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.

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.

The idea that business is only about the money doesn't hold true in the twenty-first century, when companies around the world are giving up traditional distinctions in order to succeed. Yet our expectations for businesses remain under the sway of an outdated worldview that emphasizes profits for shareholders above all else. The Power of And offers a new narrative about the nature of business, revealing the focus on responsibility and ethics that unites today's most influential ideas and companies. R. Edward Freeman, Kirsten E. Martin, and Bidhan L. Parmar detail an emerging business model built on five key concepts: prioritizing purpose as well as profits; creating value for stakeholders as well as shareholders; seeing business as embedded in society as well as markets; recognizing people's full humanity as well as their economic interests; and integrating business and ethics into a more holistic model. Drawing on examples across companies, industries, and countries, they show that these values support persevering in hard times and prospering over the long term. Real-world success stories disprove the conventional wisdom that there are unavoidable trade-offs between acting ethically and succeeding financially. The Power of And presents a conceptual revolution about what it means for business to be responsible, providing a new story for us to tell in order to help all kinds of companies thrive.