No other computational problem has been studied in more depth, or yielded a greater number of useful solutions, than sorting. Historically, business computers spent 25% of their time doing nothing but sorting data (Knuth, 2014c), and many advanced algorithms start by sorting their inputs. Dozens of algorithms have been proposed over the last 80-odd years, but there is no “best” solution to the sorting problem. Although many popular sorting algorithms were known as early as the 1940s, researchers are still designing improved versions – Python’s default algorithm was only implemented in the early 2000s and Java’s current version in the 2010s.

This chapter examines the effects that legally-oriented AI developments will have on consumer protection and to consumers’ need for legal advice and representation. The chapter provides a brief survey of the many possible ways in which AI may influence consumers’ legal needs. It provides comparative analysis of the benefits and risks of the use of AI in the legal sphere, discusses the state of regulation in this area and argues in favor of a new regulatory framework.

Computer animators have always sought to push boundaries and create impressive, realistic visual effects, but some processes are too demanding to model exactly. Effects like fire, smoke, and water have complex fluid dynamics and amorphous boundaries that are hard to recreate with standard physical calculations. Instead, animators might turn to another approach to create these effects: particle systems. Bill Reeves, a graphics researcher and animator, began experimenting with particle-based effects in the early 1980s while making movies at Lucasfilm. For a scene in Star Trek II: The Wrath of Khan (1982), he needed to create an image of explosive fire spreading across the entire surface of a planet. Reeves used thousands of independent particles, each one representing a tiny piece of fire (Reeves, 1983). The fire particles were created semi-randomly, with attributes for their 3D positions, velocities, and colors. Reeves’ model governed how particles appeared, moved, and interacted to create a realistic effect that could be rendered on an early 1980s computer. Reeves would go on to work on other Lucasfilm productions, including Return of the Jedi (1983), before joining Pixar, where his credits include Toy Story (1995) and Finding Nemo (2003).

Java is an object-oriented programming language. Java programs are implemented as collections of classes and objects that interact with each other to deliver the functionality that the programmer wants. So far, we’ve used “class” as being roughly synonymous with “program,” and all of our programs have consisted of one public class with a main method that may call additional methods. We’ve also talked about how to use the new keyword to initialize objects like Scanner that can perform useful work. It’s now time to talk about the concepts of objects and classes in more depth and then learn how to write customized classes.

The previous two chapters showed how the concept of last-in-first-out data processing is surprisingly powerful. We’ll now consider the stack’s counterpart, the queue. Like a waiting line, a queue stores a set of items and returns them in first-in-first-out (FIFO) order. Pushing to the queue adds a new item to the back of the line and pulling retrieves the oldest item from the front. Queues have a lower profile than stacks, and are rarely the centerpiece of an algorithm. Instead, queues tend to serve as utility data structures in a larger system.

A hash, in culinary terms, is a dish made of mixed foods – often including corned beef and onions – chopped into tiny pieces. In the early twentieth century, it became a shorthand for something of dubious origin, probably unwise to consume. In computer science, a hash function is an operation that rearranges, mixes, and combines data to produce a single fixed-size output. Unlike their culinary namesake, hash functions are wonderfully useful. A hash value is like a “fingerprint” of the input used to calculate it. Hash functions have applications to security, distributed systems, and – as we’ll explore – data structures.

In this chapter, we are interested in how AI may enhance our well-being – or do the opposite. A defintion of well-being and promotion of core vlaues will be discussed. It will then survey AI technologies and assess whether they enhance or diminish human well-being, using the different meanings of well-being

This Chapter will examine whether the Digital Content Directive (DCD) can sufficiently protect the consumer who concludes contracts through software on AI-driven online platforms (without being directly involved in the contractual process) against certain of the existing risks. More specifically, due to a technical error or some other factor, such contracts may be mistaken or unintended by the human consumer. Moreover, the consumer may end up dealing with an unreliable, fraudulent or even fictitious trader suffering loss as a result. The question arises as to whether the consumer will have a sufficient remedy in these cases, namely an available route to compensation. In this respect, the Digital Content Directive merits examination with the aim of ascertaining whether it responds to this need of the consumers who contract on AI-driven platforms. The main questions in this context will be whether such platforms qualify as ‘digital services’ within the meaning of said Directive and if yes, whether the provisions of the measure are suitably adjusted to the need of the substituted consumer for an available route to compensation in these cases. These questions may also pinpoint to a possible approach towards the liability of marketplaces for the non-conformity of goods and services offered by third party sellers through their systems. As it will be shown, though the DCD does contain tools that could prove useful to consumers in their attempt to claim and receive compensation, its application is not without problems that may prevent this result. Other measures, specifically the Digital Services Act (DSA) and the Unfair Commercial Practices Directive (UCPD) may offer some help, where the DCD could not do much.

AI-enhanced smart contracts exhibit a high degree of autonomy in their ability to create and execute transactions between and among humans and machines. AI should allow a broader use of of marts contracts in consumer transactions by allowing businesses to satisfy consumer protection law through the coding of smart contracts. AI should be used to advance the principles of fairness and economic efficiency in the drafting and enforcement of smart consumer contracts.

Society needs to influence and mould our expectations so AI is used for the collective good. we should be reluctant to throw away hard (and recently) won consumer rights and values on the altar of technological developments.