the preservation of human agency in digital contexts. By critically assessing how AI applications might influence future interpretations of literature, the project aligns with ethical standards that advocate for maintaining human oversight and interpretative authority over AI outputs.

Ethical Contestation:

Throughout the literature review and data analysis phases, ethical contestation involves challenging prevailing norms about the role and limits of AI in humanities research. This critical stance is informed by interdisciplinary insights from data science and humanities, particularly regarding AI’s capability and ethical deployment in understanding complex human outputs like literature.

Ethical Decision-making:

The decision-making process within the dissertation’s methodology explicitly reflects the ethical training from my coursework. Decisions regarding data handling, AI model adjustments, and interpretation of results are all made with an ethical framework in mind, ensuring that the project does not inadvertently harm the literary community or misrepresent the cultural significance of the narratives analyzed. This approach was fortified by group projects and discussions in the course, which emphasized ethical rigor and reflexivity in scholarly research.

By embedding these critical data skills into every phase of the dissertation, the project not only adheres to rigorous academic standards but also advances a responsible and ethical approach to the integration of AI in literary studies. This integration ensures that the exploration of speculative fiction through AI remains respectful, insightful, and culturally sensitive.

Synthesis of Course Learnings and Dissertation Insights

The synthesis of course learnings and dissertation insights forms a crucial nexus where theoretical frameworks meet real-world applications, particularly in the exploration of AI’s role within the literary domain. This section details how the interdisciplinary theories and ethical challenges tackled in the Ethical Data Futures course have profoundly influenced the methodologies and objectives of my dissertation project.

Integration of Course Theories and Practical Experiences

One of the pivotal themes from the Ethical Data Futures course was the discussion around the ethical implications of AI in society, which has directly influenced the foundation of my dissertation. The course underscored the necessity of ethical reflection and analysis when deploying AI technologies, especially in fields traditionally dominated by humanistic inquiry like literary studies. This has led to a conscientious approach in selecting AI tools that are not only technically proficient but also ethically designed to respect and preserve the integrity of literary texts. The practical experiences shared during the course, particularly the case studies on AI biases and the misuse of data, provided a cautionary backdrop that guided the ethical framework of my dissertation. For instance, the discussion on “algorithmic biases” and their societal impacts prompted a critical evaluation of the AI models used for narrative analysis, ensuring they do not perpetuate existing cultural biases.

Insights from Group Discussions

The group discussions in the course were instrumental in shaping the collaborative and interdisciplinary approach of my dissertation. These discussions often centered on the practical challenges of implementing AI in different contexts, offering diverse perspectives that enriched my understanding of AI’s potential and limitations. For example, insights gained from debates on “data colonialism” highlighted the importance of considering who controls AI technologies and who benefits from them, which resonated with the themes of power dynamics in speculative fiction. This influenced my decision to focus on AI’s ability to interpret and represent diverse narratives without imposing a singular, potentially dominant cultural perspective.

Influence of Ethical Challenges

The ethical challenges discussed during the course, such as those involving privacy concerns and the dehumanization potential of AI, were particularly salient. These discussions were not only theoretical but included analytical exercises that applied ethical deliberation to real scenarios, mimicking the complexities I would face in my research. This practice was directly applied to my dissertation’s methodology, where ethical evaluation became a continual process throughout the analysis of literary texts. Each step of AI application—from data collection to interpretation—was scrutinized for potential ethical pitfalls, ensuring that the research remained aligned with the principles of fairness, transparency, and accountability discussed in the course.

Furthermore, the course’s focus on “ethical decision-making” empowers me to take a proactive stance on ethical issues, rather than a reactive one. This proactive approach will be crucial in the dissertation, particularly in designing a methodology that preemptively addresses potential ethical issues in AI analysis, such as ensuring that interpretations remain sensitive to the socio-political contexts of the narratives being analyzed.

Practical Application of Theoretical Insights

The theoretical insights from the course have not only shaped the ethical contours of the dissertation but have also enhanced its analytical depth. For instance, the discussions on the societal implications of AI-driven decisions encouraged a more nuanced analysis of how speculative fiction explores and critiques these very implications. This thematic alignment has allowed the dissertation to not just analyze speculative fiction but to also critically engage with how these narratives comment on the technology that is used to study them.

The integration of course theories and the insights gained from group discussions and ethical challenges have been pivotal in crafting a dissertation approach that is robust, ethically sound, and deeply informed by interdisciplinary scholarship. This synthesis not only reinforces the academic rigor of the research but also ensures that it contributes meaningfully to the ongoing discourse on AI, ethics, and literature.

Conclusion

This exploration of the Ethical Data Futures course, aligned with the rigorous application of its teachings to my dissertation project, underscores the profound impact that structured ethical data skills have on both academic and practical fronts. By interweaving the theoretical frameworks provided by the course with the practical challenges of utilizing AI in literary studies, this project not only navigates the complexities of modern data use but also pioneers methods for their ethical application.

The project commenced with a deep dive into Ethical Reflection, recognizing personal biases and ensuring that the selection of research topics and methodologies remained aligned with ethical scholarly practices. This was followed by Ethical Analysis, where the data sources and AI tools were scrutinized for potential biases and their implications on cultural representation and narrative integrity. Ethical Deliberation allowed for engaging with various methodologies, incorporating insights from academic discussions to refine the research approach. In Ethical Evaluation, the focus was on assessing the moral outcomes of the research, particularly how AI’s interpretation of speculative fiction aligns with or deviates from ethical expectations. Ethical Contestation challenged existing norms and explored innovative pathways for integrating AI into humanities research, advocating for a balance between technological innovation and ethical constraints. Finally, Ethical Decision-making encapsulated the project’s commitment to making informed, ethically-grounded choices throughout the research process.

Reflections on the Value of Ethical Data Skills

The integration of ethical data skills has proven invaluable, not just in conducting research but in shaping future data practices that are sensitive to ethical considerations. These skills foster a research environment where data is not only a source of insight but also a field of ethical inquiry, ensuring that technological advancements enhance, rather than compromise, our societal values. The course and dissertation project together illustrate how ethical training can prepare researchers to navigate the evolving landscape of data use, making them adept at identifying potential ethical issues and proactive in addressing them.

Final Thoughts on the Evolution of Understanding and Approach to Data Ethics

Reflecting on the evolution of my understanding and approach to data ethics, it is clear that the course has been instrumental in shaping a nuanced perspective on the role of ethics in data-intensive research fields. The ability to apply these ethical considerations practically through my dissertation has not only reinforced their importance but has also highlighted the dynamic nature of ethics in practice. As data technologies continue to advance, the ethical frameworks we use must also evolve, adapting to new challenges and opportunities.

Ethical Data Skills / Ambrose Brown, Data, Inequality and Society (2023-24) by is licensed under a