We Humans and the Intelligent Machines (eBook)

1Always everywhere

“In short, success in creating effective AI could be the biggest event in the history of our civilization, or the worst. We just don’t know.”1

Stephen Hawking, physicist (1942–2018)

December 11, 2017. It is the day the New York City Council reclaims its right to self-determination.2 For the 8.6 million residents of the US metropolis, it is an important victory to ensure that the algorithms used there will become more transparent. As a result, New Yorkers are perhaps the world’s first citizens to have the right to know where, when, how and according to which criteria they are governed by machines. The man who leads the fight is James Vacca – a Bronx Democrat who heads the Committee on Technology during his third and final term as a member of the City Council. The law to be passed today will become part of his political legacy, and its significance could potentially extend far beyond New York and the United States.

“We are increasingly governed by technology.”3 With this sentence, Vacca begins his speech introducing the bill. By “we” the 62-year-old means the citizens of the city but also himself and his fellow City Council members. New York’s public administrators have been using algorithms for some time and in a wide variety of areas: law enforcement, the judiciary, education, fire protection, social transfers – all with very little transparency. Neither the public nor their elected representatives know which data are fed into the algorithms and how they are weighted. In such situations, it is just as difficult for citizens to object to automated decisions taken by the authorities as it is for elected representatives to exercise political control. Vacca fights against this lack of transparency, wanting every office that uses algorithms to be accountable to the City Council and to the public. He wants to shed light on the black box of the algorithmic society.

Much has changed since Vacca first began working nearly 40 years ago. At the beginning of his career, letters were written on typewriters. When they were to be replaced by computers, he thought it was a waste of money. Vacca is anything but a digital native. But he is not a digital naive either. Through his work for the Committee on Technology, he knows to what extent computer-based decisions affect the daily lives of New Yorkers: Police officers patrol on the basis of machine-generated crime forecasts, students are assigned to their secondary schools by computers, social welfare payments are checked by software, and pretrial detention is imposed on the basis of algorithmically calculated recidivism rates. In principle, Vacca has no objection to that. Yet he wants to understand how these decisions are made.

Vacca was irritated by the lack of openness in administrative procedures as early as the 1980s. At the time, he was annoyed by what he considered a shortage of personnel at the Bronx police station which he oversaw as district manager. When he turned to the relevant government agency, he was told that the crime rate in his district was too low for more policemen. The underlying formula used to calculate the rate, however, was not given to him. Therefore, he could neither understand nor question the quota, nor take action against it.

Vacca wanted more transparency. In August 2017, he presented the first version of the bill to the City Council. It would have required all public authorities to disclose the source code for their algorithms. Yet the experts put the brakes on during the Committee on Technology hearing: The subject area is still too unknown, they said. Too much transparency would endanger public safety, make the systems vulnerable to hackers and violate software manufacturers’ intellectual property.

Vacca had to make concessions. A commission of academics and experts was set up to draft rules, due by the end of 2019, on how City Council members and the public will be informed about such automated decisions. Vacca was nevertheless satisfied because the commission has a clearly defined mandate: “If machines, algorithms and data determine us, they must at least be transparent. Thanks to the transparency law, we will have a better overview and understanding of algorithmic decision-making, and we will be able to make agencies accountable.”4 The trend towards more openness and regulation seems unstoppable.

The legislative initiative has already stimulated a number of changes. The use of algorithms is now on New York’s public agenda – in the City Council, in the media, among the city’s residents. Algorithms are a political issue. A debate is taking place about what they are used for. And they are already used very broadly.

In the service of safety

It is not only 911 emergency calls but also computer messages that send New York police officers out on their next assignment.5 No crime has occurred at the scene assigned to the police by the software. According to the automated data analysis, however, the selected area is likely to be the site of car theft or burglary in the next few hours – crimes that could be prevented by increased patrols.

Algorithms are managing law enforcement activities. In the 1990s, New York City was notorious for its high crime rate and gangsterism. Within one year, 2,000 murders, 100,000 robberies and 147,000 car thefts took place. New York was viewed as one of the most dangerous cities in the world. Politicians reacted. Under the slogan “zero tolerance,” tougher penalties and higher detection rates were meant to make clear: Crime does not pay.

But what if modern technology could be used to prevent crime before it even occurs? The New York police force also considered this, although it initially sounded like science fiction. The Spielberg thriller Minority Report, based on the short story by Philip K. Dick, played the idea through in 2002: In a utopian society, serious crimes no longer happen because three mutants have clairvoyant abilities and reliably report every crime – a week before it is committed. Potential offenders are detained. Chief John Anderton, played in the movie by Tom Cruise, leads the police department and is proud of its results until one day his own name is spat out by the system. He is now considered a murderer-to-be and desperately tries to prove his innocence.

In New York City, algorithms play the same role that the three mutants do for Dick and Spielberg: They provide crime forecasts. Yet with one decisive difference: The computer does not predict who will commit a crime in the near future but where it will take place. The term for this is “predictive policing.”

And it works like this: Software evaluates the history of crime for each district of New York in recent years and compares the identified patterns with daily police reports. Crime may seem random at first glance, but in fact certain crimes such as burglary or theft adhere to patterns that can be worked out. These patterns depend on demographics, the day of the week, the time of day and other conditions. Just as earthquakes occur at the edges of tectonic plates, crime takes place around certain hot spots, such as supermarket parking lots, bars and schools. The predictive policing software marks small quadrants of 100 to 200 meters in length, where thefts, drug trafficking or violent crimes have recently taken place, which – according to the analysis – are often followed by other crimes.

Since law enforcement officers started using predictive policing, their day-to-day work has changed. In the past, they were only called when a crime had already been committed and needed to be solved. Today, the computer tells them where the next crime is most likely to occur. In the past, they often took the same route every day, but now the software determines so-called crime hotspots where they need to be present to monitor what is going on. The police can thus better plan and deploy their resources and work more preventively. “The hope is the holy grail of law enforcement – preventing crime before it happens,” says Washington law professor Andrew G. Ferguson.6 New York Mayor Bill de Blasio sees this in a more pragmatic and less poetic way: Algorithmic systems, he argues, have made police work more effective and more trustworthy. The city is now safer and more livable.7 In fact, within 20 years the number of murders in New York City has fallen by 80 percent to only about 350 per year. Thefts and robberies also fell by 85 percent. It is not possible to determine exactly how much predictive policing has contributed to this. In any case, the software enables policemen to be where they are needed most.

The specific functioning of the algorithms, however, remains hidden from the public: How do these programs work? What data do they collect? There are lawsuits pending against the New York police for violating the Freedom of Information Act. People have just as little knowledge about where the algorithms are used, the plaintiffs argue, as they do about how the calculations take place. The first court to hear the case ruled in favor of the plaintiffs. Nevertheless, the police continue to refuse to publish detailed information about their predictive policing.

The New York Fire Department also prefers...