Simone Natale begins Deceitful Media by discussing a demonstration of artificial intelligence (AI) technology (Google Duplex) and uses the resulting controversy as a springboard to address broader ethical and societal questions surrounding AI. This relatable, specific example immediately grounds the reader, effectively capturing their interest and setting the stage for the deeper exploration to come. The controversy surrounding Google Duplex serves as a microcosm for the book’s larger exploration of the role of deception in AI. By outlining the criticism and doubts that followed Duplex’s demonstration, Natale clearly delineates the public debate and controversy as a jumping-off point for his argument, illustrating the binary way in which AI is often discussed.
Natale efficiently sets the goal for the book by clearly articulating the central issues, framing the debate, introducing key concepts, and outlining the book’s objectives. This approach effectively prepares the reader for a comprehensive and critical examination of deception in AI and its broader implications for society. Early in the introduction, he introduces the concept of “banal deception” as a fundamental aspect of AI technologies. This concept is crucial to the book’s thesis and is explained in a way that is both accessible and intriguing, promising a novel lens through which to view AI. Natale then sets a historical context for the discussion by referencing Alan Turing and the Turing Test, situating the current debate within a broader historical narrative, which not only adds depth to the discussion but also legitimizes his approach by showing that these questions have long-standing roots in the field of AI.
He explicitly states that the book’s aim is to recalibrate the relationship between deception and AI. This objective is laid out in clear terms, indicating that the book will explore how AI developers have historically used knowledge about users to create meaningful interactions, thus providing a clear roadmap for the reader. By framing the debate around AI not as a question of whether AI can mimic human intelligence but how it already alters social interactions and perceptions, he sets an ambitious goal for the book. This broadens the scope of the discussion and signals to the reader that the book will cover new ground in understanding AI’s impact on society.
Natale takes an interdisciplinary approach, drawing from fields such as social psychology, cognitive science, and history to support his arguments, creating a well-rounded and nuanced exploration of the topic that will appeal to readers from a variety of backgrounds. The introduction concludes with an invitation to the reader to engage critically with the topic, suggesting that the book will not only present information but also encourage reflection on the ethical and cultural implications of AI technologies. This is why I am confident that the book will be useful for scholars and students across disciplines such as computer science, psychology, sociology, and history, as well as AI developers and policymakers. It offers a nuanced understanding of AI’s societal impact, ethical considerations, and the role of deception, making it valuable for anyone interested in the future of AI technology.
In the first chapter, Natale offers a thought-provoking exploration of the Turing Test, elegantly weaving historical anecdotes with the philosophical underpinnings of AI. By drawing parallels between nineteenth-century Spiritualism and the emergence of AI, he sets a captivating stage to delve into the complex interplay between human perception and machine intelligence. Faraday’s debunking of Spiritualist phenomena serves as a compelling precursor to understanding the human-centric approach to AI, emphasizing the role of human interaction and belief in attributing intelligence to machines. Additionally, the examination of the Turing Test goes beyond its technical aspects, presenting it as a reflection on human cognition, perception, and the willingness to accept machines as intelligent entities. This narrative shift from a focus on machine capabilities to human perception and communication is insightful, highlighting the relational nature of AI and its dependence on human engagement.
Continuing, Natale provides a comprehensive overview of the historical development of AI and human–computer interaction (HCI), emphasizing the role of perception and deception in shaping public and scientific understandings of AI. While this chapter adeptly highlights the foundational optimism of AI pioneers and the subsequent challenges in aligning AI capabilities with public expectations, the chapter could benefit from a deeper examination of contemporary AI applications and ethical considerations. Additionally, more explicit connections between historical developments and current debates in AI ethics and governance would enhance its relevance. The narrative would also benefit from incorporating diverse perspectives beyond the dominant Western-centric view of AI’s evolution.
In the next chapter, Natale explores ELIZA’s development by Joseph Weizenbaum and its profound influence on AI and chatbot evolution. He delves into the ELIZA effect, highlighting how this pioneering chatbot spurred debates on AI’s ethical implications and HCI. By illustrating how users attributed more depth and cognition to ELIZA than it was technically capable of, Natale argues that this projection is not just a feature of early AI like ELIZA but a fundamental aspect of how people interact with and perceive conversational agents. This insight challenges us (readers) to reflect on our expectations and interpretations of AI, highlighting the importance of critically assessing the capabilities and limitations of AI technologies. The ELIZA effect thus serves as a cornerstone for discussions about the ethical implications, societal impacts, and future direction of AI development. Further, he traces the evolution of AI from isolated experiments to integral components of everyday software, highlighting the shift in public perception and the challenges of AI integration. It is revealed that AI’s embedding in software has shaped HCIs, emphasizing the blend of technical achievements with social and cultural implications in the journey toward modern AI systems like voice assistants.
Later in the book, Natale also delves into the Loebner Prize (an annual competition to determine a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human), critiquing its narrow focus on linguistic mimicry over genuine AI advancement. Natale highlights the competition’s reliance on deception and character creation to convince judges of AI’s human-like abilities, underscoring the contest’s insights into human–AI interaction dynamics rather than technical progress. The discussion extends to ethical concerns regarding AI’s use of stereotypes, urging a more nuanced understanding of intelligence that includes emotional and social dimensions.
Lastly, he offers a thought-provoking critique of voice assistants like Siri, revealing the complexities of user interactions and the underlying corporate motives. Natale insightfully discusses the illusion of control and the anthropomorphization of technology, highlighting the ethical concerns surrounding user autonomy and data exploitation. This chapter could benefit from a more detailed exploration of user experiences across different demographics to fully understand the impact of voice assistants. Additionally, including potential solutions or guidelines for ethical design practices would provide a more balanced perspective and actionable insights for improving the relationship between users and voice assistants.
Overall, the book provides a profound exploration of AI through the lens of “banal deception,” intricately dissecting AI’s societal, ethical, and psychological impacts. It challenges readers to critically assess their interactions with AI, highlighting the subtle ways AI integrates into human life and influences behavior. It advocates for ethical development and usage of AI, emphasizing the need for awareness and sophistication among users to navigate the complexities of modern AI technologies effectively.
Notes
- Mansa Narain is a budding film scholar, a filmmaker, and an educator from India. She has an MA in cinema studies from the Tisch School of the Arts, New York University, and a Bachelor of Media Studies degree from Symbiosis International University, Pune, India. In her doctoral research at the University of Texas at Austin, she aims to investigate the influence of AI-engineered hyper-personalization through machine learning algorithms on the transnational distribution of this media content on SVOD platforms like Netflix and Amazon Prime Video. ⮭