Uses and abuses of machine learning

Most people have heard of the chess-playing computer Deep Blue—the first to win a game against a world champion—or Watson, the computer that defeated two human opponents on the television trivia game show Jeopardy. Based on these stunning accomplishments, some have speculated that computer intelligence will replace humans in many information technology occupations, just as machines replaced humans in the fields, and robots replaced humans on the assembly line.

The truth is that even as machines reach such impressive milestones, they are still relatively limited in their ability to thoroughly understand a problem. They are pure intellectual horsepower without direction. A computer may be more capable than a human of finding subtle patterns in large databases, but it still needs a human to motivate the analysis and turn the result into meaningful action.

Machines are not good at asking questions, or even knowing what questions to ask. They are much better at answering them, provided the question is stated in a way the computer can comprehend. Present-day machine learning algorithms partner with people much like a bloodhound partners with its trainer; the dog's sense of smell may be many times stronger than its master's, but without being carefully directed, the hound may end up chasing its tail.

To better understand the real-world applications of machine learning, we'll now consider some cases where it has been used successfully, some places where it still has room for improvement, and some situations where it may do more harm than good.

Machine learning successes

Machine learning is most successful when it augments rather than replaces the specialized knowledge of a subject-matter expert. It works with medical doctors at the forefront of the fight to eradicate cancer, assists engineers and programmers with our efforts to create smarter homes and automobiles, and helps social scientists build knowledge of how societies function. Toward these ends, it is employed in countless businesses, scientific laboratories, hospitals, and governmental organizations. Any organization that generates or aggregates data likely employs at least one machine learning algorithm to help make sense of it.

Though it is impossible to list every use case of machine learning, a survey of recent success stories includes several prominent applications:

• Identification of unwanted spam messages in e-mail
• Segmentation of customer behavior for targeted advertising
• Forecasts of weather behavior and long-term climate changes
• Reduction of fraudulent credit card transactions
• Actuarial estimates of financial damage of storms and natural disasters
• Prediction of popular election outcomes
• Development of algorithms for auto-piloting drones and self-driving cars
• Optimization of energy use in homes and office buildings
• Projection of areas where criminal activity is most likely
• Discovery of genetic sequences linked to diseases

By the end of this book, you will understand the basic machine learning algorithms that are employed to teach computers to perform these tasks. For now, it suffices to say that no matter what the context is, the machine learning process is the same. Regardless of the task, an algorithm takes data and identifies patterns that form the basis for further action.

The limits of machine learning

Although machine learning is used widely and has tremendous potential, it is important to understand its limits. Machine learning, at this time, is not in any way a substitute for a human brain. It has very little flexibility to extrapolate outside of the strict parameters it learned and knows no common sense. With this in mind, one should be extremely careful to recognize exactly what the algorithm has learned before setting it loose in the real-world settings.

Without a lifetime of past experiences to build upon, computers are also limited in their ability to make simple common sense inferences about logical next steps. Take, for instance, the banner advertisements seen on many web sites. These may be served, based on the patterns learned by data mining the browsing history of millions of users. According to this data, someone who views the websites selling shoes should see advertisements for shoes, and those viewing websites for mattresses should see advertisements for mattresses. The problem is that this becomes a never-ending cycle in which additional shoe or mattress advertisements are served rather than advertisements for shoelaces and shoe polish, or bed sheets and blankets.

Many are familiar with the deficiencies of machine learning's ability to understand or translate language or to recognize speech and handwriting. Perhaps the earliest example of this type of failure is in a 1994 episode of the television show, The Simpsons, which showed a parody of the Apple Newton tablet. For its time, the Newton was known for its state-of-the-art handwriting recognition. Unfortunately for Apple, it would occasionally fail to great effect. The television episode illustrated this through a sequence in which a bully's note to Beat up Martin was misinterpreted by the Newton as Eat up Martha, as depicted in the following screenshots:

Screenshots from "Lisa on Ice" The Simpsons, 20th Century Fox (1994)

Machines' ability to understand language has improved enough since 1994, such that Google, Apple, and Microsoft are all confident enough to offer virtual concierge services operated via voice recognition. Still, even these services routinely struggle to answer relatively simple questions. Even more, online translation services sometimes misinterpret sentences that a toddler would readily understand. The predictive text feature on many devices has also led to a number of humorous autocorrect fail sites that illustrate the computer's ability to understand basic language but completely misunderstand context.

Some of these mistakes are to be expected, for sure. Language is complicated with multiple layers of text and subtext and even human beings, sometimes, understand the context incorrectly. This said, these types of failures in machines illustrate the important fact that machine learning is only as good as the data it learns from. If the context is not directly implicit in the input data, then just like a human, the computer will have to make its best guess.

Machine learning ethics

At its core, machine learning is simply a tool that assists us in making sense of the world's complex data. Like any tool, it can be used for good or evil. Machine learning may lead to problems when it is applied so broadly or callously that humans are treated as lab rats, automata, or mindless consumers. A process that may seem harmless may lead to unintended consequences when automated by an emotionless computer. For this reason, those using machine learning or data mining would be remiss not to consider the ethical implications of the art.

Due to the relative youth of machine learning as a discipline and the speed at which it is progressing, the associated legal issues and social norms are often quite uncertain and constantly in flux. Caution should be exercised while obtaining or analyzing data in order to avoid breaking laws, violating terms of service or data use agreements, and abusing the trust or violating the privacy of customers or the public.

Tip

The informal corporate motto of Google, an organization that collects perhaps more data on individuals than any other, is "don't be evil." While this seems clear enough, it may not be sufficient. A better approach may be to follow the Hippocratic Oath, a medical principle that states "above all, do no harm."

Retailers routinely use machine learning for advertising, targeted promotions, inventory management, or the layout of the items in the store. Many have even equipped checkout lanes with devices that print coupons for promotions based on the customer's buying history. In exchange for a bit of personal data, the customer receives discounts on the specific products he or she wants to buy. At first, this appears relatively harmless. But consider what happens when this practice is taken a little bit further.

One possibly apocryphal tale concerns a large retailer in the U.S. that employed machine learning to identify expectant mothers for coupon mailings. The retailer hoped that if these mothers-to-be received substantial discounts, they would become loyal customers, who would later purchase profitable items like diapers, baby formula, and toys.

Equipped with machine learning methods, the retailer identified items in the customer purchase history that could be used to predict with a high degree of certainty, not only whether a woman was pregnant, but also the approximate timing for when the baby was due.

After the retailer used this data for a promotional mailing, an angry man contacted the chain and demanded to know why his teenage daughter received coupons for maternity items. He was furious that the retailer seemed to be encouraging teenage pregnancy! As the story goes, when the retail chain's manager called to offer an apology, it was the father that ultimately apologized because, after confronting his daughter, he discovered that she was indeed pregnant!

Whether completely true or not, the lesson learned from the preceding tale is that common sense should be applied before blindly applying the results of a machine learning analysis. This is particularly true in cases where sensitive information such as health data is concerned. With a bit more care, the retailer could have foreseen this scenario, and used greater discretion while choosing how to reveal the pattern its machine learning analysis had discovered.

Certain jurisdictions may prevent you from using racial, ethnic, religious, or other protected class data for business reasons. Keep in mind that excluding this data from your analysis may not be enough, because machine learning algorithms might inadvertently learn this information independently. For instance, if a certain segment of people generally live in a certain region, buy a certain product, or otherwise behave in a way that uniquely identifies them as a group, some machine learning algorithms can infer the protected information from these other factors. In such cases, you may need to fully "de-identify" these people by excluding any potentially identifying data in addition to the protected information.

Apart from the legal consequences, using data inappropriately may hurt the bottom line. Customers may feel uncomfortable or become spooked if the aspects of their lives they consider private are made public. In recent years, several high-profile web applications have experienced a mass exodus of users who felt exploited when the applications' terms of service agreements changed, and their data was used for purposes beyond what the users had originally agreed upon. The fact that privacy expectations differ by context, age cohort, and locale adds complexity in deciding the appropriate use of personal data. It would be wise to consider the cultural implications of your work before you begin your project.

Tip

The fact that you can use data for a particular end does not always mean that you should.