Data Ethics and Data Science: An Uneasy Marriage?

This theme issue has the founding ambition of landscaping Data Ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing, and use), algorithms (including AI, artificial agents, machine learning, and robots), and corresponding practices (including responsible innovation, programming, hacking, and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data Ethics builds on the foundation provided by Computer and Information Ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the Level of Abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even the data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations—the interactions among hardware, software, and data—rather than on the variety of digital technologies that enables them. And it emphasises the complexity of the ethical challenges posed by Data Science. Because of such complexity, Data Ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of Data Science and its applications within a consistent, holistic, and inclusive framework. Only as a macroethics Data Ethics will provide the solutions that can maximise the value of Data Science for our societies, for all of us, and for our environments.

Big Data, 2018

Ready data availability, cheap storage capacity, and powerful tools for extracting information from data have the potential to significantly enhance the human condition. However, as with all advanced technologies, this comes with the potential for misuse. Ethical oversight and constraints are needed to ensure that an appropriate balance is reached. Ethical issues involving data may be more challenging than the ethical challenges of some other advanced technologies partly because data and data science are ubiquitous, having the potential to impact all aspects of life, and partly because of their intrinsic complexity. We explore the nature of data, personal data, data ownership, consent and purpose of use, trustworthiness of data as well as of algorithms and of those using the data, and matters of privacy and confidentiality. A checklist is given of topics that need to be considered.

International Scientific Journal for Research, 2023

Data ethics in AI plays a crucial role in ensuring responsible and trustworthy applications of machine learning (ML) and data governance. With the rise of AI technologies, ethical concerns such as bias, fairness, privacy, transparency, and accountability are becoming more significant. This paper explores the ethical challenges in machine learning and data governance, focusing on how they impact the development and deployment of AI systems. It examines the importance of establishing robust frameworks for responsible data science, emphasizing the need for comprehensive policies to manage ethical risks, data protection, and the socio-technical implications of AI. Additionally, it discusses potential solutions and best practices to address these challenges while fostering innovation and societal benefits through AI.

IEEE Security & Privacy, 2019

Statistical journal of the IAOS, 2022

Data are an integral part of the normative world and therefore ethics. With the advent of big data and data science, increased attention has been given to the ethics of artificial intelligence. However, data ethics is broader than that and must now be considered on its own as a field of ethics. In this paper, we make the case for the importance of data ethics and propose a general framework to support organizations in adopting ethical data practices. We provide the examples of statistics and standards as two contexts within which data ethics can be advanced and where advancements have already been made.

Ethics and Information Technology, 2021

This contribution discusses the development of the Data Ethics Decision Aid (DEDA), a framework for reviewing government data projects that considers their social impact, the embedded values and the government’s responsibilities in times of data-driven public management. Drawing from distinct qualitative research approaches, the DEDA framework was developed in an iterative process (2016–2018) and has since then been applied by various Dutch municipalities, the Association of Dutch Municipalities, and the Ministry of General Affairs (NL). We present the DEDA framework as an effective process to moderate case-deliberation and advance the development of responsible data practices. In addition, by thoroughly documenting the deliberation process, the DEDA framework establishes accountability. First, this paper sheds light on the necessity for data ethical case deliberation. Second, it describes the prototypes, the final design of the framework, and its evaluation. After a comparison with...

IEEE Swiss Data Science Conference , 2019

In this paper, we outline the structure and content of a code of ethics for companies engaged in data-based business, i.e. companies whose value propositions strongly depends on using data. The code provides an ethical reference for all people in the organization who are responsible for activities around data. It is primarily targeting private industry, but public organizations and administrations may also use it. A joint industry-academic initiative, involving specialists for ethics as well as for all relevant data-related issues, developed this code.

Journal of Data and Information Science

This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.

This is an introduction to the special issue of “Ethics as Methods: Doing Ethics in the Era of Big Data Research.” Building on a variety of theoretical paradigms (i.e., critical theory, [new] materialism, feminist ethics, theory of cultural techniques) and frameworks (i.e., contextual integrity, deflationary perspective, ethics of care), the Special Issue contributes specific cases and fine-grained conceptual distinctions to ongoing discussions about the ethics in data-driven research. In the second decade of the 21st century, a grand narrative is emerging that posits knowledge derived from data analytics as true, because of the objective qualities of data, their means of collection and analysis, and the sheer size of the data set. The by-product of this grand narrative is that the qualitative aspects of behavior and experience that form the data are diminished, and the human is removed from the process of analysis. This situates data science as a process of analysis performed by the tool, which obscures human decisions in the process. The scholars involved in this Special Issue problematize the assumptions and trends in big data research and point out the crisis in accountability that emerges from using such data to make societal interventions. Our collaborators offer a range of answers to the question of how to configure ethics through a methodological framework in the context of the prevalence of big data, neural networks, and automated, algorithmic governance of much of human socia(bi)lity.

2018

The number of datasets available to legal practitioners, policy makers, scientists, and many other categories of citizens is growing at an unprecedented rate. Ethics-aware data processing has become a pressing need, considering that data are often used within critical decision processes (e.g., staff evaluation, college admission, criminal sentencing). The goal of this paper is to propose a vision for the injection of ethical principles (fairness, non-discrimination, transparency, data protection, diversity, and human interpretability of results) into the data analysis lifecycle (source selection, data integration, and knowledge extraction) so as to make them first-class requirements. In our vision, a comprehensive checklist of ethical desiderata for data protection and processing needs to be developed, along with methods and techniques to ensure and verify that these ethically motivated requirements and related legal norms are fulfilled throughout the data selection and exploration ...