Opinions from social media are increasingly used by individuals and organizations for making purchase decisions and making choices at elections and for marketing and product design. Positive opinions often mean profits and fames for businesses and individuals. Unfortunately, that gives imposters a strong incentive to game the system by posting fake reviews or opinions to promote or to discredit some target products, services, organizations, individuals, and even ideas without disclosing their true intentions, or the person or organization for which they are secretly working. Such individuals are called opinion spammers and their activities are called opinion spamming (Jindal and Liu, 2007, 2008). An opinion spammer is also called a shill, a plant, or a stooge in the social media environment, and opinion spamming is also called shilling or astroturfing. Opinion spamming can not only hurt consumers and damage businesses, but also warp opinions and mobilize masses into positions counter to legal or ethical mores. This can be frightening, especially when spamming is about opinions on social and political issues. It is safe to say that as opinions in social media are increasingly used in practice, opinion spamming is becoming more and more sophisticated, which presents a major challenge for its detection. However, such offenses must be detected to ensure that social media continue to be a trusted source of public opinions, rather than being full of fakes, lies, and deceptions.

As discussed in Chapter 3, document-level sentiment classification is too coarse for practical applications. We now move to the sentence level and look at methods that classify sentiment expressed in each sentence. The goal is to classify each sentence in an opinion document (e.g., a product review) as expressing a positive, negative, or neutral opinion. This gets us closer to real-life sentiment analysis applications, which require opinions about sentiment targets. Sentence-level classification is about the same as document-level classification because sentences can be regarded as short documents. Sentence-level classification, however, is often harder because the information contained in a typical sentence is much less than that contained in a typical document owing to their length difference. Most document-level sentiment classification research papers ignore the neutral class mainly because it is more difficult to perform three-class classification (positive, neutral, and negative) accurately. However, for sentence-level classification, the neutral class cannot be ignored because an opinion document can contain many sentences that express no opinion or sentiment. Note that neutral opinion often means no opinion or sentiment expressed.

The rise of artificial intelligence is mainly associated with software-based robotic systems such as mobile robots, unmanned aerial vehicles, and increasingly, semi-autonomous cars. However, the large gap between the algorithmic and physical worlds leaves existing systems still far from the vision of intelligent and human-friendly robots capable of interacting with and manipulating our human-centered world. The emerging discipline of machine intelligence (MI), unifying robotics and artificial intelligence, aims for trustworthy, embodiment-aware artificial intelligence that is conscious both of itself and its surroundings, adapting its systems to the interactive body it is controlling. The integration of AI and robotics with control, perception and machine-learning systems is crucial if these truly autonomous intelligent systems are to become a reality in our daily lives. Following a review of the history of machine intelligence dating back to its origins in the twelfth century, this chapter discusses the current state of robotics and AI, reviews key systems and modern research directions, outlines remaining challenges and envisages a future of man and machine that is yet to be built.

As robots and intangible autonomous systems increasingly interact with humans, we wonder who should be held accountable when things go wrong. This chapter examines the extra-contractual liability of users, keepers and operators for wrongs committed by autonomous systems. It explores how the concept of ‘wrong’ can be defined with respect to autonomous systems and what standard of care can reasonably be expected of them. The chapter also looks at existing accountability rules for things and people in various legal orders and explains how they can be applied to autonomous systems. From there, various approaches to a new liability regime are explored. Neither product liability nor the granting of a legal persona to robots is an adequate response to the current challenges. Rather, both the keeper and the operator of the autonomous system should be held strictly liable for any wrong committed, opening up the possibility of privileges being granted to the operators of machine-learning systems that learn from data provided by the system’s users.

The possible emulation of human creativity by various models of artificial intelligence systems is discussed in this chapter. In some instances, the degree of originality of creations using algorithms may surprise even human beings themselves. For this reason, copyright protection of ‘works’ created by autonomous systems is proposed, which would take account of both the fundamental contributions of computer science researchers and the investment in human and economic resources that give rise to these ‘works’.

Rapid progress in AI and robotics is challenging the traditional boundaries of law. Algorithms are widely employed to make decisions that have an increasingly far-reaching impact on individuals and society, potentially leading to manipulation, biases, censorship, social discrimination, violations of privacy and property rights, and more. This has sparked a global debate on how to regulate AI and robotics.