What is data ethics

2019

Ethics-related aspects are becoming prominent in data management, thus the current processes for searching, querying, or analyzing data should be designed is such a way as to take into account the social problems their outcomes could bring about. In this paper we provide reflections on the unavoidable ethical facets entailed by all the steps of the information life-cycle, including source selection, knowledge extraction, data integration and data analysis. Such reflections motivated us to organize the First International Workshop on Processing Information Ethically (PIE).

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.

IEEE Security & Privacy, 2019

Springer eBooks, 2023

Civil society around the world has called for data-driven companies to take their responsibility seriously and to work on becoming more fair, transparent, accountable and trustworthy, to name just a few of the goals that have been set. Data ethics has been put forward as a promising strategy to make this happen. However, data ethics is a fuzzy concept that can mean different things to different people. This chapter is therefore dedicated to explaining data ethics from different angles. It will first look into data ethics as an academic discipline and illustrate how some of these academic viewpoints trickle down in the debate on data science and AI. Next, it will focus on how data ethics has been put forward as a regulatory strategy by datadriven companies. It will look into the relation between law and ethics, because if in this entrepreneurial context data ethics is not properly embedded, it can be used as an escape from legal regulation. This chapter will end with a reflection on the future relation of data ethics and data science and provide some discussion questions to instigate further debate.

2020

This class introduces philosophical ethics through contemporary issues concerning computing technology. Topics include algorithmic bias and fairness, data privacy, cybersecurity, surveillance, free speech, automation, and artificial intelligence. A running theme will be how technologies are situated within social and political systems, and what kinds of ethical implications that brings. In her recent book Weapons of Math Destruction, Cathy O'Neil writes: Data is not going away. Nor are computers-much less mathematics. Predictive models are, increasingly, the tools we will be relying on to run our institutions, deploy our resources, and manage our lives. But as I've tried to show throughout this book, these models are constructed not just from data but from the choices we make about which data to pay attention to and which to leave out. Those choices are not just about logistics, profits, and efficiency. They are fundamentally moral.

Research into the ethics of artificial intelligence is often categorized into two subareas-robot ethics and machine ethics. Many of the definitions and classifications of the subject matter of these subfields, as found in the literature, are conflated, which I seek to rectify. In this essay, I infer that using the term 'machine ethics' is too broad and glosses over issues that the term computational ethics best describes. I show that the subject of inquiry of computational ethics is of great value and indeed is an important frontier in developing ethical artificial intelligence systems (AIS). I also show that computational is a distinct, often neglected field in the ethics of AI. In contrast to much of the literature, I argue that the appellation 'machine ethics' does not sufficiently capture the entire project of embedding ethics into AI/S and hence the need for computational ethics. This essay is unique for two reasons; first, it offers a philosophical analysis of the subject of computational ethics that is not found in the literature. Second, it offers a finely grained analysis that shows the thematic distinction among robot ethics, machine ethics and computational ethics.

We are designing Artificial Intelligence and Big Data Algorithms (machine learning) to process aspects of data about ourselves that most of us are not even aware are being collected. The design aspects of these systems concerns 10 areas that will likely affect our world and our society for generations to come. To help consider the 10 design areas, we suggest applying Prof. Daniel Solove's taxonomy on privacy to the design of AI and Big Data : process, data collection and data distribution.

New Media & Society, 2023

Research on AI ethics tends to examine the subject through philosophical, legal, or technical perspectives, largely neglecting the sociocultural one. This literature also predominantly focuses on Europe and the United States. Addressing these gaps, this article explores how data scientists justify and explain the ethics of their algorithmic work. Based on a pragmatist social analysis, and of 60 semi-structured interviews with Israeli data scientists, we ask: how do data scientists understand, interpret, and depict algorithmic ethics? And what ideologies, discourses, and worldviews shape algorithmic ethics? Our findings point to three dominant moral logics: (1) ethics as a personal endeavor; (2) ethics as hindering progress; and (3) ethics as a commodity. We show that while data science is a nascent profession, these moral logics originate from the technolibertarian culture of its parent profession-engineering. Finally, we discuss the potential of these moral logics to mature into a more formal, agreed-upon moral regime.

2018

Algorithms and digital systems are increasingly taking the role of 'artificial persons' – hence becoming both the subjects and objects of regulation and policing.This summary paper provides an overview of the emerging 'digital ethics' field from a system design and engineering perspective. The objective is to lay out the critical questions and the current research directions that are likely to shape a new 'ethics engineering' profession, which will have significant impact across all sectors.

2020 7th Swiss Conference on Data Science (SDS), 2020

Up to date, more than 80 codes exist for handling ethical risks of artificial intelligence and big data. In this paper, we analyse where those codes converge and where they differ. Based on an in-depth analysis of 20 guidelines, we identify three procedural action types (1. control and document, 2. inform, 3. assign responsibility) as well as four clusters of ethical values whose promotion or protection is supported by the procedural activities. We achieve a synthesis of previous approaches with a framework of seven principles, combining the four principles of biomedical ethics with three distinct procedural principles: control, transparency and accountability.