Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls with respect (...) to this requirement by focusing on the significance of contextual and performative factors in the implementation of transparency. We show that human–computer interaction and human-robot interaction literature do not provide clear results with respect to the benefits of transparency for users of artificial intelligence technologies due to the impact of a wide range of contextual factors, including performative aspects. We conclude by integrating the information- and explanation-based approach to transparency with the critical contextual approach, proposing that transparency as required by the General Data Protection Regulation in itself may be insufficient to achieve the positive goals associated with transparency. Instead, we propose to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications. This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking. (shrink)

The development of numerical models to produce realistic prospective scenarios for the evolution of biological diversity is essential. Only integrative impact assessment models are able to take into account the diverse and complex interactions embedded in social-ecological systems. The knowledge used is objective, the procedure of their integration is rigorous and the data massive. Nevertheless, the technical choices made at each stage of the development of models and scenarios are mostly circumstantial, depending on both the skills of modellers on a (...) project and the means available to them. In the end, the scenarios selected and the way they are simulated limit the futures explored, and the options offered to decision makers and stakeholders to act. The ethical implications of these circumstantial choices are generally not documented, explained or even perceived by modellers. Applied ethics propose a coherent set of principles to guide a critical reflection on the social and environmental consequences of integrative modelling and simulation of biodiversity scenarios. Such reflection should be incorporated into the actual modelling process, in a broad participatory framework, and foster effective moral involvement of modellers, policy-makers and stakeholders, in preference to the application of fixed ethical rules. (shrink)

Organizations increasingly rely on algorithm-based HR decision-making to monitor their employees. This trend is reinforced by the technology industry claiming that its decision-making tools are efficient and objective, downplaying their potential biases. In our manuscript, we identify an important challenge arising from the efficiency-driven logic of algorithm-based HR decision-making, namely that it may shift the delicate balance between employees’ personal integrity and compliance more in the direction of compliance. We suggest that critical data literacy, ethical awareness, the use of participatory (...) design methods, and private regulatory regimes within civil society can help overcome these challenges. Our paper contributes to literature on workplace monitoring, critical data studies, personal integrity, and literature at the intersection between HR management and corporate responsibility. (shrink)

The growing presence of smart devices in our lives turns all of society into something largely unknown to us. The strategy of demanding transparency stems from the desire to reduce the ignorance to which this automated society seems to condemn us. An evaluation of this strategy first requires that we distinguish the different types of non-transparency. Once we reveal the limits of the transparency needed to confront these devices, the article examines the alternative strategy of explainable artificial intelligence and concludes (...) with the idea that these types of complex realities exceed individual capacities and are only comprehensible in a collective fashion. (shrink)

Scholars and practitioners across domains are increasingly concerned with algorithmic transparency and opacity, interrogating the values and assumptions embedded in automated, black-boxed systems, particularly in user-generated content platforms. I report from an ethnography of infrastructure in Wikipedia to discuss an often understudied aspect of this topic: the local, contextual, learned expertise involved in participating in a highly automated social–technical environment. Today, the organizational culture of Wikipedia is deeply intertwined with various data-driven algorithmic systems, which Wikipedians rely on to help manage (...) and govern the “anyone can edit” encyclopedia at a massive scale. These bots, scripts, tools, plugins, and dashboards make Wikipedia more efficient for those who know how to work with them, but like all organizational culture, newcomers must learn them if they want to fully participate. I illustrate how cultural and organizational expertise is enacted around algorithmic agents by discussing two autoethnographic vignettes, which relate my personal experience as a veteran in Wikipedia. I present thick descriptions of how governance and gatekeeping practices are articulated through and in alignment with these automated infrastructures. Over the past 15 years, Wikipedian veterans and administrators have made specific decisions to support administrative and editorial workflows with automation in particular ways and not others. I use these cases of Wikipedia’s bot-supported bureaucracy to discuss several issues in the fields of critical algorithms studies; critical data studies; and fairness, accountability, and transparency in machine learning—most principally arguing that scholarship and practice must go beyond trying to “open up the black box” of such systems and also examine sociocultural processes like newcomer socialization. (shrink)

Recent work on Big Data and analytics reveals a tension between analyzing the role of emerging objects and processes in existing systems and using those same objects and processes to create new and purposeful forms of action. While the field of science and technology studies has had considerable success in pursuing the former goal, as Halford and Savage argue, there is an ongoing need to discover or invent ways to “do Big Data analytics differently.” In this commentary, I suggest that (...) attempts to produce new ways of working with Big Data and analytics might be hindered by how science and technology studies-influenced scholars have conceptualized assemblages. While these scholars have foregrounded objects’ relations within existing assemblages, new materialist philosophers draw attention to properties of objects that transcend those relations and might indicate opportunities for more creative or generative uses of Big Data and analytics. (shrink)

Since the inception of recorded music there has been a need for standards and reliability across sound formats and listening environments. The role of the audio mastering engineer is prestigious and akin to a craft expert combining scientific knowledge, musical learning, manual precision and skill, and an awareness of cultural fashions and creative labour. With the advent of algorithms, big data and machine learning, loosely termed artificial intelligence in this creative sector, there is now the possibility of automating human audio (...) mastering processes and radically disrupting mastering careers. The emergence of dedicated products and services in artificial intelligence-driven audio mastering poses profound questions for the future of the music industry, already having faced significant challenges due to the digitalization of music over the past decades. The research reports on qualitative and ethnographic inquiry with audio mastering engineers on the automation of their expertise and the potential for artificial intelligence to augment or replace aspects of their workflows. Investigating audio mastering engineers' awareness of artificial intelligence, the research probes the importance of criticality in their labour. The research identifies intuitive performance and critical listening as areas where human ingenuity and communication pose problems for simulation. Affective labour disrupts speculation of algorithmic domination by highlighting the pragmatic strategies available for humans to adapt and augment digital technologies. (shrink)

The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (ii) the kind of enquiry (...) that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally. (shrink)