The chapter argues that to create consumer trust requires technologically neutral rules of consumer and data protection law. The limited impact of the established rules on digital markets raises the question about the need for reform. The chapter focuses on personalised advertising as our case study. Regulation by design is the best method of weaker parties.

Recommender systems (RSs) are one of the most important examples of how AI can be used to improve the experience of consumers, as well as to increase revenues for companies. The chapter presents a short survey of the main approaches. The manipulation of consumer behavior by RCs is less a legal issue, then an ethical one, which should be considered when designing these type of systems

This chapter argues that AI can be a positive force in consumer protection enforcement, although in its current form, it has a limited range. If not used with adequate caution and safeguards or understanding of its limitations, it could lead to under-enforcement. Enforcement authorities are encouraged not to reach for AI solutions first but reflect on the best strategy for including AI-enabled technology in their enforcement toolbox.

Java is one of the world’s most popular programming languages. Widely used in enterprise software development, Java’s strengths lie in its combination of performance and portability, as well as its large, robust library of built-in features, which allow developers to create complex applications entirely within the language. Java was developed in the early 1990s by a team from Sun Microsystems led by James Gosling. Initially called Oak (after a tree outside Gosling’s office), the new language was intended to be a development environment for interactive TV, but pivoted to the emerging World Wide Web after its public release in 1995. Since then, Java has expanded into almost every area of software development. It is the default programming language for Android mobile devices, the Hadoop large-scale data processing system, and Minecraft. Java is one of the most well-known object-oriented programming languages.

This chapter surveys the core elements of Java programming, assuming some familiarity with programming in any language. If you already have Java experience, it will be a refresher on important points. If your experience is with Python, JavaScript, or other languages, this chapter will help you understand how Java does things differently.

After the linear data structures and hash tables, we’re now ready to introduce the third major kind of structure: trees, which represent hierarchical data. Computer science, like nature, delights in trees: There are a huge number of tree-based structures customized for different problems. In particular, trees are used to construct map data structures that provide useful alternatives to hash tables.

Regulators are increasingly aware of the practical implication of such phenomena as algorithmic bias, price discrimination, blackbox AI as well as the misuse of the personal information of consumers. The dangers of algorithmic exploitation in the context of mass market consumer transactions is underexplored. The chapter describes how technology-driven exploitative commercial practices have passed under the legal and regulatory radar. It examines the extent recent regulatory addresses the impact of such practices.

Despite the benefits of the convergence of AI in ecommerce, it is necessary to address some concerns. The presence of AI-powered platforms raises significant challenges to consumer autonomy. This chapter discusses the overlap and interplay among three main legal regimes – EU AI Act Proposal, Digital Services Act (DSA), and EU Consumer Law.These laws will need to be amended with new articles to adequately address AI-specific concerns

A heap is a tree-based data structure that’s designed to quickly return the maximum or minimum of its items. Like a search tree, it maintains a special ordering among its nodes, and takes advantage of the hierarchical nature of binary trees to perform its operations in O(log n) time. Heaps are the primary implementation of the priority queue abstract data type. Like the first-in-first-out queues we studied in Chapter 13, a priority queue supports operations to insert and remove elements, but also maintains an ordering among its items. Polling the queue returns the next item according to the underlying ordering, rather than strictly returning items in FIFO order. Priority queues are used in applications that need to continually fetch the “next” item of a dynamic set that may change over time. A common application is ordering events by time in a simulation program.

Seymour Papert published Mindstorms in 1980. Subtitled Children, Computers, and Powerful Ideas, the book advocated for making computational thinking a core part of the curriculum for young children (Papert, 1980). Mindstorms was influential in computer education circles, and the approaches it described were the first exposure to programming and computer science for many children. LEGO later adopted the name for a line of programmable building sets.

The previous two chapters introduced hash functions and hash tables. In this chapter, we’ll combine hash tables with lists to construct a search engine index. Our index will map a search word to the list of locations where that word occurs in a set of text documents. The data for our example search engine will come from the plays of William Shakespeare, the most influential English-language dramatist in history. We’ll work primarily with the text of Macbeth. The play provides a good development example because the text is rich enough to be interesting, but the structured script format makes it easy to extract everything we need with a reasonable amount of code.

The chapter examines the issue of civil liability in the framework of damages resulting from the use of such autonomous systems. A particular emphasis is placed on the importance of access to justice – of enhancing access for victims of harm to remedies and relief – rather than more abstract or conceptual considerations of the appropriateness of a particular regulatory solution

The stack is the Incredible Hulk of data structures: superficially dumb, but so strong that it doesn’t matter. Stacks have only a few basic operations and they’re easy to implement, but they unlock a number of algorithms that are both theoretically powerful and practically important. You may recall that we previously discussed the role of the stack in recursion, and there’s a connection between the stack as an explicit data structure and recursive methods that use a stack implicitly.

Up to this point we’ve focused on introducing the Java language and – starting with the previous chapter – the technique of algorithm analysis. We’re now ready to put that machinery to work by describing our first important new data structure: lists. A list is like an array, in that it represents an ordered sequence of data values, but lists are more flexible: They support operations for dynamically inserting and removing data as the program executes. We’ve already used Java’s built-in ArrayList class to manage a collection of values; now we’re ready to talk about how it’s implemented internally.