One of the aims of this book is to show how the evolution of the data industry and the organisations that use data in all its forms has facilitated the current wave of innovation in AI. The two sectors are inextricably linked, with data acting as the fuel powering the rapidly developing plethora of AI services.

In Chapter 1, we have seen how the first transcribing and storing of information had its roots in commerce, with technology evolving to better manage and share data within and between organisations. More recently, data-driven innovation has resulted in the creation of some of the world's largest and most profitable companies. During the second half of the 20th century, AI has moved from universities and research centres into commercial applications, with businesses’ and the public's imaginations being captured by GenAI as a tangible and usable product. We have also examined some of the research and frameworks that explain the drivers of innovation with a focus on the impact of digital platforms. Finally, we looked at how the recent wave of data-powered platforms and concerns about the applications of AI are impacting political and legal initiatives around the world.

These developments are all in a state of flux and it will take several years before the economic and social implications of this data-driven AI revolution are fully understood. This chapter considers how this all might play out in the medium term in the context of how AI technologies will evolve (and what this means for the data sector and businesses) as well as the broader social and economic impacts.

Predicting the future is fraught with dangers and, for the most part, inaccurate. However, looking at recent precedents in the ways new technologies have diffused, the unchanging realities of business and the need to generate profits, and the current state of AI technologies, we can make some broad assumptions in this space.

We are entering a new phase in the information revolution driven by the introduction of new artificial intelligence (AI) technologies and how they are being used to transform the data amassed within organisations over the previous 30 or so years. This revolution started hundreds of years ago when moveable type was used to print books at a scale and speed not previously possible. The rise of mass media and then mass communications in the form of telecommunication networks, coupled with the data processing capabilities of computers, powered the next phases. Thirty years ago, the expansion of the internet and the widespread adoption of the World Wide Web (WWW) as a means to publish and share information brought the digital revolution into our homes and businesses. Mobile computing has put powerful, always connected computers in the pockets of most people in the industrialised world and social networks have provided platforms for individuals to reach billions of others with their thoughts and ideas.

A result of these innovations and the ways they have been used is a world awash with information, most of which sits unused, unstructured and hidden away in archives and data silos within the organisations, public and private, that created it (Lange, 2023). Despite significant advances in information management techniques and technologies, only a fraction of the value of this data is being realised. However, we are now at an inflection point where much of the infrastructure is in place to begin the process of changing that.

The initial excitement as well as considerable hype from software companies, AI developers and technology commentators following the launch of ChatGPT and other GenAI products in late 2022 and into 2023 has died down to a large extent. The ‘magic’ of seeing text and images being created in seconds from a few simple prompts is now just another clever thing that computers can do and is becoming part of many people's daily workflows. It was the same with e-mail, the World Wide Web (WWW), mobile phones and social media when they first became available. They are all now part of the warp and weft of everyday life. In time, GenAI and the applications that incorporate it will be no different. However, ‘time’ is the watchword here. This will not happen overnight for all the reasons discussed in this book. Developers need to demonstrate the value AI offers to organisations through real use cases and solid evidence of a return on investment. Adopting organisations need to be confident that the benefits outweigh the risks and this requires further work from developers and vendors in removing problems such as hallucinations and privacy breaches. If agentic AI is to take hold, then trust in such systems will be key. Alongside this, regulators and public policy makers will need to adapt their approaches as the technology evolves and its opportunities and risks become clearer. Finally, education will be a vital factor in helping workers, existing and yet to enter the workforce, adapt to this transformative technology, as well as teaching all individuals what they can and cannot trust online. This last requirement is, perhaps, the most important as it touches on foundational issues such as literacy and democracy.

In this chapter, the author reflects on his experience as the founding director of IDSS. The reflections examine the challenges of establishing new entities within academia, offer insights into the process, and conclude with a discussion on how this journey has impacted the author’s thinking and research agenda.

In this chapter, we aim to contribute to the ongoing discussions involving legal entities, big tech, and governments by introducing several key topics and questions related to data privacy, decision-making, and regulation. We explore the balance between mathematical logic and social justice, the challenge of eliminating persistent biases through programming, and the extent of control and accountability humans should maintain over generative systems. We also consider whether machines should be held to the same ethical standards as humans and contemplate the role of the free market in shaping societal outcomes.

The chapter concludes with an examination of how data is monetized through Ad markets, its role in perpetuating bias, and the need to define personal data. Through these discussions, we hope to provide a foundation for deeper exploration and understanding of the complex issues surrounding data privacy, decision-making, and regulation.

This chapter delves into the complexities and challenges of data science, emphasizing the potential pitfalls and ethical considerations inherent in decision-making based on data. It explores the intricate nature of data, which can be multifaceted, noisy, temporally and spatially disjointed, and often a result of the interplay among numerous interconnected components. This complexity poses significant difficulties in drawing causal inferences and making informed decisions.

A central theme of the chapter is the compromise of privacy that individuals may face in the quest for data-driven insights, which raises ethical concerns regarding the use of personal data. The discussion extends to the concept of algorithmic fairness, particularly in the context of racial bias, shedding light on the need for mitigating biases in data-driven decision-making processes.

Through a series of examples, the chapter illustrates the challenges and potential pitfalls associated with data science, underscoring the importance of robust methodologies and ethical considerations. It concludes with a thought-provoking examination of income inequality as a controversial example of data science in practice. The example highlights the nuanced interplay between data, decisions, and societal impacts.

This chapter asserts that the evolution of AI over the past seven decades has been closely intertwined with advancements in computational power. It identifies four key computing developments – mainframes, personal computers, wireless communication and the internet, and embedded systems – that have significantly influenced the field of data science and AI.

Starting from the early concepts of Turing machines, the chapter traces the parallel evolution of AI through milestones such as the invention of the perceptron, the development of machine learning techniques, and the current state of AI systems. It highlights key moments in AI history, from the first computer to play checkers to the algorithmic triumph of Deep Blue over a chess champion, as well as the recent achievements of AlphaGo.

By placing these advances in the context of broader computing history, the chapter argues that contemporary AI capabilities are the culmination of deliberate and iterative technological progress. It concludes by examining the profound impact of computing and AI on political institutions, citing examples such as the Arab Spring and the Cambridge Analytica scandal.

This chapter introduces Data, Systems, and Society (DSS), a new transdiscipline bridging statistics, information and decision systems, and social and institutional behavior. It emphasizes the value of transdisciplinarity over multidisciplinarity and interdisciplinarity and advocates for integrating DSS across domains where data and systems are pivotal (e.g., engineering, sciences, social sciences, and management). The chapter concludes by illustrating how DSS training has been instrumental in tackling different facets of the COVID-19 pandemic, including testing, vaccination strategies, and evaluating regional policies.

This chapter explores how students and faculty have applied their training in the DSS transdiscipline to address complex societal problems. Through examples from fields as diverse as climate change, social media, genomics, and anesthesia, it demonstrates the breadth of the transdiscipline’s power. Those examples illustrate how the DSS framework enables researchers to navigate and effectively address complex societal questions.

This brief chapter serves as a prelude to the book, initiating a dialogue on a societal challenge – specifically, transportation systems – and their intersection with data science and AI. It establishes a thematic framework for ensuing discussions.

This chapter explores the role of abstraction in addressing complex societal issues, challenging the perception that abstractions are merely approximations that separate physical systems from high-level computational systems. It emphasizes the importance of intentional and expert-driven abstraction in modeling and analyzing intricate systems. The chapter argues that deriving abstractions is a creative process lacking a systematic methodology.

Drawing examples from information theory and equilibrium theory, the chapter illustrates how abstractions have shaped discoveries over time. It emphasizes the close relationship between abstractions and objectives, noting the presence of multiple abstractions within a single domain.

The chapter concludes with an example demonstrating how abstractions can offer valuable insights into questions surrounding collective intelligence and crowd-sourcing.

This chapter explores the establishment of the Institute of Data, Systems, and Society (IDSS) at MIT, which was founded on the principles of the DSS transdiscipline. It provides a historical overview of the various components of DSS at MIT and describes how the new institute built upon this foundation. The chapter delves into the creation of a robust statistics effort within IDSS, emphasizes the importance of strong connections with the social sciences and humanities, and discusses the need to embed the institute across various domains. It also examines the rationale behind the creation of new academic programs designed to prepare students proficient in this transdiscipline while simultaneously focusing on specific domains. In addition, the chapter outlines the breadth of academic programs offered by IDSS, including considerations for online educational programs.

The chapter offers a thorough description of the unique administrative architecture of IDSS and how it was designed to support the transdiscipline’s growth and development. It addresses MIT’s approach to hiring and promoting faculty within IDSS and highlights the professional challenges faced by junior faculty members.

The chapter concludes with an overview of the launch of the Initiative on Combating Systemic Racism within IDSS, highlighting why the institute is well-suited to nurture such initiatives within MIT.