Section 230 of the Communications Decency Act is often called "The Twenty-Six Words That Created the Internet." This 1996 law grants platforms broad legal immunity against claims arising from both third-party content that they host, and good-faith content moderation decisions that they make. Most observers agree that without Section 230 immunity, or some variant of it, the modern internet and social media could not exist. Nonetheless, Section 230 has been subject to vociferous criticism, with both Presidents Biden and Trump having called for its repeal. Critics claim that Section 230 lets platforms have it both ways, leaving them free to host harmful content but also to block any content they object to. This chapter argues that criticisms of Section 230 are largely unwarranted. The diversity of the modern internet, and ability of ordinary individuals to reach broad audiences on the internet, would be impossible without platform immunity. As such, calls for repeal of or major amendments to Section 230 are deeply unwise. The chapter concludes by pointing to important limits on Section 230 immunity and identifying some narrow amendments to Section 230 that may be warranted.

As Chapter 1 discusses, one of the most consistent conservative critiques of social media platforms is that social media is biased against conservative content. A common policy proposal to address this is to regulate such platforms as common carriers. Doing so would require social media platforms to host, on a nondiscriminatory basis, all legal user content and to permit all users to access platforms on equal terms. While this seems an attractive idea – after all, who could object to nondiscrimination – it is not. For one thing, the Supreme Court has now recognized that social media platforms possess "editorial rights" under the First Amendment to control what content they carry, block, and emphasize in their feeds. So, regulating platforms as common carriers, as Texas and Florida have sought to do, is unconstitutional. It is also a terrible idea. Requiring platforms to carry all content on a nondiscriminatory basis, even if limited to legal content (which it would be hard to do) would flood user feeds with such lawful-but-awful content as pornography, hate speech, and terrorist propaganda. This in turn would destroy social media as a usable medium, to the detriment of everyone.

This chapter introduces the concept of metacognition from a cognitive perspective, where it refers to knowledge and mental processes that operate on one’s own cognition. We review different forms of metacognition that involve distinct types of explicit reasoning and automatic processes, as well as various measures and functional benefits. We articulate four conjectures regarding the nature of metacognition in the specific context of the ACT-R cognitive architecture: (1) it involves extracting information about processes in cognitive modules; (2) the information is quantitative and approximate rather than symbolic; (3) the metacognitive information is available in working memory for cognitive processing; and (4) general cognitive processes are sufficient to respond to a situation detected by metacognitive monitoring. We illustrate these principles with examples of past work involving neuro-symbolic models of perception and introspection into declarative models of decision-making. Finally, we situate this approach within the context of theories such as predictive coding and the Common Model of Cognition encompassing other cognitive architectures.

Metacognitive AI is closely connected to certifiable AI and trustworthy AI, the two areas focusing on equipping AI with trustworthy guarantees in high-stake domains. This chapter provides a systematic overview, tutorial, and discussion of the certified approaches in trustworthy deep learning. The chapter introduces essential terminologies, core methodologies, and representative applications of certified approaches. We believe that certified approaches, as a prerequisite for deploying AI in high-stake and safety-critical applications, would be an essential tool in metacognitive AI, and we hope that this chapter can inspire readers to further advance the field of certifiable trustworthiness for metacognitive AI.

This chapter presents a metacognitive AI approach via formal verification and repair of neural networks (NNs). We observe that a neural network repair is a form of metacognition, where trained AI systems relearn until specifications hold. We detail Veritex, a tool for reachability analysis and repair of deep NNs (DNNs). Veritex includes methods for exact and over-approximative reachability analysis of DNNs. The exact methods can compute the exact output reachable domain, as well as the exact unsafe input space that causes safety violations of DNNs. Based on the exact unsafe input–output reachable domain, Veritex can repair unsafe DNNs on multiple safety properties with negligible performance degradation, by updating the DNN parameters via retraining. Veritex primarily addresses the synthesis of provably safe DNNs, which is not yet significantly addressed in the literature. Veritex is evaluated for safety verification and DNN repair. Benchmarks for verification include ACAS Xu, and benchmarks for the repair include an unsafe ACAS Xu and an unsafe agent trained in deep reinforcement learning (DRL), where it is able to modify the NNs until safety is proven.

In this chapter, we use task failure as a trigger to engage in metacognitive processes. We present a procedure by which an agent may exploit failure in the zero-shot outputs of LLMs as a trigger to investigate alternative solutions to the problem using object interactions and knowledge of the object semantics. We additionally propose a method through which knowledge gained from the object interactions can be distilled back into the LLM and avenues for future research.

We investigate the incorporation of metacognitive capabilities into Machine Learning Integrated with Network (MLIN) systems and develop machine Learning Integrated with Knowledge (mLINK) strata. This stratum is aimed at integrating knowledge obtained from multiple MLIN elements and reflecting on the ML application performance outcomes in order to provide feedback on metacognitive actions aimed at ensuring performance and improving ML application robustness towards Data Quality (DQ) variations. We discuss multiple use cases to show how the knowledge on the interrelationships between MLIN components, DQ, and ML application performance can be generated and employed by mLINK. We elaborate on how this knowledge is integrated into mLINK to produce metaknowledge, deemed as recommendations on adaptation actions or strategies needed. We define the process of employing these recommendations by mLINK as metacognition and describe multiple examples of utilizing these metacognitive strategies in practice, such as optimizing the data collection; reflection on DQ; DQ assurance; enhanced transfer learning; and Federated Learning for enhancing security, privacy, collaboration, and communication in MLIN.

To enhance understanding and collaboration with autonomous agents, it is crucial to construct a representation of their task strategies that integrates interpretability, monitoring, and formal reasoning. This dual-purpose representation fosters human comprehension and enables automated analytical processes. We achieve this balance by formalizing task strategies through temporal logic formulas. Recent trends emphasize inferring temporal logic formulas from data to explain system behaviors and assess autonomous agents’ competencies. Our methodology relies on positive and negative examples from system observations to construct a concise temporal logic formula consistent with the data. However, existing approaches often overlook real-world data’s noise and uncertainties, limiting practical deployment. Addressing this, we analyze labeled trajectories and aim to infer interpretable formulas that minimize misclassification loss. To tackle data uncertainties, we focus on labeled interval trajectories. Our algorithm maximizes the worst-case robustness margin, enhancing formula robustness and ensuring the adaptability and reliability of temporal logic inference in real-world applications.

One of the central aspects of metacognitive AI is the AI agent’s ability to reason about its own behavior. In particular, for AI systems to be deployed in real-world applications with high impact, it is crucial that we can reason about and guarantee their fairness and robustness. Here, we provide a probabilistic reasoning framework to audit and enforce fairness of automated decision-making systems, using classifiers as the main example, while being robust to uncertainties and noise in the distribution.

This brief conclusion summarizes the main thesis of the book, noting that both conservative and progressive critiques of social media lack strong empirical justifications, and that many if not most of the regulatory proposals directed at social media are not only likely to be found unconstitutional, but are also wrong-headed. It then argues that it is time we all accept that the old, pre-social media world of gatekeepers is over; and further, that this development has important, positive implications for the democratization of public discourse in ways that free speech theory supports. Finally, the Conclusion analogizes the modern hysteria over the growth of social media to earlier panics over changes in communications technology, such as the inventions of the printing press and of moving pictures. As with those earlier panics, this one too is overblown and ignores the positive potential impacts of technological change.