What is Data Ethics?

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.

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.

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).

IEEE Security & Privacy, 2019

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.

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.

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.

GigaScience, 2018

Being asked to write about the ethics of big data is a bit like being asked to write about the ethics of life. Big data is now integral to so many aspects of our daily lives-communication, social interaction, medicine, access to government services, shopping, and navigation. Given this diversity, there is no one-size-fits-all framework for how to ethically manage your data. With that in mind, I present seven ethical values for responsible data use.

IAEME PUBLICATION, 2024

This paper delves into the ethical dimensions of data-centric artificial intelligence (AI), a domain where the quality, management, and use of data play a pivotal role in the development and functioning of AI systems. As AI continues to permeate various sectors including healthcare, finance, and transportation, it becomes increasingly important to balance the substantial benefits of these technologies against potential ethical risks and challenges. The main objectives of this study are to identify and analyze the ethical issues inherent in data-centric AI, propose strategies for balancing these issues with the benefits, and examine existing and potential frameworks for ethical governance. The methodology encompasses a comprehensive literature review, analysis of case studies, and synthesis of ethical frameworks and principles. Key findings reveal that data-centric AI poses unique ethical challenges, particularly concerning privacy, bias, fairness, transparency, and accountability. Realworld case studies illustrate how these challenges manifest and the consequences they entail. The paper highlights the significant advantages of data-centric AI, such as improved efficiency, accuracy, and new capabilities in various domains, while stressing that these benefits often come with ethical trade-offs. Strategies for balancing benefits and risks include the development of robust ethical frameworks, enhanced regulatory and governance mechanisms, and the active engagement of diverse stakeholders in ethical decision-making processes. The paper emphasizes the importance of principles like transparency, fairness, and accountability, proposing their integration into the lifecycle of AI systems. In conclusion, this study underscores the necessity of ongoing ethical reflections in the advancement of data-centric AI. It advocates for a proactive approach in addressing ethical challenges, ensuring that AI development is aligned with societal values and human rights. The paper concludes with a call to action for continued research and collaborative efforts in fostering ethical AI practices.

2022

Awareness and management of ethical issues in data science is becoming increasingly relevant to us all, and a crucial skill for data scientists. Discussion of contemporary issues in collaborative and interdisciplinary spaces is an engaging way to allow data science work to be influenced by those with expertise in philosophy, history, sociology and beyond, and so improve the ability of data scientists to think critically about the ethics of their work. However, opportunities to do so are limited. Data Ethics Club (based at dataethicsclub.com) is a fortnightly discussion group about data science and ethics, whose community-generated resources are hosted in an open online repository. This repository includes a list of data science and ethics materials around multiple topics of interest, alongside processes and templates for leading an online data ethics discussion group. These meetings and materials are designed to reduce the barrier to learning, reflection and critique on data science...