arXiv subjects

Kate Crawford

Publications and source records attributed to Kate Crawford.

7 recordsLinked to original sources

arXiv2025

Generative AI and Creative Work: Narratives, Values, and Impacts

Generative AI has gained a significant foothold in the creative and artistic sectors. In this context, the concept of creative work is influenced by discourses originating from technological stakeholders and mainstream media. The framing of narratives surrounding creativity and artistic production not only reflects a particular vision of culture but also actively contributes to shaping it. In this article, we review online media outlets and analyze the dominant narratives around AI's impact on creative work that they convey. We found that the discourse promotes creativity freed from its material realisation through human labor. The separation of the idea from its material conditions is achieved by automation, which is the driving force behind productive efficiency assessed as the reduction of time taken to produce. And the withdrawal of the skills typically required in the execution of the creative process is seen as a means for democratising creativity. This discourse tends to correspond to the dominant techno-positivist vision and to assert power over the creative economy and culture.

cs.CY↗

arXiv2025

From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate

As the climate crisis deepens, artificial intelligence (AI) has emerged as a contested force: some champion its potential to advance renewable energy, materials discovery, and large-scale emissions monitoring, while others underscore its growing carbon footprint, water consumption, and material resource demands. Much of this debate has concentrated on direct impacts -- energy and water usage in data centers, e-waste from frequent hardware upgrades -- without addressing the significant indirect effects. This paper examines how the problem of Jevons' Paradox applies to AI, whereby efficiency gains may paradoxically spur increased consumption. We argue that understanding these second-order impacts requires an interdisciplinary approach, combining lifecycle assessments with socio-economic analyses. Rebound effects undermine the assumption that improved technical efficiency alone will ensure net reductions in environmental harm. Instead, the trajectory of AI's impact also hinges on business incentives and market logics, governance and policymaking, and broader social and cultural norms. We contend that a narrow focus on direct emissions misrepresents AI's true climate footprint, limiting the scope for meaningful interventions. We conclude with recommendations that address rebound effects and challenge the market-driven imperatives fueling uncontrolled AI growth. By broadening the analysis to include both direct and indirect consequences, we aim to inform a more comprehensive, evidence-based dialogue on AI's role in the climate crisis.

cs.CY↗

arXiv2025

A Critical Field Guide for Working with Machine Learning Datasets

Machine learning datasets are powerful but unwieldy. Despite the fact that large datasets commonly contain problematic material--whether from a technical, legal, or ethical perspective--datasets are valuable resources when handled carefully and critically. A Critical Field Guide for Working with Machine Learning Datasets suggests practical guidance for conscientious dataset stewardship. It offers questions, suggestions, strategies, and resources for working with existing machine learning datasets at every phase of their lifecycle. It combines critical AI theories and applied data science concepts, explained in accessible language. Equipped with this understanding, students, journalists, artists, researchers, and developers can be more capable of avoiding the problems unique to datasets. They can also construct more reliable, robust solutions, or even explore new ways of thinking with machine learning datasets that are more critical and conscientious.

cs.CY↗

arXiv2024

Building Better Datasets: Seven Recommendations for Responsible Design from Dataset Creators

The increasing demand for high-quality datasets in machine learning has raised concerns about the ethical and responsible creation of these datasets. Dataset creators play a crucial role in developing responsible practices, yet their perspectives and expertise have not yet been highlighted in the current literature. In this paper, we bridge this gap by presenting insights from a qualitative study that included interviewing 18 leading dataset creators about the current state of the field. We shed light on the challenges and considerations faced by dataset creators, and our findings underscore the potential for deeper collaboration, knowledge sharing, and collective development. Through a close analysis of their perspectives, we share seven central recommendations for improving responsible dataset creation, including issues such as data quality, documentation, privacy and consent, and how to mitigate potential harms from unintended use cases. By fostering critical reflection and sharing the experiences of dataset creators, we aim to promote responsible dataset creation practices and develop a nuanced understanding of this crucial but often undervalued aspect of machine learning research.

cs.LG↗

arXiv2022

A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication

Datasets are central to training machine learning (ML) models. The ML community has recently made significant improvements to data stewardship and documentation practices across the model development life cycle. However, the act of deprecating, or deleting, datasets has been largely overlooked, and there are currently no standardized approaches for structuring this stage of the dataset life cycle. In this paper, we study the practice of dataset deprecation in ML, identify several cases of datasets that continued to circulate despite having been deprecated, and describe the different technical, legal, ethical, and organizational issues raised by such continuations. We then propose a Dataset Deprecation Framework that includes considerations of risk, mitigation of impact, appeal mechanisms, timeline, post-deprecation protocols, and publication checks that can be adapted and implemented by the ML community. Finally, we propose creating a centralized, sustainable repository system for archiving datasets, tracking dataset modifications or deprecations, and facilitating practices of care and stewardship that can be integrated into research and publication processes.

cs.CY↗

arXiv2021

Datasheets for Datasets

The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains. To address this gap, we propose datasheets for datasets. In the electronics industry, every component, no matter how simple or complex, is accompanied with a datasheet that describes its operating characteristics, test results, recommended uses, and other information. By analogy, we propose that every dataset be accompanied with a datasheet that documents its motivation, composition, collection process, recommended uses, and so on. Datasheets for datasets will facilitate better communication between dataset creators and dataset consumers, and encourage the machine learning community to prioritize transparency and accountability.

cs.DB↗

arXiv2020

Overcoming Failures of Imagination in AI Infused System Development and Deployment

NeurIPS 2020 requested that research paper submissions include impact statements on "potential nefarious uses and the consequences of failure." However, as researchers, practitioners and system designers, a key challenge to anticipating risks is overcoming what Clarke (1962) called 'failures of imagination.' The growing research on bias, fairness, and transparency in computational systems aims to illuminate and mitigate harms, and could thus help inform reflections on possible negative impacts of particular pieces of technical work. The prevalent notion of computational harms -- narrowly construed as either allocational or representational harms -- does not fully capture the open, context dependent, and unobservable nature of harms across the wide range of AI infused systems.The current literature focuses on a small range of examples of harms to motivate algorithmic fixes, overlooking the wider scope of probable harms and the way these harms might affect different stakeholders. The system affordances may also exacerbate harms in unpredictable ways, as they determine stakeholders' control(including of non-users) over how they use and interact with a system output. To effectively assist in anticipating harmful uses, we argue that frameworks of harms must be context-aware and consider a wider range of potential stakeholders, system affordances, as well as viable proxies for assessing harms in the widest sense.

cs.CY↗