In the latest JournalismAI newsletter, a list of recommendations called “Reporting on AI Effectively” shares wisdom from several journalists who are reporting about a range of artificial intelligence and machine learning topics. The advice is grouped under these headings:
Build a solid foundation
Beat the hype
Complicate the narrative
Be compassionate, but embrace critical thinking
Karen Hao, senior AI editor at MIT Technology Review — whose articles I read all the time! — points out that to really educate yourself about AI, you’re going to need to read some of the research papers in the field. She also recommends YouTube as a resource for learning about AI — and I have to agree. I’ve never used YouTube so much to learn about a topic before I began studying AI.
The post also offers good advice about questions a reporter should ask about AI research and new developments int the field.
Some investigations in the public interest require journalists to search through large quantities of official documents. Often the set of documents is very diverse — that is, the format, structure, and even language of the documents might vary greatly.
One of the more impressive investigations I know of is the ongoing Implant Files project, conducted originally by 250 journalists in 36 countries. The purpose: To examine how medical devices (specifically, those implanted into human bodies) are “tested, approved, marketed, and monitored” (source). I’ve heard this project discussed at conferences, and I’m full of admiration for the editors and reporters involved, led by the International Consortium of Investigative Journalists (ICIJ).
At the heart of the investigation, with its first results published in 2018, was “an analysis of more than 8 million device-related health records, including death and injury reports and recalls.”
“The entire process involved text mining, clustering, feature selection, association rules and classification algorithms to identify events not always described consistently in different parts of the data.”
These implanted devices — hip replacements, defibrillators, breast implants, intraocular lenses, and more — are used all around the world. When something goes wrong and a product recall is issued, however, the news might not spread to all the locations where the devices continue to be used in new surgeries for new patients. Moreover, people who already have a faulty implant might not be notified. This is why a global investigation was sorely needed.
In 2018, ICIJ shared “a publicly searchable database of more than 70,000 recalls and safety warnings in 11 countries.” The project has continued since then, and the database now contains “more than 120,000 recalls, safety alerts and field safety notices” for medical devices. Throughout 2019, thousands more records were added.
A December 2018 post details the team’s data methodology for the Implant Files. First, journalists had to get the records — and often, their legitimate requests for public records were denied. Of the 8 million device-related records they managed to obtain, 5.4 million came from the U.S. Food and Drug Administration.
The records “describe cases where a device is suspected to have caused or contributed to a serious injury or death or has experienced a malfunction that would likely lead to harm if it were to recur.”
The value in these records was in the connections — connections among cases, and connections among devices. The ICIJ analysis concluded that “devices that broke, misfired, corroded, ruptured or otherwise malfunctioned after implantation or use were linked to more than 1.7 million injuries and nearly 83,000 deaths” in just one decade.
To identify the records that involved a patient’s death, it was necessary for humans to determine various terms and phrasing used instead of the word “death” in the documents. Eventually they developed “a set of more than 3,400 key phrases” that were used to train the machine learning system. After using that model to extract the relevant records, it was necessary to run them through another algorithm configured to determine whether the implant device had contributed to the death.
Continuing my summary of the lessons in Introduction to Machine Learning from the Google News Initiative, today I’m looking at Lesson 5 of 8, “Training your Machine Learning model.” Previous lessons were covered here and here.
Now we get into the real “how it works” details — but still without looking at any code or computer languages.
The “lesson” (actually just a text) covers a common case for news organizations: comment moderation. If you permit people to comment on articles on your site, machine learning can be used to identify offensive comments and flag them so that human editors can review them.
With supervised learning (one of three approaches included in machine learning; see previous post here), you need labeled data. In this case, that means complete comments — real ones — that have already been labeled by humans as offensive or not. You need an equally large number of both kinds of comments. Creating this dataset of comments is discussed more fully in the lesson.
You will also need to choose a machine learning algorithm. Comments are text, obviously, so you’ll select among the existing algorithms that process language (rather than those that handle images and video). There are many from which to choose. As the lesson comes from Google, it suggests you use a Google algorithm.
In all AI courses and training modules I’ve looked at, this step is boiled down to “Here, we’ll use this one,” without providing a comparison of the options available. This is something I would expect an experienced ML practitioner to be able to explain — why are they using X algorithm instead of Y algorithm for this particular job? Certainly there are reasons why one text-analysis algorithm might be better for analyzing comments on news articles than another one.
What is the algorithm doing? It is creating and refining a model. The more accurate the final model is, the better it will be at predicting whether a comment is offensive. Note that the model doesn’t actually know anything. It is a computer’s representation of a “world” of comments in which some — with particular features or attributes perceived in the training data — are rated as offensive, and others — which lack a sufficient quantity of those features or attributes — are rated as not likely to be offensive.
The lesson goes on to discuss false positives and false negatives, which are possibly unavoidable — but the fewer, the better. We especially want to eliminate false negatives, which are offensive comments not flagged by the system.
“The most common reason for bias creeping in is when your training data isn’t truly representative of the population that your model is making predictions on.”
—Lesson 6, Bias in Machine Learning
Lesson 6 in the course covers bias in machine learning. A quick way to understand how ML systems come to be biased is to consider the comment-moderation example above. What if the labeled data (real comments) included a lot of comments offensive to women — but all of the labels were created by a team of men, with no women on the team? Surely the men would miss some offensive comments that women team members would have caught. The training data are flawed because a significant number of comments are labeled incorrectly.
There’s a pretty good video attached to this lesson. It’s only 2.5 minutes, and it illustrates interaction bias, latent bias, and selection bias.
Lesson 6 also includes a list of questions you should ask to help you recognize potential bias in your dataset.
It was interesting to me that the lesson omits a discussion of how the accuracy of labels is really just as important as having representative data for training and testing in supervised learning. This issue is covered in ImageNet and labels for data, an earlier post here.
The separation of machine learning into three different approaches — supervised learning, unsupervised learning, and reinforcement learning — is standard (Lesson 3). In keeping with the course’s focus on journalism applications of ML, the example given for supervised learning is The Atlanta Journal-Constitution‘s deservedly famous investigative story about sex abuse of patients by doctors. Supervised learning was used to sort more than 100,000 disciplinary reports on doctors.
The example of unsupervised learning is one I hadn’t seen before. It’s an investigation of short-term rentals (such as Airbnb rentals) in Austin, Texas. The investigator used locality-sensitive hashing (LSH) to group property records in a set of about 1 million documents, looking for instances of tax evasion.
The main example given for reinforcement learning is AlphaGo (previously covered in this blog), but an example from The New York Times — How The New York Times Is Experimenting with Recommendation Algorithms — is also offered. Reinforcement learning is typically applied when a clear “reward” can be identified, which is why it’s useful in training an AI system to play a game (winning the game is a clear reward). It can also be used to train a physical robot to perform specified actions, such as pouring a liquid into a container without spilling any.
Also in Lesson 3, we find a very brief description of deep learning (it doesn’t mention layers and weights). and just a mention of neural networks.
“What you should retain from this lesson is fairly simple: Different problems require different solutions and different ML approaches to be tackled successfully.”
—Lesson 3, Different approaches to Machine Learning
The examples in this lesson are really good, so maybe you should just read it directly. You’ll learn about a variety of unusual stories that could only be told when journalists used machine learning to augment their reporting.
“Machine learning is not magic. You might even say that it can’t do anything you couldn’t do — if you just had a thousand tireless interns working for you.”
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.”
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.
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.
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.