قوانین

Laws

We humans are social animals who subdued all other species and conquered Earth thanks to our ability to cooperate. We’ve developed laws to incentivize and facilitate cooperation, so if AI can improve our legal and governance systems, then it can enable us to cooperate more successfully than ever before, bringing out the very best in us. And there’s plenty of opportunity for improvement here, both in how our laws are applied and how they’re written, so let’s explore both in turn.

What are the first associations that come to your mind when you think about the court system in your country? If it’s lengthy delays, high costs and occasional injustice, then you’re not alone. Wouldn’t it be wonderful if your first thoughts were instead “efficiency” and “fairness”? Since the legal process can be abstractly viewed as a computation, inputting information about evidence and laws and outputting a decision, some scholars dream of fully automating it with robojudges: AI systems that tirelessly apply the same high legal standards to every judgment without succumbing to human errors such as bias, fatigue or lack of the latest knowledge.

Robojudges

Byron De La Beckwith Jr. was convicted in 1994 of assassinating civil rights leader Medgar Evers in 1963, but two separate all-white Mississippi juries had failed to convict him the year after the murder, even though the physical evidence was essentially the same.34 Alas, legal history is rife with judgments biased by skin color, gender, sexual orientation, religion, nationality and other factors. Robojudges could in principle ensure that, for the first time in history, everyone becomes truly equal under the law: they could be programmed to all be identical and to treat everyone equally, transparently applying the law in a truly unbiased fashion.

Robojudges could also eliminate human biases that are accidental rather than intentional. For example, a controversial 2012 study of Israeli judges claimed that they delivered significantly harsher verdicts when they were hungry: whereas they denied about 35% of parole cases right after breakfast, they denied over 85% right before lunch.35 Another shortcoming of human judges is that they may lack sufficient time to explore all details of a case. In contrast, robojudges can easily be copied, since they consist of little more than software, allowing all pending cases to be processed in parallel rather than in series, each case getting its own robojudge for as long as it takes. Finally, although it’s impossible for human judges to master all technical knowledge required for every possible case, from thorny patent disputes to murder mysteries hinging on the latest forensic science, future robojudges may have essentially unlimited memory and learning capacity.

One day, such robojudges may therefore be both more efficient and fairer, by virtue of being unbiased, competent and transparent. Their efficiency makes them fairer still: by speeding up the legal process and making it harder for savvy lawyers to skew the outcome, they could make it dramatically cheaper to get justice through the courts. This could greatly increase the chances of a cash-strapped individual or startup company prevailing against a billionaire or multinational corporation with an army of lawyers.

On the other hand, what if robojudges have bugs or get hacked? Both have already afflicted automatic voting machines, and when years behind bars or millions in the bank are at stake, the incentives for cyberattacks are greater still. Even if AI can be made robust enough for us to trust that a robojudge is using the legislated algorithm, will everybody feel that they understand its logical reasoning enough to respect its judgment? This challenge is exacerbated by the recent success of neural networks, which often outperform traditional easy-to-understand AI algorithms at the price of inscrutability. If defendants wish to know why they were convicted, shouldn’t they have the right to a better answer than “we trained the system on lots of data, and this is what it decided”? Moreover, recent studies have shown that if you train a deep neural learning system with massive amounts of prisoner data, it can predict who’s likely to return to crime (and should therefore be denied parole) better than human judges. But what if this system finds that recidivism is statistically linked to a prisoner’s sex or race—would this count as a sexist, racist robojudge that needs reprogramming? Indeed, a 2016 study argued that recidivism-prediction software used across the United States was biased against African Americans and had contributed to unfair sentencing.36 These are important questions that we all need to ponder and discuss to ensure that AI remains beneficial. We aren’t facing an all-or-nothing decision regarding robojudges, but rather a decision about the extent and speed with which we want to deploy AI in our legal system. Do we want human judges to have AI-based decision support systems, just like tomorrow’s medical doctors? Do we want to go further and have robojudge decisions that can be appealed to human judges, or do we want to go all the way and give even the final say to machines, even for death penalties?

Legal Controversies

So far, we’ve explored only the application of law; let us now turn to its content. There’s broad consensus that our laws need to evolve to keep pace with our technology. For example, the two programmers who created the aforementioned ILOVEYOU worm and caused billions of dollars in damages were acquitted of all charges and walked free because at that time, there were no laws against malware creation in the Philippines. Since the pace of technological progress appears to be accelerating, laws need to be updated ever more rapidly, and have a tendency to lag behind. Getting more tech-savvy people into law schools and governments is probably a smart move for society. But should AI-based decision support systems for voters and legislators ensue, followed by outright robo-legislators?

How to best alter our laws to reflect AI progress is a fascinatingly controversial topic. One dispute reflects the tension between privacy versus freedom of information. Freedom fans argue that the less privacy we have, the more evidence the courts will have, and the fairer the judgments will be. For example, if the government taps into everyone’s electronic devices to record where they are and what they type, click, say and do, many crimes would be readily solved, and additional ones could be prevented. Privacy advocates counter that they don’t want an Orwellian surveillance state, and that even if they did, there’s a risk of it turning into a totalitarian dictatorship of epic proportions. Moreover, machine-learning techniques have gotten better at analyzing brain data from fMRI scanners to determine what a person is thinking about and, in particular, whether they’re telling the truth or lying.37 If AI-assisted brain scanning technology became commonplace in courtrooms, the currently tedious process of establishing the facts of a case could be dramatically simplified and expedited, enabling faster trials and fairer judgments. But privacy advocates might worry about whether such systems occasionally make mistakes and, more fundamentally, whether our minds should be off-limits to government snooping. Governments that don’t support freedom of thought could use such technology to criminalize the holding of certain beliefs and opinions. Where would you draw the line between justice and privacy, and between protecting society and protecting personal freedom? Wherever you draw it, will it gradually but inexorably move toward reduced privacy to compensate for the fact that evidence gets easier to fake? For example, once AI becomes able to generate fully realistic fake videos of you committing crimes, will you vote for a system where the government tracks everyone’s whereabouts at all times and can provide you with an ironclad alibi if needed?

Another captivating controversy is whether AI research should be regulated or, more generally, what incentives policymakers should give AI researchers to maximize the chances of a beneficial outcome. Some AI researchers have argued against all forms of regulation of AI development, claiming that they would needlessly delay urgently needed innovation (for example, lifesaving self-driving cars) and would drive cutting-edge AI research underground and/or to other countries with more permissive governments. At the Puerto Rico beneficial-AI conference mentioned in the first chapter, Elon Musk argued that what we need right now from governments isn’t oversight but insight: specifically, technically capable people in government positions who can monitor AI’s progress and steer it if warranted down the road. He also argued that government regulation can sometimes nurture rather than stifle progress: for example, if government safety standards for self-driving cars can help reduce the number of self-driving-car accidents, then a public backlash is less likely and adoption of the new technology can be accelerated. The most safety-conscious AI companies might therefore favor regulation that forces less scrupulous competitors to match their high safety standards.

Yet another interesting legal controversy involves granting rights to machines. If self-driving cars cut the 32,000 annual U.S. traffic fatalities in half, perhaps carmakers won’t get 16,000 thank-you notes, but 16,000 lawsuits. So if a self-driving car causes an accident, who should be liable—its occupants, its owner or its manufacturer? Legal scholar David Vladeck has proposed a fourth answer: the car itself! Specifically, he proposes that self-driving cars be allowed (and required) to hold car insurance. This way, models with a sterling safety record will qualify for premiums that are very low, probably lower than what’s available to human drivers, while poorly designed models from sloppy manufacturers will only qualify for insurance policies that make them prohibitively expensive to own.

But if machines such as cars are allowed to hold insurance policies, should they also be able to own money and property? If so, there’s nothing legally stopping smart computers from making money on the stock market and using it to buy online services. Once a computer starts paying humans to work for it, it can accomplish anything that humans can do. If AI systems eventually get better than humans at investing (which they already are in some domains), this could lead to a situation where most of our economy is owned and controlled by machines. Is this what we want? If it sounds far-off, consider that most of our economy is already owned by another form of non-human entity: corporations, which are often more powerful than any one person in them and can to some extent take on life of their own.

If you’re OK with granting machines the rights to own property, then how about granting them the right to vote? If so, should each computer program get one vote, even though it can trivially make trillions of copies of itself in the cloud if it’s rich enough, thereby guaranteeing that it will decide all elections? If not, then on what moral basis are we discriminating against machine minds relative to human minds? Does it make a difference if machine minds are conscious in the sense of having a subjective experience like we do? We’ll explore in greater depth these controversial questions related to computer control of our world in the next chapter, and questions related to machine consciousness in chapter 8.