How to Use This Book
This book embarks on a crucial exploration into how data is wielded across different disciplines, a landscape that is increasingly shaping our modern world. It’s essential to note, however, this inquiry is fraught with challenges, particularly because many businesses and organizations guard their data practices as proprietary trade secrets. For instance, platforms like TikTok deliberately shroud their algorithms in mystery, as much of their success hinges on the perception that they’ve mastered the secret sauce of user engagement.
Our student contributors have diligently navigated these barriers, piecing together an overview of how data is impacting various fields. While we aimed to incorporate DEI perspectives in every chapter, we often found ourselves stymied by the opaque nature of industry practices. The veil of trade secrets not only limits what we can definitively say about data practices in these sectors but also complicates efforts to evaluate these practices from a DEI standpoint.
Despite these challenges, this book fills an essential gap in the current literature. It offers an entry point into the complex interplay of data and industry, providing foundational insights that can spur further inquiry and discussion. However, given the constraints and limitations, this book should not serve as a standalone course text.
For a more rounded educational experience, we recommend supplementing this book with additional resources that focus explicitly on DEI issues. To help in this regard, the appendix includes several Creative Commons licensed readings. We also provide a suggested readings list, carefully curated to complement the perspectives explored in this text and to broaden the DEI lens through which these issues can be examined.
This book is intended to be a living and continually updated document each time the class is taught, rather than a final product. As such, I’d like to briefly explain how it is laid out and how others might use it for their own courses.
Introduction: The introduction is intended to give an historical overview of the project as well as the class in which it was written. I believe transparency is important and the layout of the class may be helpful to other instructors who are teaching a course and want to adopt this textbook. Because of this, it may be of less interest to students themselves.
Chapter 1: Why Care about Data & Society? When I teach this class, it’s important to me that I connect with students about what the major issues are related to data and society, and also why I personally care about them. This chapter attempts to bring those approaches together in writing. This approach is not meant to simply share my own accolades. As I discuss in this chapter, I’ve found that discussing data in the abstract can sometimes cause students to tune out, or make it difficult to connect to the subject. By giving an overview of data through the lens of my own career and experiences, I hope students will be able to see my passion and better understand, in a concrete way, why this topic is important.
Chapter 2 Generative AI in the Classroom and Workplace: This chapter includes a lesson with activities and guiding questions that can be used in class to teach about Large Language Models and other generative AI programs, like ChatGPT. It also features important ethical questions and considerations.
Chapter 3: Case Study: “It’s Perfect, Four Stars!”: This is a case study written from a first-person perspective by a business professional about their experiences related to data and society. While this is currently the only case in the text, I plan to add more of these in the future, sprinkled throughout the text. This particular case study highlights the voice and experience of a woman business owner.
Chapters 4-10: These chapters were written by students on subjects of their own choosing related to their future career interests. Students in future classes will be encouraged to either expand on existing chapters or write new chapters about career paths that aren’t currently represented. Instructors may want to assign only relevant chapters to their students, or allow their students to contribute to the text as I do.
Chapter 11: GenAI hype surrounds us on a daily basis, but so does substantial fear and anxiety. Many worry that such tools will continue to erode critical thinking skills, or remove something that is essentially “human” from the creative process. Others believe that because GenAI tools are trained on the writing and artwork of humans, all use of such tools is a form of intellectual and creative theft. Will the technology continue to improve and eventually achieve sentience? This chapter aims to give an overview of some of these major issues while also demonstrating how to use a variety of GenAI-based tools that might increase the productivity and creativity of professionals.
Chapter 12: SOPHIA Discussion Guides: This chapter features a series of discussion guides that were created by groups of students for public discussions, as part of a partnership with the Society of Philosophers in America. Students in this class could choose to use and modify them to hold their own public discussions, or they could be adapted for in-class discussions.
Chapters 13-14: These two chapters are available via a Creative Commons license and can be assigned to help better fill some of the gaps related to diversity, equity, and inclusion that occur in the above chapters focused on data in specific disciplines. They are written by leading scholars in the field.
Chapter 15: This chapter offers additional recommended reading and viewing, organized by topic. Many of them can be assigned for reading under Fair Use laws or are publicly available multimedia content such as TV shows, podcasts, and documentaries.
