Data are not neutral. They are created by humans and are the products of biased choices and perspectives. The use, interpretation and analysis of data are also shaped by people's biases and perspectives.
To learn about approaching data in an ethical way, consult:
Algorithms of oppression : how search engines reinforce racism by
In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem. Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of color, especially women of color. Through an analysis of textual and media searches as well as extensive research on paid online advertising, Noble exposes a culture of racism and sexism in the way discoverability is created online. As search engines and their related companies grow in importance--operating as a source for email, a major vehicle for primary and secondary school learning, and beyond--understanding and reversing these disquieting trends and discriminatory practices are of utmost importance.
Data Ethics by
Data-gathering technology is more sophisticated than ever, as are the ethical standards for using this data. This second edition shows how to navigate this complex environment. Data Ethics provides a practical framework for the implementation of ethical principles into information management systems. It shows how to assess the types of ethical dilemmas organizations might face as they become more data-driven. This fully updated edition includes guidance on sustainability and environmental management and on how ethical frameworks can be standardized across cultures that have conflicting values. There is also discussion of data colonialism, the challenge of ethical trade-offs with ad-tech and analytics such as Covid-19 tracking systems and case studies on Smart Cities and Demings Principles. As the pace of developments in data-processing technology continues to increase, it is vital to capitalize on the opportunities this affords while ensuring that ethical standards and ideals are not compromised. Written by internationally regarded experts in the field, Data Ethics is the essential guide for students and practitioners to optimizing ethical data standards in organizations.
Data ethics and challenges by
This book gives a thorough and systematic introduction to Data, Data Sources, Dimensions of Data, Privacy, and Security Challenges associated with Data, Ethics, Laws, IPR Copyright, and Technology Law. This book will help students, scholars, and practitioners to understand the challenges while dealing with data and its ethical and legal aspects. The book focuses on emerging issues while working with the Data.
Ethics of Data and Analytics by
The ethics of data and analytics, in many ways, is no different than any endeavor to find the "right" answer. When a business chooses a supplier, funds a new product, or hires an employee, managers are making decisions with moral implications. The decisions in business, like all decisions, have a moral component in that people can benefit or be harmed, rules are followed or broken, people are treated fairly or not, and rights are enabled or diminished. However, data analytics introduces wrinkles or moral hurdles in how to think about ethics. Questions of accountability, privacy, surveillance, bias, and power stretch standard tools to examine whether a decision is good, ethical, or just. Dealing with these questions requires different frameworks to understand what is wrong and what could be better. Ethics of Data and Analytics: Concepts and Cases does not search for a new, different answer or to ban all technology in favor of human decision-making. The text takes a more skeptical, ironic approach to current answers and concepts while identifying and having solidarity with others. Applying this to the endeavor to understand the ethics of data and analytics, the text emphasizes finding multiple ethical approaches as ways to engage with current problems to find better solutions rather than prioritizing one set of concepts or theories. The book works through cases to understand those marginalized by data analytics programs as well as those empowered by them. Three themes run throughout the book. First, data analytics programs are value-laden in that technologies create moral consequences, reinforce or undercut ethical principles, and enable or diminish rights and dignity. This places an additional focus on the role of developers in their incorporation of values in the design of data analytics programs. Second, design is critical. In the majority of the cases examined, the purpose is to improve the design and development of data analytics programs. Third, data analytics, artificial intelligence, and machine learning are about power. The discussion of power--who has it, who gets to keep it, and who is marginalized--weaves throughout the chapters, theories, and cases. In discussing ethical frameworks, the text focuses on critical theories that question power structures and default assumptions and seek to emancipate the marginalized.
Invisible Women by
#1 International Bestseller "A rallying cry to fight back." --Sunday Times (London) Winner, 2019 Financial Times and McKinsey Business Book of the Year Award Winner, 2019 Royal Society Science Book Prize Data is fundamental to the modern world. From economic development, to healthcare, to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems. And women pay tremendous costs for this bias in time, money, and sometimes with their lives. Celebrated feminist advocate Caroline Criado Perez investigates the shocking root cause of gender inequality and research in Invisible Women, diving into women's lives at home, the workplace, the public square, the doctor's office, and more. Chapters here include: Can Snow-Clearing Be Sexist The Myth of Meritocracy The Henry Higgins Effect One-Size-Fits-Men Yentl Syndrome From Purse to Wallet Women's Rights Are Human Rights Perez writes in her preface, "It's when women are able to step out from the shadows with their voices and their bodies that things start to shift. The gaps close. And so, at heart, Invisible Women is also a call for change. For too long we have positioned women as a deviation from standard humanity and this is why they have been allowed to become invisible. It's time for a change in perspective. It's time for women to be seen." Built on hundreds of studies in the US, the UK, and around the world, and written with energy, wit, and sparkling intelligence, this is a groundbreaking, unforgettable exposé that will change the way you look at the world.
Toward information justice : technology, politics, and policy for data in higher education administration by
Toward Information Justice answers a key question for the 21st Century: how can an information-driven society be just? Many of those concerned with the ethics of data focus on control over data, and argue that if data is only controlled by the right people then just outcomes will emerge. There are serious problems with this control metaparadigm, however, especially related to the initial creation of data and prerequisites for its use. This text is suitable for academics in the fields of information ethics, political theory, philosophy of technology, and science and technology studies, as well as policy professionals who rely on data to reach increasingly problematic conclusions about courses of action.
Weapons of Math Destruction by
Longlisted for the National Book Award | New York Times Bestseller A former Wall Street quant sounds an alarm on the mathematical models that pervade modern life and threaten to rip apart our social fabric. We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we get a car loan, how much we pay for health insurance--are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated. But as Cathy O'Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination: If a poor student can't get a loan because a lending model deems him too risky (by virtue of his zip code), he's then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a "toxic cocktail for democracy." Welcome to the dark side of Big Data. Tracing the arc of a person's life, O'Neil exposes the black box models that shape our future, both as individuals and as a society. These "weapons of math destruction" score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health. O'Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it's up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.
Calling bullshit : the art of skepticism in a data-driven world by
The world is awash in bullshit, and we're drowning in it. Politicians are unconstrained by facts. Science is conducted by press release. Startup culture elevates bullshit to high art. These days, calling bullshit is a noble act. Based on Carl Bergstrom and Jevin West's popular course at the University of Washington, Calling Bullshit is a modern handbook to the art of skepticism. Bergstrom, a computational biologist, and West, an information scientist, catalogue bullshit in its many forms, explaining and offering readers the tools to see through the obfuscations, deliberate and careless, that dominate every realm of our lives.
Coded bias by
CODED BIAS explores the fallout of MIT media lab researcher Joy Buolamwini's startling discovery that facial recognition does not see dark-skinned faces accurately, and her journey to push for the first-ever legislation in the U.S. to govern against bias in the algorithms that impact us all,