This chapter explores the regulatory regimes that may be applied to data science. They could be legally mandated, established by voluntary trade groups, or established for business rationale (e.g., to minimize insurance costs). Perhaps, growing societal norms will become de facto standards. The authors focus on just a few topics and refer the reader to a vast and growing literature on regulation coming from public policy, economic, technology, and legal perspectives.

This chapter summarizes societal concerns surrounding data and automation. It is informed not only by what is being discussed by politicians and journalists, but also by the challenges discussed in previous chapters. Examples include societal concerns over data science’s impact on inequality, the scale of large, data-science-oriented companies and the influence of social networks.

This chapter makes two R&D recommendations to address concerns raised in Chapter 15: increasing focused and transdisciplinary research; and fostering innovation.

This chapter discusses the importance of education and rigor in the definition and use of vocabulary surrounding automation and data. More education helps individuals by enhancing their ability to understand data and data science’s growing impact, and to both contribute to and benefit from the field. A more knowledgeable public and a clear vocabulary for discourse is needed to have better communication and debate.

This chapter addresses the four aspects of dependability: privacy, security, resistance to abuse, and resilience. To be accepted by society, data science applications must perform properly for a wide variety of users in a wide variety of circumstances, with few, if any, critical errors. Achieving needed dependability goals (for example, in self-driving cars or health-care applications) is one of the greatest challenges in data science.

This chapter focuses on the challenges in setting the clear and proper objectives of a data science application, a necessity when the goals are to predict, optimize, or recommend. The main challenges include the clarity of the objectives, the balance of benefits across affected parties, fairness (a specific topic within the topic of balance), and the impact of the objectives on an individual: manipulation, filter bubbles (though these affect society as well), privacy, and being human.

This chapter discusses the sensitivity of data science applications to failures, failures which may occur because data science problems often have no unambiguously correct answers and because solutions are often only probabilistically correct. This chapter also discusses how to characterize uncertainty, how to minimize risks while balancing them against rewards, and how to assess liability for any residual harms that may occur, despite the best efforts to minimize them.

This chapter recommends the careful application of the Analysis Rubric to increase the likelihood that the myriad aforementioned challenges are considered and met. It also makes several simple proposals to encourage the application of ethical principles.

This chapter explores data science’s broad legal issues and some previously undiscussed societal (primarily economic) implications of data science. Importantly, this chapter continues the ethics thread begun in Chapters 3 and 7 with a pragmatic discussion of the challenges of internalizing ethical considerations in organizations that apply data science.

This chapter explains how data science has been able to achieve its theoretical, methodological, and practical results by combining the approaches of different disciplines to create a new field. It also surveys the breadth of data science’s likely impact and explains that its continued success is due not only to its own core advances, but to coalitions with many other disciplines.