Artificial Intelligence

Sending AI Off to School

Can machine learning really free up time dedicated to grunt work, giving your educators more time for working with students?

Plenty of discussions about the use of artificial intelligence talk about how AI could help educators by shrinking the amount of time they have to spend on the trivia that pervades their work and freeing them up to focus on the job of teaching. In the latest CoSN IT leadership survey, more than half of respondents (55 percent) said that AI would have a significant or even transformational impact on teaching and learning within the next five years, if privacy issues can be addressed to everybody's satisfaction.

In a recent session delivered during the CoSN2020 virtual conference, Girard Kelly gave a preview of what the use of AI could look like by using an example from his own employer. Kelly works as counsel and director of the privacy program at nonprofit Common Sense Media. The education division of Common Sense serves as a vetting tool to help teachers choose educational software based on learning ratings, community ratings and privacy ratings. It's that last category that's especially difficult to nail down, Kelly said.

The Problem with Privacy Policies

The problem Common Sense faced was in trying to help teachers and families make better decisions about the software they use, based on the privacy policies published by the companies that produce those programs. The privacy evaluation process is intense and has both "qualitative and quantitative" aspects. The organization has to evaluate whether it exists in the first place, whether the privacy practices are transparent, whether there's advertising or user tracking in the product, whether the company does data collection and sells that data, and a whole bunch of other issues.

Common Sense uses a three-tier rating system: Blue means pass (about 20 percent of products fall into this rating); red means fail (about 10 percent); and orange means proceed with caution (about 70 percent).

The existence of the privacy rating in itself has already made an impact, according to Kelly. Of the 750 products already evaluated, about half of the companies have updated their policies based on the ratings they've initially been given.

A big part of the job of developing each rating involves having legal experts sit for hours and read through the privacy policy posted by the companies. Many of the policies run "dozens of pages long" — some 50 to 60 pages — and they're full of legalese, Kelly noted. "We found that nobody actually reads the privacy policies because it's really hard and it takes a really long time."

Common Sense wondered whether machine learning might help speed up the privacy policy evaluation process by identifying the elements that were essential in a well-written privacy policy.

Some Machine Learning Terms Worth Understanding

As Kelly explained, the very concept of AI covers a lot of ground, encompassing machine learning, knowledge graphs and expert systems. In the case of ed tech, machine learning dominates by a wide margin. What distinguishes it from the other flavors of AI is "its ability to modify itself when exposed to more data." The more data that's fed into a machine learning model, the more accurate its recommendations or findings.

Machine learning itself has subsets too, primarily broken down into how categorization — the task to be done — takes place.

"Supervised learning" references the use of prior knowledge of the output. The model already knows what output is desired, and AI is used to sort out or classify the input that will lead to that output. Once the model learns the formula, it can stop learning and do its job. According to Kelly, supervised learning crops up in identity fraud detection, image identification and weather forecasting, among other applications. When you identify the squares in a reCAPTCHA that show the crosswalk, for instance, you're helping the model learn how to identify certain imagery in the data.

In "unsupervised learning," you have the input data but you don't know what the output should look like. The goal for the model is to learn more about the data and reveal patterns to you. This proves useful in recommendation systems, targeted marketing and big data visualization, as examples.

There's a gray area between those two approaches, which is called "semi-supervised" machine learning. You have a lot of data for input, and some of the data is identified. Human experts help the model evolve by undertaking identification until it can learn to a sufficient level of competency.

A third category of machine learning is called "reinforcement learning." This is the process by which a learning algorithm (the "agent") learns how to learn, based on whether it gets a positive or negative response: It loses the chess game or it wins. There's no training data plugged in; the training happens as the process runs. According to the experts, this most closely resembles how humans learn too. (Think: small child and hot stove.) Reinforcement learning shows up in gaming and products doing real-time decision-making, performing robot navigation or undertaking learning tasks.

Machine Learning to the Rescue?

There are two challenges to the effective use of machine learning for the kind of work Common Sense had in mind:

  • First, even humans "go back and forth" on various issues of privacy policy interpretation. When the humans can't agree, "we can't expect machines to help us," noted Kelly.

  • Second, privacy policies don't follow a consistent order. Companies' privacy practices aren't laid out in a standard format.

The organization turned to the use of natural language processing and "transformers" to do an initial assessment of privacy policies. Transformers, pre-trained models, parse sentences and paragraphs to highlight what would be relevant for human analysis. For example, if the evaluators want to see specific language in a privacy policy to grant the product a favorable score in a particular category of the scoring rubric, possibly machine learning could help them make that first pass. Then the humans could focus on the "hard parts," not the straightforward contents.

However, the results haven't always met the bar. As Kelly asserted, a lot of the machine learning technology currently available relies on keyword-based pattern matching, which isn't entirely accurate and generates "too many false negatives."

For example, the privacy policy might say that the company doesn't sell data when it actually does:

"We do not sell your data, except if you give us your consent by creating an account with the service."

The transformer might pick up the relevant part ("we do not sell your data") and give a passing score, thereby ignoring the caveat that a human evaluator would immediately recognize ("except if you give consent...").

Or it's possible that the policy doesn't say whether data is sold, even though the company does sell it:

"We only share your data with third parties for legitimate interest purposes."

In that case, suggested Kelly, what's a "legitimate interest"? For companies, that's "to make money." While the human evaluator would pick up on that, it's "something where automated keyword-based systems won't catch it."

Hope for the Future

Kelly said the systems that Common Sense has tried out have had some success, and the process of "growing the training data" continues. There are a few cases where the machine learning is good enough to answer some questions about the privacy policies under evaluation. One example is, does the privacy policy reference an effective date? Because that's the most structured (the policy usually references "version" or "effective date"), it's easy to detect.

Because of the promise offered by AI, the organization intends to continue its work with machine learning. That will probably involve developing a "hybrid approach" that could reduce the amount of time humans spend on the evaluation by automating some aspects of the privacy policy evaluation and bring it down to "half time."

"But there still needs to be the human touch," Kelly emphasized. "There's got to be some human intervention," to continue training the AI model."

Questions to Ask

Kelly offered a list of questions for school leaders to ask companies that tout their programs' AI capabilities:

  • What does the software really do, and where's the evidence that can support that claim?

  • How general or specific does the AI get? For example, can it "measure general student outcomes or just a slice of them"?

  • If AI can do the work better than humans, which humans are we talking about, and how much better? What's the cost-benefit equation?

  • What are the privacy or ethical risks to students involved in the use of AI-enabled software?

  • What populations don't exist in the training data, which could make for less effectiveness or downright bias?

  • Is there a demo that lets the school try the software using its own data?

  • Can the software be trained by the users? Can it work with other data training sets or does the company have control over those aspects?

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