Comment moderation as a machine learning case study

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.

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Ask a computer to draw what it sees

If a computer can correctly identify an object (an apple, a tricycle) or an animal such as a zebra, can it produce a drawing of that object or animal? This is something most people can do, even if their drawing skills are minimal. After all, almost anyone can play Pictionary.

This 8-minute video shows us what happened when a programmer-artist reversed the process of an AI that recognizes objects and animals in digital images. I really admire the deft storytelling here.

Object recognition has improved amazingly in the past 10 years, but that does not mean these AI systems see the same way as a human does. In some cases, that might not matter at all. In other cases, it can mean the difference between life and death.

In yesterday’s post I mentioned the way a convolutional neural network (part of a machine learning system) processes an image through many stacked layers of detection units (sometimes called neurons), identifying edges and shapes that eventually lead to a conclusion that the image is likely to contain such-and-such an object, animal, or person. Today’s video shows a bit more about the training process that an AI goes through before it can perform these identifications.

Training is necessary in the type of machine learning called supervised learning. The training data (in this case, digital images of objects and animals) must be labeled in advance. That is, the system receives thousands of images labeled “tiger” before it is able to recognize a tiger in a random photo or video. If a system can identify 20 different animals, that system was trained on thousands of images of each animal.

If the system was never trained on tigers, it cannot recognize a tiger.

So today’s video gives us a nice glimpse into how and why that training works, and what its limitations are. What’s really fascinating to me, though, are the images produced by programmer-artist Tom White‘s system.

“I have created a drawing system that allows neural networks to produce abstract ink prints that reveal their visual concepts. Surprisingly, these prints are recognized not only by the neural networks that created them, but also universally across most AI systems which have been trained to recognize the same objects.”

—Tom White

In the video, you’ll see that humans cannot recognize what the AI drew. The rendering is too abstract, too unlike what we see and what we would draw ourselves. Note what White says, though, about other AI systems: they can recognize the object in these AI-produced drawings.

This is, I think, related to what is called adversarial AI, which I’ll discuss in a future post.

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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.
Include the author’s name (Mindy McAdams) and a link to the original post in any reuse of this content.

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