Big Data and Society

In this article, we present new empirical evidence to demonstrate the severe limitations of existing machine learning content moderation methods to keep pace with, let alone stay ahead of, hateful language online. Building on the collaborative coding project “AI4Dignity” we outline the ambiguities and complexities of annotating problematic text in AI-assisted moderation systems. We diagnose the shortcomings of the content moderation and natural language processing approach as emerging from a broader epistemological trapping wrapped in the liberal-modern idea of “the human”. Presenting a decolonial critique of the “human vs machine” conundrum and drawing attention to the structuring effects of coloniality on extreme speech, we propose “ethical scaling” to highlight moderation process as political praxis. As a normative framework for platform governance, ethical scaling calls for a transparent, reflexive, and replicable process of iteration for content moderation with community participation and global parity, which should evolve in conjunction with addressing algorithmic amplification of divisive content and resource allocation for content moderation.

Sahana Udupa

Antonis Maronikolakis

Axel Wisiorek

Big Data & Society

Ethical scaling for content moderation: Extreme speech and the (in)significance of artificial intelligence

内容审核的伦理扩展：极端言论与人工智能的（不）重要性

Proponents of generative AI tools claim they will supplement, even replace, the work of cultural production. This raises questions about the politics of visibility: what kinds of stories do these tools tend to generate, and what do they generally not? Do these tools match the kind of diversity of representation that marginalized populations and non-normative communities have fought to secure in publishing and broadcast media? I tested three widely available generative AI tools with prompts designed to reveal these normative assumptions; I prompted the tools multiple times with each, to track the diversity of the outputs to the same query. I demonstrate that, as currently designed and trained, generative AI tools tend to reproduce normative identities and narratives, rarely representing less common arrangements and perspectives. When they do generate variety, it is often narrow, maintaining deeper normative assumptions in what remains absent.

Tarleton Gillespie

Generative AI and the politics of visibility

生成式人工智能与可见性政治

Chinese cities are increasingly using digital technologies to address urban problems and govern society. However, little is known about how this digital transition has been implemented. This study explores the introduction of digital governance in Shenzhen, one of China's most advanced smart cities. We show that, at the local level, the successful implementation of digital systems faces numerous hurdles in long-standing data management and bureaucratic practices that are at least as challenging as the technical problems. Furthermore, the study finds that the digital systems in Shenzhen entail a creeping centralisation of data that potentially turns lower administrative government units into mere users of the city-level smart platforms rather than being in control of their own data resources. Smart city development and big data ambitions thereby imply shifting stakeholder relations at the local level and also pull non-governmental stakeholders, such as information technology companies and research institutions, closer to new data flows and smart governance systems. The findings add to the discussion of big data-driven smart systems and their implications for governance processes in an authoritarian context.

Jelena Große-Bley

Genia Kostka

Big Data Dreams and Reality in Shenzhen: An Investigation of Smart City Implementation in China

深圳大数据梦想与现实：中国智慧城市实施调查

Scholars are increasingly concerned about social biases in facial analysis systems, particularly with regard to the tangible consequences of misidentification of marginalized groups. However, few have examined how automated facial analysis technologies intersect with the historical genealogy of racialized gender—the gender binary and its classification as a highly racialized tool of colonial power and control. In this paper, we introduce the concept of auto-essentialization: the use of automated technologies to re-inscribe the essential notions of difference that were established under colonial rule. We consider how the face has emerged as a legitimate site of gender classification, despite being historically tied to projects of racial domination. We examine the history of gendering the face and body, from colonial projects aimed at disciplining bodies which do not fit within the European gender binary, to sexology's role in normalizing that binary, to physiognomic practices that ascribed notions of inferiority to non-European groups and women. We argue that the contemporary auto-essentialization of gender via the face is both racialized and trans-exclusive: it asserts a fixed gender binary and it elevates the white face as the ultimate model of gender difference. We demonstrate that imperialist ideologies are reflected in modern automated facial analysis tools in computer vision through two case studies: (1) commercial gender classification and (2) the security of both small-scale (women-only online platforms) and large-scale (national borders) spaces. Thus, we posit a rethinking of ethical attention to these systems: not as immature and novel, but as mature instantiations of much older technologies.

Morgan Klaus Scheuerman

Madeleine Pape

Alex Hanna

Auto-essentialization: Gender in automated facial analysis as extended colonial project

自动本质化：自动化面部分析中的性别作为扩展的殖民项目

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1) opacity as intentional corporate or state secrecy, (2) opacity as technical illiteracy, and (3) an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented), and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm.

Jenna Burrell

How the machine ‘thinks’: Understanding opacity in machine learning algorithms

机器如何“思考”：理解机器学习算法中的不透明性

The emerging ecosystem of artificial intelligence (AI) ethics and governance auditing has grown rapidly in recent years in anticipation of impending regulatory efforts that encourage both internal and external auditing. Yet, there is limited understanding of this evolving landscape. We conduct an interview-based study of 34 individuals in the AI ethics auditing ecosystem across seven countries to examine the motivations, key auditing activities, and challenges associated with AI ethics auditing in the private sector. We find that AI ethics audits follow financial auditing stages, but tend to lack robust stakeholder involvement, measurement of success, and external reporting. Audits are hyper-focused on technically oriented AI ethics principles of bias, privacy, and explainability, to the exclusion of other principles and socio-technical approaches, reflecting a regulatory emphasis on technical risk management. Auditors face challenges, including competing demands across interdisciplinary functions, firm resource and staffing constraints, lack of technical and data infrastructure to enable auditing, and significant ambiguity in interpreting regulations and standards given limited (or absent) best practices and tractable regulatory guidance. Despite these roadblocks, AI ethics and governance auditors are playing a critical role in the early ecosystem: building auditing frameworks, interpreting regulations, curating practices, and sharing learnings with auditees, regulators, and other stakeholders.

Daniel S Schiff

Stephanie Kelley

Javier Camacho Ibáñez

The emergence of artificial intelligence ethics auditing

人工智能伦理审计的兴起

The COVID-19 pandemic response in the UK, as in other countries, drew heavily on health and social care data, making its utility extremely visible as necessary for timely government decision-making and planning. The urgency created by the crisis, however, meant that additional data collection and sharing under emergency legislation was implemented with minimal public consultation. To understand the public perception of these new data measures and initiatives, three citizens’ juries took place in the spring of 2021. This article reports on qualitative observations of the small group deliberations from these juries. The analysis shows that jurors frequently drew on normative discourses of transparency and trust in discussions, and the different roles they were assumed to fulfil. Transparency was expected to offer greater visibility into the organisations involved in health and social care data sharing, but this was made difficult by the increased complexity of the health data economy. Transparency into the political justifications for additional health data collection was important for jurors. The utilitarian narratives used by the government were considered problematic, restricting opportunities for individuals to express concerns and leading to cynicism. The findings will be situated with the critical literature on visibility practices to highlight the need to unpick what the promise of transparency and trust offers to the public and how it links to power and control. Lastly, it will examine what the deliberations around transparency mean for wider policy on health and social care data-sharing.

Louise Laverty

Elisa Jones

Niels Peek

Malcolm Oswald

Kyle Bozentko

Sarah Atwood

Sabine van der Veer

The power and promise of transparency: Perspectives from citizens’ juries of pandemic health data sharing

透明度的力量与前景：来自公民陪审团对疫情健康数据共享的视角

Artificial intelligence has become an issue in public policy. Multiple documents issued by public sector actors link artificial intelligence to a wide range of issues, problems or goals and propose corresponding measures and interventions. While there has been substantial research on national and supranational artificial intelligence strategies and regulations, this article is interested in unpacking the processes and priorities of artificial intelligence policy in the making. Conceptually, this article takes a controversy studies lens onto artificial intelligence policy, and complements this with concepts and insights from policy studies. Empirically, we investigate the emergence of German artificial intelligence policy based on content analyses of policy documents and expert interviews. The findings reveal a late, but then powerful institutionalisation of artificial intelligence policy in German federal politics. Artificial intelligence policy in Germany focuses on funding research and supporting industry actors in networked configurations, much more than addressing societal concerns on inequality, discrimination or political economy. With regard to controversies, we observe that German policy is evading controversies by normalising artificial intelligence both with regard to taking artificial intelligence integration in all sectors of society for granted, as well as by accommodating artificial intelligence issues into the routines and institutions of German policy.

Laura Liebig

Anna Jobin

Licinia Güttel

Christian Katzenbach

Situating AI policy: Controversies covered and the normalisation of AI

定位人工智能政策：争议覆盖与人工智能的正常化

As a mechanism for addressing data-related harms, fairness has been subjected to considerable criticism, seen as failing to acknowledge the power relationships that produce said harms, or as a ‘floating signifier’ devoid of specific meaning. In contrast to fairness, it is argued that equity does a better job of recognising data-related harms. Criticisms such as these emerge in specific cultural contexts and rarely acknowledge everyday understandings of terms and concepts. This paper engages with these criticisms, drawing on research exploring how 112 UK residents perceive data uses in specific public service organisations. We found that participants perceive fairness and equity to be interwoven with each other, a finding which shows that who gets to define what is fair matters and which challenges assumptions about what does and does not constitute thinking and talking data politics. We conclude by proposing that linking fairness with equity can be seen as a kind of everyday data solidarity.

Helen Kennedy

Hannah Ditchfield

Susan Oman

Jo Bates

Itzelle Medina Perea

Monika Fratczak

Mark Taylor

How people connect fairness and equity when they talk about data uses

人们在讨论数据使用时如何将公平与公正联系起来

Advancements in generative artificial intelligence have led to a rapid proliferation of machine learning models capable of synthesizing text, images, sounds, and other kinds of content. While the increasing realism of synthetic content stokes fears about misinformation and triggers debates around intellectual property, generative models are adopted across creative industries and synthetic media seep into cultural production. Qualitative research in the social and human sciences has dedicated comparatively little attention to this category of machine learning, particularly in terms of what types of novel research methodology they both demand and facilitate. In this article, we propose a methodological approach for the qualitative study of generative models grounded on the experimentation with field devices which we call synthetic ethnography. Synthetic ethnography is not simply a qualitative research methodology applied to the study of the social and cultural contexts developing around generative artificial intelligence, but also strives to envision practical and experimental ways to repurpose these models as research tools in their own right. After briefly introducing generative models and revisiting the trajectory of digital ethnographic research, we discuss three case studies for which the authors have developed experimental field devices to study different aspects of generative artificial intelligence ethnographically. In the conclusion, we derive a broader methodological proposal from these case studies, arguing that synthetic ethnography facilitates insights into how the algorithmic processes, training datasets and latent spaces behind these systems modulate bias, reconfigure agency, and challenge epistemological categories.

Gabriele de Seta

Matti Pohjonen

Aleksi Knuutila

Synthetic ethnography: Field devices for the qualitative study of generative models

合成民族志：生成模型定性研究的田野工具

Abbreviation	Big. Data. Soc.
Journal Impact	6.38
Quartiles(Global)	SOCIAL SCIENCES, INTERDISCIPLINARY(Q1)

ISSN	2053-9517
h-index	69
Top Journals	Yes

Publication Information	Publisher: SAGE Publications Ltd，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2014，Proportion of original research papers: 96.10%，Self Citation Rate:10.80%， Gold OA Rate: 91.73%
Journal Fees	$2000Details
Average review cycle	网友分享经验：11 Weeks

Big Data and Society

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