As a descriptor, artificial intelligence (AI) is polysemous and problematic. Like the muddled "big data" before it, this term du jour tends to be invoked broadly and haphazardly, by boosters as well as critics in some cases, making it difficult to discern exactly what AI is supposed to represent in the world, let alone how it is intended to work as a means of performing human tasks-from recognizing images, blocking spam email, and serving up algorithmic newsfeed recommendations to the more complicated challenges of autonomously flying drones and driving cars. Because science fiction and Hollywood so often depict AI in the form of sentient machines, many people associate AI with "thinking" robots or computers that can mimic human reasoning and behavior with uncanny accuracy-though, as Meredith Broussard points out in this special forum and in her book Artificial Intelligence: How Computers Misunderstand the World (Broussard, 2018), nothing could be further from the truth.Rather, AI more narrowly refers to a branch of computer science focused on simulating human intelligence, one that recently has been especially engaged in the subfield of machine learning: the training of a machine to learn from data, recognize patterns, and make subsequent judgments, with little to no human intervention. More narrowly still, and turning to the particular relevance for this journal, "communicative AI" may refer to AI technologies-such as conversational agents, social robots, and automated-writing software-that are designed to function as communicators, rather than merely mediators of human communication, often in ways that confound traditional conceptions of communication theory and practice . Amid this confusion surrounding the definition of AI and its emergent role in everyday life, this special forum attempts to carve out an elaboration on AI in the context of journalism-a key domain through which to illustrate many of the opportunities and challenges that AI presents for the broader realm of communication, media, and society.The implications of AI for journalism must be foregrounded in the larger context of the digitization of media and public life-a transition to apps, algorithms, social media, and the like in ways that have transformed journalism as institution: undercutting business models, upending work routines, and unleashing a flood of information alternatives to news, among other things. In that sense, AI technologies, regardless of how transformative they yet prove to be in the short, medium, or long term, may be understood as part of a broader story of journalism's reconfiguration in relation to computation.

A guide to understanding the inner workings and outer limits of technology and why we should never assume that computers always get it right. In Artificial Unintelligence, Meredith Broussard argues that our collective enthusiasm for applying computer technology to every aspect of life has resulted in a tremendous amount of poorly designed systems. We are so eager to do everything digitally—hiring, driving, paying bills, even choosing romantic partners—that we have stopped demanding that our technology actually work. Broussard, a software developer and journalist, reminds us that there are fundamental limits to what we can (and should) do with technology. With this book, she offers a guide to understanding the inner workings and outer limits of technology—and issues a warning that we should never assume that computers always get things right. Making a case against technochauvinism—the belief that technology is always the solution—Broussard argues that it's just not true that social problems would inevitably retreat before a digitally enabled Utopia. To prove her point, she undertakes a series of adventures in computer programming. She goes for an alarming ride in a driverless car, concluding “the cyborg future is not coming any time soon”; uses artificial intelligence to investigate why students can't pass standardized tests; deploys machine learning to predict which passengers survived the Titanic disaster; and attempts to repair the U.S. campaign finance system by building AI software. If we understand the limits of what we can do with technology, Broussard tells us, we can make better choices about what we should do with it to make the world better for everyone.

When technology reinforces inequality, it's not just a glitch—it's a signal that we need to redesign our systems to create a more equitable world. The word “glitch” implies an incidental error, as easy to patch up as it is to identify. But what if racism, sexism, and ableism aren't just bugs in mostly functional machinery—what if they're coded into the system itself? In the vein of heavy hitters such as Safiya Umoja Noble, Cathy O'Neil, and Ruha Benjamin, Meredith Broussard demonstrates in More Than a Glitch how neutrality in tech is a myth and why algorithms need to be held accountable. Broussard, a data scientist and one of the few Black female researchers in artificial intelligence, masterfully synthesizes concepts from computer science and sociology. She explores a range of examples: from facial recognition technology trained only to recognize lighter skin tones, to mortgage-approval algorithms that encourage discriminatory lending, to the dangerous feedback loops that arise when medical diagnostic algorithms are trained on insufficiently diverse data. Even when such technologies are designed with good intentions, Broussard shows, fallible humans develop programs that can result in devastating consequences. Broussard argues that the solution isn't to make omnipresent tech more inclusive, but to root out the algorithms that target certain demographics as “other” to begin with. With sweeping implications for fields ranging from jurisprudence to medicine, the ground-breaking insights of More Than a Glitch are essential reading for anyone invested in building a more equitable future.

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Born-digital news content is increasingly becoming the format of the first draft of history. Archiving and preserving this history is of paramount importance to the future of scholarly research, but many technical, legal, financial, and logistical challenges stand in the way of these efforts. This is especially true for news applications, or custom-built websites that comprise some of the most sophisticated journalism stories today, such as the “Dollars for Docs” project by ProPublica. Many news applications are standalone pieces of software that query a database, and this significant subset of apps cannot be archived in the same way as text-based news stories, or fully captured by web archiving tools such as Archive-It. As such, they are currently disappearing. This paper will outline the various challenges facing the archiving and preservation of born-digital news applications, as well as outline suggestions for how to approach this important work.