We Humans and the Intelligent Machines
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We Humans and the Intelligent Machines

How algorithms shape our lives and how we can make good use of them

Jörg Dräger, Ralph Müller-Eiselt

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eBook - ePub

We Humans and the Intelligent Machines

How algorithms shape our lives and how we can make good use of them

Jörg Dräger, Ralph Müller-Eiselt

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About This Book

Defeat cancer before it develops. Prevent crime before it happens. Get the perfect job without having to know the right people. Algorithms turn long-wished-for dreams into reality. At the same time, they can weaken solidarity in healthcare systems, lead to discriminatory court judgements and exclude individuals from the labor market.Algorithms are already deeply determining our lives. This book uses illuminating examples to describe the opportunities and risks machine-based decision-making presents for each of us. It also offers specific suggestions for ensuring artificial intelligence serves society as it should.

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What algorithms can do for us

A world without algorithms is hardly imaginable today. They have crept almost imperceptibly into our lives. Intelligent machines are now used almost everywhere that information is available electronically. The following nine chapters show the extent to which they are deployed and the impact they have. This second part uses practical examples to show how algorithms can make life better and more just for each of us and for society as a whole. Yet people and machines do not always complement each other in a meaningful way. Their interaction can also have negative consequences for individuals and society – be it unintentionally or by malice aforethought.

An algorithm for algorithms

It is precisely this tension that interests us. We want to examine those algorithmic systems that influence whether people can participate in society. For better or for worse. With consequences that concern us all, because they bring either social progress or serious disadvantages. Not all algorithms are truly relevant to society. Neither the spell checker in word processing software nor a car rental company’s computer-driven fleet management system will shake the foundations of communal life. They do not need public discourse – which, on the other hand, is indispensable if algorithms are to have a say in asylum procedures or prison sentences.
To select the examples in the following chapters we used an “algorithm for algorithms” developed by researchers Kilian Vieth and Ben Wagner (see Chapter 14), which measures the relevance to society as a whole. Its main criteria: Are people being evaluated by the algorithmic system? How dependent are they on the result? How much political and economic power does the organization using the algorithm have? What is the system’s scope? The answers are assigned cumulative scores. The higher the overall result, the more relevant a system is to social participation. And, consequently, the more attention it deserves – not only in this book.

Creating order in the jungle of algorithms

The second part of this book dives into the world of practical applications. It looks at the impacts that algorithms can have on individuals and on society as a whole. We place more emphasis on clarity than on technical detail: Our focus is on the effect of the algorithms, not on their programming code. At the same time, we try to bring order to the jungle of different applications. The following nine chapters identify four impacts on the individual, four on society as a whole and one on our social interactions.
At the individual level (Chapters 5 to 8), algorithms can meet personal needs more effectively, provide fairer access to goods essential for social participation, expand human capabilities and create space for activities we are particularly good at or like. Possible downsides are manipulation, exclusion of weaker individuals, an algorithmic arms race and reckless efforts to achieve ever higher productivity.
At the societal level (Chapters 9 to 12), algorithms offer the potential to monitor the use of government services more accurately, distribute limited resources more efficiently, establish effective preventive measures for healthier and safer communities, and make fairer decisions. This is countered by risks such as excessive state intervention, the misappropriation of software, the weakening of social solidarity, and growing social inequality and discrimination. Last but not least, algorithms also influence personal relationships and our communication and values. They can strengthen cohesion, but also promote social polarization (Chapter 13).

5Personalization: Suitable for everyone

“No one can get outside his own individuality.”1
Arthur Schopenhauer, philosopher
(1778–1860)
Felix is unique.2 For six years, the 10-year-old from California has been continuously sending data into the cloud: pulse, stress level, exercise activity, blood sugar. Every day, tens of thousands of data points are collected by his various devices. Felix is probably the best-measured diabetes patient in the world. He is probably also one of the children whose diabetes is best managed. After all, a computer permanently evaluates all the data it receives from the boy’s smartwatch and his blood sugar monitor. An algorithm uses the information to calculate a therapy tailored to his exact needs. His parents constantly receive precise updates as to when Felix needs a snack or a dose of insulin.
For Felix, this adds directly to his quality of life. He has Type 1 diabetes, an incurable autoimmune disease in which the body’s insulin-producing cells are destroyed. This causes him to oscillate between two states: hypoglycemia, which makes him restless and unfocused, and hyperglycemia, which makes him tired, weak and listless. To ensure Felix experiences these states as rarely as possible, his blood sugar level must be kept stable, providing the body with the right dose of insulin at the right time.
Calculating that is the job of an algorithm. And it is pretty good at it: The phases in which Felix is dangerously hyperglycemic have been reduced by almost half. This means not only that Felix is exhausted only half as often as he otherwise would be, but he also has a much lower risk of falling into a diabetic coma. In the six years since his diagnosis, his parents have never had to take him to the emergency room, not once has he lost consciousness because of high blood sugar levels or been in a life-threatening situation.
Felix owes this personalized medical care not to a doctor, but to his mother. Vivienne Ming loves data. The neuroscientist, who conducted her research at the University of California, Berkeley, is driven by the idea of filtering what is unique out of the mass, recognizing the exceptional and not what is merely average. As lead scientist at the online recruiter Gild, she designed algorithms in Silicon Valley that searched for unrecognized skills in the resumes and digital footprints of 70 million people. With her computer programs, Ming has found jobs for those who are otherwise overlooked by companies because they do not meet the usual criteria.
Standard therapy was also out of the question for her son. The doctors had suggested exactly that when the diagnosis of diabetes was made. Felix’ parents were asked to measure his blood sugar three times a day for one week and fill in a paper form. This would determine Felix’s average insulin dose. Ming would not accept that; it seemed like a betrayal of what science could do.
She began to monitor her son. She read the specialist literature, kept meticulous records of when Felix played and when he was lethargic, and noted his meals and their nutritional value on a daily basis. Within four weeks she programmed an algorithm that could derive therapy-relevant patterns and forecasts from her observations and from the collected data on her son’s pulse, blood sugar and physical activity.
One result particularly amazed the parents. After breakfast, Felix’s pulse and blood sugar levels rose – although not always to the same extent. It regularly went up Mondays to Fridays, with an upward outlier every Tuesday. On weekends his numbers remained lower, without Felix having had anything different to eat at breakfast. The explanation: The boy was feeling stressed. He had just started attending a new preschool and was afraid of what awaited him every morning, especially the math lessons on Tuesdays.
Vivienne Ming took her findings to the doctors. Her son’s insulin requirements, as the analysis clearly showed, fluctuated greatly. They were so dependent on factors such as the day’s lesson plan that it was impossible to give him the same amount of insulin day in and day out, calculated from the average of 21 random measurements.
The doctors, however, insisted on using the conventional method. They ignored the tables with hundreds of thousands of data points. Instead, they wanted to treat the child, who had been assessed in minute detail, just based on their own crude average data. Ming decided to ignore the physicians. She rejected the recommended standard procedure in favor of her own expertise, saying: “We have enough data and algorithms that we don’t have to deal with the average.”3
Felix got an insulin pump that is connected to the Internet and that his mother can use remotely to inject the right dose before his blood sugar spikes or crashes. Tuesday mornings before the math lesson the dose is a bit higher, on weekends a bit lower. Today, Felix is a bright, cheerful child who does not have to do without anything except excess amounts of sweets. However, even if he gets ahold of a bar of chocolate, overriding the programmed amount of insulin, the algorithm quickly notices and alerts his parents, who can make an adjustment if necessary.
After Vivienne Ming was able to demonstrate how well her son is doing with his personalized therapy and the quality of life he gained from the algorithm she developed, she began to share her findings with academics and pharmaceutical companies. In fact, pharma giant Eli Lilly has now announced a fully automated pump that records and evaluates health data so it can inject the correct amount of insulin. Ming is pleased to have started the ball rolling, but at the same time she is disappointed at the pace at which medical science has embraced algorithmic innovations. “It is amazing how slow progress is,” she says. “It took only one month to personalize Felix’s diabetes therapy. But it has taken ten years to make that kind of treatment available more broadly. I don’t want to substitute doctors. I just want to make them smarter.”4

Math does not have to horrify

Homogeneity is an illusion – in the treatment of diabetes and in learning. That is something Elke Stuthmann knows well.5 She also knows that the subject she teaches has divided generations of pupils. Some love mathematics, others – like Felix – hate it. In the end, both groups usually end up unhappy: During the classic math lesson, the whizzes are bored, while the less gifted fight desperately to make sense of it all or have long since switched off. “Learning in lockstep, in my opinion that’s not how it should be in the classroom,” Stuthmann says. It was an insight she gained as a young teacher.6 That is why the 58-year-old has never simply stood at the blackboard, explained the subject matter and hoped that everyone would somehow understand it. Yet her ideal solution, creating an individual curriculum for each student, has proven overwhelming.
She is now closer to achieving her goal, thanks to the help algorithms can provide. When Stuthmann leads her class into the computer room at Hamburg’s Friedrich Ebert Secondary School, the 27 students are confronted with different tasks. The math aces warm up with two introductory questions and then jump directly to much trickier problems. Weaker students can stick with the basics, gaining more time to review and understand. Everyone learns at their own pace. Stuthmann follows the progress of each student on her screen. If she sees that someone is struggling with a task and is not getting anywhere, she goes and helps. The rest of the class is not held up by this intervention; everyone stays busy because the computer constantly provides them with new assignments appropriate to their level.
Bettermarks, a computer program made by a Berlin start-up of the same name, is what makes this personalized teaching possible.7 It works like an interactive math book. The software explains topics, shows sample calculations and assigns problem sets – the traditional approach, as in printed textbooks. What is new is that each student is guided through the problem set along a personal learning path. An algorithm selects the exercise that fits best from a database of more than 100,000. If the student solves the problem easily and quickly, the level of difficulty goes up. If they get stuck or select a wrong answer, the system analyses the knowledge that is lacking. “Students receive feedback at each step along the way and, if they make mistakes, they are offered a series of exercises that deepens the knowledge required,” says Arndt Kwiatkowski, founder and managing director of Bettermarks.8 The student no longer has to adapt to the textbook, since the program adapts to the student.
Elke Stuthmann and her students do not go to the computer room for every math lesson. She only uses Bettermarks in class every other week, but she assigns it regularly for homework. She also selects the exercises herself with a mouse click, not leaving it entirely to the algorithm. But even these first steps towards personalizing teaching are having an effect. According to Stuthmann, the high-flyers are two to three times faster than their classmates – and grateful not to have to wait. Instead, they can work on more demanding tasks or even jump to the next lesson. Yet the weaker students also benefit. “They feel less alone, especially when doing their homework,” she says. “The software gives them tips and as much help as they need.”9
If many students are unable to complete a certain exercise, the teacher can respond by repeating the material in class. Stuthmann firmly believes that the computer program increases the students’ motivation to learn. It even lessens the anxiety of doing math because students succeed more often and feel like their achievement is recognized. This is what Bettermarks hopes to achieve. It regards difference as normal, not as a problem. The company’s software is designed to avoid both under- and over-challenging the students and thus boring or stressing them. Bettermarks is now available in every classroom in Uruguay; in Germany in can be found in just a few hundred schools.
Educational systems like the one in Germany, however, would be well advised to abandon the illusion of homogeneity. People are different and they learn differently, too. Even if everyone had to achieve the same goal, their path, style and pace to get there would be very different. Schools too rarely take this into account and often still focus on the average learner. “You’re 12, it’s autumn, so it’s time to do fractions,” says jour...

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