Misinformation before COVID-19

While COVID-19 is the most recent and startling example of misinformation having been spread by social media, it's far from the first. In fact, between 2006 and 2017, false news stories travelled faster than true news stories on Twitter (Vosoughi, Roy and Aral, 2018). While the majority of misinformation relating to COVID-19 was found on Facebook, Twitter and TikTok, another type of social media was far from immune: virtual worlds. Second Life, a virtual world launched in 2003 by Linden Lab, initially gained popularity as a platform for businesses and social interaction (Muller and Koschate, 2012). Despite its early promise, Second Life's success has been limited due to low active user numbers and challenges in finding trustworthy information (Suomi, Mantymaki and Soderlund, 2014).

Mumps, measles and rubella

The belief in the combined MMR (mumps, measles and rubella) vaccination is another severe case of misinformation. Despite the fact that Andrew Wakefield, the doctor who put forward the original theory in 1998 has long since been discredited and struck off the UK medical register, the harm was already done. Vaccine scepticism increased in the wake of the publication (Motta and Stecula, 2021) and despite the retraction, Wakefield continues to be quoted by the anti-vaccine lobby.

For Gabarron, Oyeyemi and Wynn (2021), while misinformation might be a particular concern for the general public, healthcare providers themselves can also, on occasion, be responsible for spreading misinformation. Healthcare librarians have a responsibility in this area: namely, dispelling the misinformation or disinformation that was so prevalent during the COVID- 19 pandemic and ensuring that accurate resources are made accessible instead.

The data landscape has changed almost beyond recognition over the last 30 years. Established methods of collecting, compiling and publishing infor - mation have either been made redundant or radically transformed. We have looked at the drivers of these changes in the previous chapter, with the internet and, more recently, AI reconfiguring data value chains and business models in the process. This chapter explores the current state of the information landscape and considers how much data is being created in the mid-2020s, where it is coming from and how it is being used.

How much data is there?

Precisely mapping and measuring the global information economy is impossible as countries measure things differently and many of the inputs and outputs are hidden behind firewalls and corporate networks. However, approximations can be made based on publicly reported data, government statistics and technology sales. Broadly, we can attempt to measure the monetary value of data and information products produced each year as well as the quantity of data produced, distributed and stored. For the purposes of this book, the utility of considering such figures is to help us better understand broader trends in the production, distribution and use of data and what this might mean for the future of information professionals from a range of disciplines. Understanding the shifting sands of our data-driven environment can help us make better decisions about where to invest our time and resources going forward.

One of the first comprehensive and rigorous attempts to measure the volume of information being generated each year was carried out by economists Peter Lyman and Hal Varian at Berkeley University in 2000 (Lyman and Varian, 2000).

This chapter explores the rapidly developing policies of national and international governmental bodies relating to data and AI, the emerging resulting legislation and the ethical concerns leading to these initiatives. As policies, laws and rulings are evolving to deal with the new challenges posed by AI and the data it feeds off, this chapter presents a snapshot of the legal situation in late 2024; however, the issues underpinning policy and legislative changes are ongoing. These centre on concerns with privacy and data protection, copyright, monopolistic practices and trust as well as efforts to stimulate economic growth and international competitiveness. The chapter focuses on developments in the UK and the US as well as across the EU, which allows for policies and legislation to be compared and highlights different national concerns and priorities.

Ethical concerns

Scientific breakthroughs and new technologies have long been a source of alarm for philosophers, writers and politicians. This is particularly true where questions of what it is to be human and how far machines can replace human activities emerge. Mary Shelley's 1818 creation of the Frankenstein monster highlighted concerns emerging from the enlightenment and the move from a society based on superstition to one more founded on the principles of science (Gunkel, 2024, 3). Science fiction novels and films – from Metropolis in 1927 and Brave New World in 1932 to 2001: A Space Odyssey in 1968 and Bladerunner in 1982 – developed these ideas in different and unsettling ways. While public policies and laws change fairly frequently in response to events and differing priorities, the values and ethics that underpin them are more constant.

The title of this chapter may be provocative to some as it suggests radical change driven by data and AI are leading a revolution in how organisations will operate in the near future. Caution over analysts and pundits calling technological advances ‘revolutions’ is wise (as we shall see in Chapter 5) and there is no shortage of examples where irrational exuberance has got the better of writers and forecasters. As the management guru, Peter Drucker, is quoted as saying, ‘Trying to predict the future is like trying to drive down a country road at night with no lights while looking out the back window’ (Guber, 2014). This is particularly true with trying to forecast how new technologies will evolve and whether they will succeed in the marketplace. Established technology vendors may have a vested interest in playing down the significance of a new innovation developed outside their own company, while businesses launching new products may go in the opposite direction.

Over-confidence in the existing order was demonstrated in 1876 when the president of the telegraph giant, Western Union, was offered the patent on the telephone for US$100,000 and stated, ‘This “telephone” has too many shortcomings to be seriously considered as a means of communication’ (Yeuh, 2019). Less than 30 years later, his company was bought by telephony ‘upstart’ AT&T. On the other side, we have WeWork, once valued at US$65 billion by Goldman Sachs for its innovative use of technology to revolutionise officeletting but sold for approximately one hundredth of that figure a few years later (Neate, 2019; Sherman, 2024). Hubris and a confused business model were to blame there.

While acknowledging the difficulties of predicting technology adoption curves, the contention of this book is that we are at the start of a revolution in business practices driven by data and AI. Chapter 5 will explore how this may take shape over the coming five to ten years. This chapter examines a number of current developments and how organisations are already experimenting with and deploying data-driven AI services.

A brief history of the information industry

Before considering the business and social impacts of the current data-driven AI revolution, it is worth considering how our use of information has evolved over the last 5,000 or so years. In many ways, the forces that led to the rise of information being transcribed for the first time are not too dissimilar to those driving change today. Commerce and the desire to maintain power over disparate groups of citizens have always required the creation, storage and distribution of information in multiple forms and across a variety of media.

One of the earliest forms of transcription was discovered in 1929 by Julius Jordan, a German archaeologist who excavated a collection of clay tablets over 5,000 years old in what is now Iraq. It took almost 50 years for researchers to decipher the markings on the tablets, which turned out to be records of the sale of commodities such as sheep, grain and honey. What many archae - ologists had thought were early forms of poetry or personal correspondence were, in fact, much more mundane, but vital to the orderly flow of goods across the region (Harford, 2017). Further study of the tablets revealed an unexpected sophistication in the way information was transcribed in the clay. As higher quantities of goods needed to be recorded, new ways of representing larger numbers were needed. For example, the sale of three sheep involved the sheep being pressed into the clay three times, but when quantities grew above ten, other symbols were required. These symbols allowed records of ever-larger exchanges to take place.

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.

Introduction

As access to funding becomes increasingly competitive, with ever closer scrutiny to ensure wise investment and value for money, writing an internal business case is now an essential skill across all areas of library practice. But I believe it is also an area where libraries can leverage their unique position. At the nexus of professional services and the faculty, library business cases can be well placed to garner wide support, deliver on multiple agendas and strategies and derive maximum benefit.

However, faced with a blank sheet of virtual paper, writing a business case can feel a daunting task. This chapter aims to demystify the process and provide an outline of the key steps and considerations needed to make a great idea happen or to address a known need. This guidance can apply to any area of library work, including building projects, new or replacement technology, increased staff resource, a change in the delivery of a service or a new way of working. And it is of course likely that, with many estates-related projects, all of the above will be included.

The need for a business case

There are many reasons why an argument for a change or new course of action might be proposed, but before a business case is started I find it helpful to consider some key questions, such as the following.

• • Is there an identifiable benefit for the organisation?

• • Is there a good reason or clear drivers for the change being proposed?

• • What options have been considered?

• • Are the costs and the benefits clear?

• • Who are the key stakeholders and what is their interest in the proposal?

• • Have assumptions been tested and the required evidence gathered?

Introduction

The University of Northampton (UON) was established as a university in 2005 and specialises in healthcare, education, science, technology, arts and business. It has approximately 12,000 students based at the main Waterside Campus and is the only university in Northamptonshire, employing around 2,200 staff, over half of whom live in the town and surrounding county.

Until 2018 the University operated from two main campuses approximately 2.5 miles apart, Park Campus just to the north of the town, and Avenue Campus, closer to the town centre.

As the name suggests, Park Campus was reminiscent of open parkland, providing an array of green space around a portfolio of academic buildings ranging in size, style, age and condition, whereas Avenue Campus comprised a variety of interconnecting buildings, many from the early 20th century, built around a series of landscaped courtyards. The campus had been recently supplemented by the purchase of an adjacent Grade II former middle school.

While distinctly different in character and design, both campuses shared similarities, and problems, associated with cost and space inefficiency. In addition, their distance from the town centre was a contributory factor to a lack of cohesion with and connection to the town and its wider population.

There was a centrally situated library on each of the old campuses. Neither was in a discrete building, but each was self-contained, with a traditional single entrance and exit arrangement guarded by library security gates. Both libraries had been extended and extensively remodelled and refurbished over the previous 20 years to accommodate growth in the physical collections, increased numbers of personal computers (PCs) and a variety of staff, study and social spaces, including a very popular library café just outside the entrance gates at Park Campus.