Data and the FAIR Principles

Lesson 2: Ethics

Overview

Teaching: Self Paced min
Exercises: min
Questions
  • What ethics policies and issues surround privacy, data sharing, and the use of data?

Objectives
  • Introduction to ethics surrounding privacy, data publication, data sharing, and algorithmic decision-making

Introduction

This lesson links to externally available information to introduce the student to ethics surrounding privacy, data sharing, and algorithmic decision-making.

Selected External Lesson Material

Online courses:

Abstract: As patients, we care about the privacy of our medical record; but as patients, we also wish to benefit from the analysis of data in medical records. As citizens, we want a fair trial before being punished for a crime; but as citizens, we want to stop terrorists before they attack us. As decision-makers, we value the advice we get from data-driven algorithms; but as decision-makers, we also worry about unintended bias. Many data scientists learn the tools of the trade and get down to work right away, without appreciating the possible consequences of their work. This course focused on ethics specifically related to data science will provide you with the framework to analyze these concerns. This framework is based on ethics, which are shared values that help differentiate right from wrong. Ethics are not law, but they are usually the basis for laws. Everyone, including data scientists, will benefit from this course. No previous knowledge is needed.

Additional materials:

Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 374(2083), 20160360. http://doi.org/10.1098/rsta.2016.0360

Abstract: 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 artificial intelligence, 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 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 enable them. And it emphasizes 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 will data ethics provide solutions that can maximize the value of data science for our societies, for all of us and for our environments.This article is part of the themed issue ‘The ethical impact of data science’.

Relevant Organizations:

Abstract: In collaboration with the National Science Foundation, the Council for Big Data, Ethics, and Society was started in 2014 to provide critical social and cultural perspectives on big data initiatives. The Council brings together researchers from diverse disciplines — from anthropology and philosophy to economics and law — to address issues such as security, privacy, equality, and access in order to help guard against the repetition of known mistakes and inadequate preparation.

Relevant Books:

Abstract: What are your organization’s policies for generating and using huge datasets full of personal information? This book examines ethical questions raised by the big data phenomenon, and explains why enterprises need to reconsider business decisions concerning privacy and identity. Authors Kord Davis and Doug Patterson provide methods and techniques to help your business engage in a transparent and productive ethical inquiry into your current data practices.

Key Points