hype – AI in Media and Society

Sayash Kapoor and Arvind Narayanan are writing a book about AI. The title is AI Snake Oil. They’ve been writing a Substack newsletter about it, and on Sept. 30 they published a post titled Eighteen pitfalls to beware of in AI journalism. Narayanan is a computer science professor at Princeton, and Kapoor is a former software engineer at Facebook and current Ph.D. student at Princeton.

“There is seldom enough space in a news article to explain how performance numbers like accuracy are calculated for a given application or what they represent. Including numbers like ‘90% accuracy’ in the body of the article without specifying how these numbers are calculated can misinform readers …”
—Kapoor and Narayanan

They made a checklist, in PDF format, to accompany the post. The list is based on their analysis of more than 50 articles from five major publications: The New York Times, CNN, the Financial Times, TechCrunch, and VentureBeat. In the Substack post, they linked to three annotated examples — one each from The New York Times, CNN, and the Financial Times. The annotated articles are quite interesting and could form a base for great discussions in a journalism class. (Note, in the checklist, the authors over-rely on one article from The New York Times for examples.)

Their goals: The public should be able to detect hype about AI when it appears in the media, and their list of pitfalls could “help journalists avoid them.”

“News articles often cite academic studies to substantiate their claims. Unfortunately, there is often a gap between the claims made based on an academic study and what the study reports.”
—Kapoor and Narayanan

Kapoor and Narayanan have been paying attention to the conversations around journalism and AI. One example is their link to How to report effectively on artificial intelligence, a post published in 2021 by the JournalismAI group at the London School of Economics and Political Science.

I was pleased to read this post because it neatly categorizes and defines many things that have been bothering me in news coverage of AI breakthroughs, products, and even ethical concerns.

There’s far too much conflation of AI abilities and human abilities. Words like learning, thinking, guessing, and identifying all serve to obscure computational processes that are only mildly similar to what happens in human brains.
“Claims about AI tools that are speculative, sensational, or incorrect”: I am continually questioning claims I see reported uncritically in the news media, with seemingly no effort made to check and verify claims made by vendors and others with vested interests. This is particularly bad with claims about future potential — every step forward nowadays is implied to be leading to machines with human-level intelligence.
“Limitations not addressed”: Again, this is slipshod reporting, just taking what the company says about its products (or researchers about their research) and not getting assessments from disinterested parties or critics. Every reporter reporting on AI should have a fat file of critical sources to consult on every story — people who can comment on ethics, labor practices, transparency, and AI safety.

Another neat thing about Kapoor and Narayanan’s checklist: Journalism and mass communication researchers could adapt it for use as a coding instrument for analysis of news coverage of AI.

AI in Media and Society by Mindy McAdams is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Include the author’s name (Mindy McAdams) and a link to the original post in any reuse of this content.

I guess it should be no surprise that people want to talk about sentient machines when the term artificial intelligence has become more common than bread and butter. I was hoping this July 2022 article in The Wall Street Journal would go further than it does to assert that there are no grounds at all for talking about sentience in today’s AI, but I was disappointed. The two authors at least did not try to “both sides” the spurious claims.

First, they state that there are a lot of exaggerated claims from companies selling so-called AI products and “solutions.” Second, they touch on the danger this holds for policy decisions — when our elected officials, lawyers, judges, etc., don’t have a clear idea of how AI systems work, they are bound to make poor laws and poor rulings. AI ethicists warn that the hype is “distorting policy makers’ views of the power and fallibility” of AI systems. The reporters quote Oren Etzioni, CEO of the nonprofit Allen Institute for Artificial Intelligence, as saying policy makers are “well-intentioned and ask good questions, but they’re not super well-informed.”

“The belief that AI is becoming — or could ever become — conscious remains on the fringes in the broader scientific community, researchers say.”
—Hao and Kruppa, in “Tech Giants Pour Billions into AI, but Hype Doesn’t Always Match Reality”

The WSJ article also covers the claims of a (now former) Google engineer who claimed the LaMDA chatbot is sentient. On July 22, The Verge was among several news organizations reporting that the engineer has been fired. That article links to a YouTube video that explains “how LaMDA works and how it could produce the responses that convinced [the engineer] without actually being sentient.”

I was dismayed that the media gave so much attention to the engineer’s claims — which he never should have made in the first place, being an engineer. If you take some time to learn about how chatbots are created (or voice assistants — my undergrad college students are decidedly unimpressed with Siri and Alexa), you’ll understand that they cannot possibly have sentience. These conversational systems are prediction machines — they predict “the likelihood of a token (character, word or string) given either its preceding context or … its surrounding context” (source: Bender et al., 2021). The results can be astoundingly good, or hilariously awful. Either way, the process that generates the responses is the result of computational predictions and not the product of a sentient being.

The same is true of the output from DALL-E (and the newer DALL-E 2), which creates an image based on a text description. The less you know about today’s algorithms and powerful AI hardware, the more likely you are to wonder whether there’s a humanlike intelligence behind the system that can produce these graphics. The output is extraordinary and literally was not even possible just a few years ago. What makes it possible today are the combination of massively parallel computational structures and the algorithms designed by humans to enable really, really good guesses (predictions) at what images would best match the descriptive text.

When I say we shouldn’t talk about sentience, I am being a bit coy. I do think we should be talking about what constitutes intelligence in humans and animals, how we know an entity is conscious, and what it means to think, to feel, to perceive the world. I don’t think we should be looking for sentience in our computers — not today and not for a long, long time to come. It distracts us from what today’s AI systems are actually doing, which is making guesses that then affect real sentient humans’ real lives.

Tag: hype

What journalists get wrong about AI

We should not talk about sentience