The need for interdisciplinary AI work

Discussions and claims about artificial intelligence often conflate quite different types of AI systems. People need both to understand and to shape the technology that’s part of their day-to-day lives, but understanding is a challenge when descriptions and terms are used inconsistently — or over-broadly. This idea is part of a 2019 essay titled Artificial Intelligence — The Revolution Hasn’t Happened Yet, published in the Harvard Data Science Review.

“Academia will also play an essential role … in bringing researchers from the computational and statistical disciplines together with researchers from other disciplines whose contributions and perspectives are sorely needed — notably the social sciences, the cognitive sciences, and the humanities,” wrote Michael I. Jordan, whose lengthy job title is Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley.

Jordan’s thoughtful, very readable essay is accompanied by 11 essay-length commentaries by various distinguished people and a rejoinder from Jordan himself.

In one of those commentaries, Barbara J. Grosz emphasized that “Rights of both individuals and society are at stake” in the shaping of technologies and practices built on AI systems. She said researchers and scholars in social science, cognitive science, and the humanities are vital participants in “determining the values and principles that will form the foundation” of a new AI discipline. Grosz is Higgins Research Professor of Natural Sciences at Harvard and the recipient of a lifetime achievement award from the Association for Computational Linguistics.

“When matters of life and well-being are at stake, as they are in systems that affect health care, education, work and justice, AI/ML systems should be designed to complement people, not replace them. They [the AI/ML systems] will need to be smart and to be good teammates,” Grosz wrote.

Concerns about ethical practices in the development of AI systems, in the collection and use of data, and in the deployment and use of technology based on AI systems are not new now, nor were they new in 2019. The idea of having the right mix of people in the room, at the table, however, has recently focused on racial, ethnic, socio-cultural and economic diversity more, perhaps, than on diversity of academic disciplines. Bringing in researchers from outside engineering, statistics, computer science, etc., can surface questions that would never arise in a group consisting only of engineers, statisticians, and computer scientists.

For me, those ideas dovetailed with a book chapter I happened to read on the previous day: “Beyond extraordinary: Theorizing artificial intelligence and the self in daily life,” in A Networked Self and Human Augmentics, Artificial Intelligence, Sentience (2018). Author Andrea L. Guzman wrote that in many senses, AI has become “ordinary” for us — one example is the voice assistants used by so many people in a completely everyday way. Intelligent robots and androids like Star Trek’s Lieutenant Commander Data, or evil world-controlling computer systems like Skynet in the Terminator movies, are part of a view of AI as “extraordinary” — which was the AI imagined for the future, before we had voice assistants and self-driving cars in the real world.

To be clear, there still exists the idea of extraordinary AI, super-intelligence or artificial general intelligence (AGI) — the “strong” AI that does not yet exist (and maybe never will). What Guzman describes is the way people today regard the AI–based tools and systems with which they interact. The AI that is, rather than the AI that might be.

How that connects to what both Jordan and Grosz wrote about interdisciplinary collaboration in AI development is this: Guzman is a journalism professor at Northern Illinois University, and she’s writing about the ways people communicate with a built system. Not interact with it, but communicate with it. When she investigated people’s perceptions and attitudes toward voice assistants, she realized that we don’t think about Siri and Alexa as intelligent devices. I was struck by Guzman’s description of how she initially approached her study and how her own perceptions changed.

“Conceptualizations of who we are in relation to AI, then, have formed around the myth that is AI” (Guzman, 2018, p. 87). “… I was applying a theory of the self that was developed around AI as extraordinary to the study of AI that was situated within the ordinary. The theoretical lens was an inadequate match for my subject” (Guzman, 2018, p. 90).

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What’s the use of machine learning?

I’m interested in applications of machine learning in journalism. This is natural, as my field is journalism. In the field of computer science, however, accolades and honors tend to favor research on new algorithms or procedures, or new network architectures. Applications are practical uses of algorithms, networks, etc., to solve real-world problems — and developing them often doesn’t garner the acclaim that researchers need to advance their careers.

Hannah Kerner, a professor and machine learning researcher at the University of Maryland, wrote about this in the MIT Technology Review. Her essay is aptly titled “Too many AI researchers think real-world problems are not relevant.”

“The first image of a black hole was produced using machine learning. The most accurate predictions of protein structures, an important step for drug discovery, are made using machine learning.”

—Hannah Kerner

Noting that applications of machine learning are making real contributions to science in fields outside computer science, Kerner (who works on machine learning solutions for NASA’s food security and agriculture program) asks how much is lost because of the priorities set by the journals and conferences in the machine learning field.

She also ties this focus on ML research for the sake of advancing ML to the seepage of bias out from widely used datasets into the mainstream — the most famous cases being in face recognition, with systems (machine learning models) built on flawed datasets that disproportionately skew toward white and male faces.

“When studies on real-world applications of machine learning are excluded from the mainstream, it’s difficult for researchers to see the impact of their biased models, making it far less likely that they will work to solve these problems.”

—Hannah Kerner

Machine learning is rarely plug-and-play. In creating an application that will be used to perform useful work — to make new discoveries, perhaps, or to make medical diagnoses more accurate — the machine learning researchers will do substantial new work, even when they use existing models. Just think, for a moment, about the data needed to produce an image of a black hole. Then think about the data needed to make predictions of protein structures. You’re not going to handle those in exactly the same way.

I imagine the work is quite demanding when a number of non–ML experts (say, the biologists who work on protein structures) get together with a bunch of ML experts. But either group working separately from the other is unlikely to come up with a robust new ML application. Kerner linked to this 2018 news report about a flawed cancer-detection system — leaked documents said that “instead of feeding real patient data into the software,” the system was trained on data about hypothetical patients. (OMG, I thought — you can’t train a system on fake data and then use it on real people!)

Judging from what Kerner has written, machine learning researchers might be caught in a loop, where they work on pristine and long-used datasets (instead of dirty, chaotic real-world data) to perfect speed and efficiency of algorithms that perhaps become less adaptable in the process.

It’s not that applications aren’t getting made — they are. The difficulty lies in the priorities for research, which might dissuade early-career ML researchers in particular from work on solving interesting and even vital real-world problems — and wrestling with the problems posed by messy real-world data.

I was reminded of something I’ve often heard from data journalists: If you’re taught by a statistics professor, you’ll be given pre-cleaned datasets to work with. (The reason being: She just wants you to learn statistics.) If you’re taught by a journalist, you’ll be given real dirty data, and the first step will be learning how to clean it properly — because that’s what you have to do with real data and a real problem.

So the next time you read about some breakthrough in machine learning, consider whether it is part of a practical application, or instead, more of a laboratory experiment performed in isolation, using a tried-and-true dataset instead of wild data.

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Uses of AI in journalism

Part of my interest in AI centers on the way it is presented in online, print and broadcast media. Another focal point for me is how journalism organizations are using AI to do journalism work.

At the London School of Economics, a project named JournalismAI mirrors my interests. In November 2019 they published a report on a survey of 71 news organizations in 32 countries. They describe the report as “an introduction to and discussion of journalism and AI.”

Above: From the JournalismAI report

Many people in journalism are aware of the use of automation in producing stories on financial reports, sports, and real estate. Other applications of AI (mostly machine learning) are less well known — and they are numerous.

Above: From page 32 in JournalismAI report

Another resource available from JournalismAI is a collection of case studies — in the form of a Google sheet with links to write-ups about specific projects at news organizations. This list is being updated as new cases arise.

Above: From the JournalismAI case studies

It’s fascinating to open the links in the case studies and discover the innovative projects under way at so many news organizations. Journalism educators (like me) need to keep an eye on these developments to help us prepare journalism students for the future of our field.

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AI in Media and Society by Mindy McAdams is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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