Joffrey Becker, Collège de France

Robots raise all kinds of concerns. They could steal our jobs, as some experts think. And if artificial intelligence grows, they might even be tempted to enslave us, or to annihilate the whole of humanity.

Robots are strange creatures, and not only for these frequently invoked reasons. We have good cause to be a little worried about these machines.

Imagine that you are visiting the Quai Branly-Jacques Chirac, a museum in Paris dedicated to anthropology and ethnology. As you walk through the collection, your curiosity leads you to a certain piece. After a while, you begin to sense a familiar presence heading towards the same objet d’art that has caught your attention.

You move slowly, and as you turn your head a strange feeling seizes you because what you seem to distinguish, still blurry in your peripheral vision, is a not-quite-human figure. Anxiety takes over.

As your head turns, and your vision become sharper, this feeling gets stronger. You realise that this is a humanoid machine, a robot called Berenson. Named after the American art critic Bernard Berenson and designed by the roboticist Philippe Gaussier (Image and Signal processing Lab) and the anthropologist Denis Vidal (Institut de recherche sur le développement), Berenson is part of an experiment underway at the Quai Branly museum since 2012.

The strangeness of the encounter with Berenson leaves you suddenly frightened, and you step back, away from the machine.

The uncanny valley

This feeling has been explored in robotics since the 1970s, when Japanese researcher Professor Masahiro Mori proposed his “uncanny valley” theory. If a robot resembles us, he suggested, we are inclined to consider its presence in the same way as we would that of a human being.

But when the machine reveals its robot nature to us, we will feel discomfort. Enter what Mori dubbed “the uncanny valley”. The robot will then be regarded as something of a zombie.

Mori’s theory cannot be systematically verified. But the feelings we experience when we meet an autonomous machine are certainly tinged with both incomprehension and curiosity.

The experiment conducted with Berenson at the Quai Branly, for example, shows that the robot’s presence can elicit paradoxical behaviour in museum goers. It underlines the deep ambiguity that characterises the relationship one can have with a robot, particularly the many communication problems they pose for humans.

If we are wary of such machines, it is mainly because it is not clear to us whether they have intentions. And, if so, what they are and how to establish a basis for the minimal understanding that is essential in any interaction. Thus, it is common to see visitors of the Quai Branly adopting social behaviour with Berenson, such as talking to it, or facing it, to find out how it perceives its environment.

In one way or another, visitors mainly try to establish contact. It appears that there is something strategic in considering the robot, even temporarily, as a person. And these social behaviours are not only observed when humans interact with machines that resembles us: it seems we make anthropomorphic projections whenever humans and robots meet.

Social interactions

An interdisciplinary team has recently been set up to explore the many dimensions revealed during these interactions. In particular, they are looking at the moments when, in our minds, we are ready to endow robots with intentions and intelligence.

This is how the PsyPhINe project was born. Based on interactions between humans and a robotic lamp, this project seeks to better understand people’s tendency to anthropomorphise machines.

After they get accustomed to the strangeness of the situation, it is not uncommon to observe that people are socially engaging with the lamp. During a game in which people are invited to play with this robot, they can be seen reacting to its movements and sometimes speaking to it, commenting on what it is doing or on the situation itself.

Mistrust often characterises the first moments of our relations with machines. Beyond their appearance, most people don’t know exactly what robots are made of, what their functions are and what their intentions might be. The robot world seems way too far from ours.

But this feeling quickly disappears. Assuming they have not already run away from the machine, people usually seek to define and maintain a frame for communication. Typically, they rely on existing communication habits, such as those used when talking to pets, for example, or with any living being whose world is to some degree different from theirs.

Ultimately, it seems, we humans are as suspicious of our technologies as we are fascinated by the possibilities they open up.

Joffrey Becker, Anthropologue, Laboratoire d’anthropologie sociale, Collège de France

This article was originally published on The Conversation. Read the original article.

Christopher Markou, University of Cambridge

This is where we are at in 2017: sophisticated algorithms are both predicting and helping to solve crimes committed by humans; predicting the outcome of court cases and human rights trials; and helping to do the work done by lawyers in those cases. By 2040, there is even a suggestion that sophisticated robots will be committing a good chunk of all the crime in the world. Just ask the toddler who was run over by a security robot at a California mall last year.

How do we make sense of all this? Should we be terrified? Generally unproductive. Should we shrug our shoulders as a society and get back to Netflix? Tempting, but no. Should we start making plans for how we deal with all of this? Absolutely.

Fear of Artificial Intelligence (AI) is a big theme. Technology can be a downright scary thing; particularly when its new, powerful, and comes with lots of question marks. But films like Terminator and shows like Westworld are more than just entertainment, they are a glimpse into the world we might inherit, or at least into how we are conceiving potential futures for ourselves.

Among the many things that must now be considered is what role and function the law will play. Expert opinions differ wildly on the likelihood and imminence of a future where sufficiently advanced robots walk among us, but we must confront the fact that autonomous technology with the capacity to cause harm is already around. Whether it’s a military drone with a full payload, a law enforcement robot exploding to kill a dangerous suspect or something altogether more innocent that causes harm through accident, error, oversight, or good ol’ fashioned stupidity.

There’s a cynical saying in law that “wheres there’s blame, there’s a claim”. But who do we blame when a robot does wrong? This proposition can easily be dismissed as something too abstract to worry about. But let’s not forget that a robot was arrested (and released without charge) for buying drugs; and Tesla Motors was absolved of responsibility by the American National Highway Traffic Safety Administration when a driver was killed in a crash after his Tesla was in autopilot.

While problems like this are certainly peculiar, history has a lot to teach us. For instance, little thought was given to who owned the sky before the Wright Brothers took the Kitty Hawk for a joyride. Time and time again, the law is presented with these novel challenges. And despite initial overreaction, it got there in the end. Simply put: law evolves.

Robot guilt

The role of the law can be defined in many ways, but ultimately it is a system within society for stabilising people’s expectations. If you get mugged, you expect the mugger to be charged with a crime and punished accordingly.

But the law also has expectations of us; we must comply with it to the fullest extent our consciences allow. As humans we can generally do that. We have the capacity to decide whether to speed or obey the speed limit – and so humans are considered by the law to be “legal persons”.

To varying extents, companies are endowed with legal personhood, too. It grants them certain economic and legal rights, but more importantly it also confers responsibilities on them. So, if Company X builds an autonomous machine, then that company has a corresponding legal duty.

The problem arises when the machines themselves can make decisions of their own accord. As impressive as intelligent assistants, Alexa, Siri or Cortana are, they fall far short of the threshold for legal personhood. But what happens when their more advanced descendants begin causing real harm?

A guilty AI mind?

The criminal law has two critical concepts. First, it contains the idea that liability for harm arises whenever harm has been or is likely to be caused by a certain act or omission.

Second, criminal law requires that an accused is culpable for their actions. This is known as a “guilty mind” or “mens rea”. The idea behind mens rea is to ensure that the accused both completed the action of assaulting someone and had the intention of harming them, or knew harm was a likely consequence of their action.

So if an advanced autonomous machine commits a crime of its own accord, how should it be treated by the law? How would a lawyer go about demonstrating the “guilty mind” of a non-human? Can this be done be referring to and adapting existing legal principles?

Take driverless cars. Cars drive on roads and there are regulatory frameworks in place to assure that there is a human behind the wheel (at least to some extent). However, once fully autonomous cars arrive there will need to be extensive adjustments to laws and regulations that account for the new types of interactions that will happen between human and machine on the road.

As AI technology evolves, it will eventually reach a state of sophistication that will allow it to bypass human control. As the bypassing of human control becomes more widespread, then the questions about harm, risk, fault and punishment will become more important. Film, television and literature may dwell on the most extreme examples of “robots gone awry” but the legal realities should not be left to Hollywood.

So can robots commit crime? In short: yes. If a robot kills someone, then it has committed a crime (actus reus), but technically only half a crime, as it would be far harder to determine mens rea. How do we know the robot intended to do what it did?

For now, we are nowhere near the level of building a fully sentient or “conscious” humanoid robot that looks, acts, talks, and thinks like us humans. But even a few short hops in AI research could produce an autonomous machine that could unleash all manner of legal mischief. Financial and discriminatory algorithmic mischief already abounds.

Play along with me; just imagine that a Terminator-calibre AI exists, and that it commits a crime (let’s say murder) then the task is not determining whether it in fact murdered someone; but the extent to which that act satisfies the principle of mens rea.

But what would we need to prove the existence of mens rea? Could we simply cross-examine the AI like we do a human defendant? Maybe, but we would need to go a bit deeper than that and examine the code that made the machine “tick”.

And what would “intent” look like in a machine mind? How would we go about proving an autonomous machine was justified in killing a human in self-defense or the extent of premeditation?

Let’s go even further. After all, we’re not only talking about violent crimes. Imagine a system that could randomly purchase things on the internet using your credit card – and it decided to buy contraband. This isn’t fiction; it has happened. Two London-based artists created a bot that purchased random items off the dark web. And what did it buy? Fake jeans, a baseball cap with a spy camera, a stash can, some Nikes, 200 cigarettes, a set of fire-brigade master keys, a counterfeit Louis Vuitton bag and ten ecstasy pills. Should these artists be liable for what the bot they created bought?

Maybe. But what if the bot “decided” to make the purchases itself?

Robo-jails?

Even if you solve these legal issues, you are still left with the question of punishment. What’s a 30-year jail stretch to an autonomous machine that does not age, grow infirm or miss its loved ones? Unless, of course, it was programmed to “reflect” on its wrongdoing and find a way to rewrite its own code while safely ensconced at Her Majesty’s leisure. And what would building “remorse” into machines say about us as their builders?

What we are really talking about when we talk about whether or not robots can commit crimes is “emergence” – where a system does something novel and perhaps good but also unforeseeable, which is why it presents such a problem for law.

AI has already helped with emergent concepts in medicine, and we are learning things about the universe with AI systems that even an army of Stephen Hawkings might not reveal.

The hope for AI is that in trying to capture this safe and beneficial emergent behaviour, we can find a parallel solution for ensuring it does not manifest itself in illegal, unethical, or downright dangerous ways.

At present, however, we are systematically incapable of guaranteeing human rights on a global scale, so I can’t help but wonder how ready we are for the prospect of robot crime given that we already struggle mightily to contain that done by humans.

Christopher Markou, PhD Candidate, Faculty of Law, University of Cambridge

This article was originally published on The Conversation. Read the original article.

Michael Milford, Queensland University of Technology

Tesla chief executive and OpenAI founder Elon Musk suggested last week that humanity might stave off irrelevance from the rise of the machines by merging with the machines and becoming cyborgs.

However, current trends in software-only artificial intelligence and deep learning technology raise serious doubts about the plausibility of this claim, especially in the long term. This doubt is not only due to hardware limitations; it is also to do with the role the human brain would play in the match-up.

Musk’s thesis is straightforward: that sufficiently advanced interfaces between brain and computer will enable humans to massively augment their capabilities by being better able to leverage technologies such as machine learning and deep learning.

But the exchange goes both ways. Brain-machine interfaces may help the performance of machine learning algorithms by having humans “fill in the gaps” for tasks that the algorithms are currently bad at, like making nuanced contextual decisions.

The idea in itself is not new. J. C. R. Licklider and others speculated on the possibility and implications of “man-computer symbiosis” in the mid-20th century.

However, progress has been slow. One reason is development of hardware. “There is a reason they call it hardware – it is hard,” said Tony Fadell, creator of the iPod. And creating hardware that interfaces with organic systems is even harder.

Current technologies are primitive compared to the picture of brain-machine interfaces we’re sold in science fiction movies such as The Matrix.

Deep learning quirks

Assuming that the hardware challenge is eventually solved, there are bigger problems at hand. The past decade of incredible advances in deep learning research has revealed that there are some fundamental challenges to be overcome.

The first is simply that we still struggle to understand and characterise exactly how these complex neural network systems function.

We trust simple technology like a calculator because we know it will always do precisely what we want it to do. Errors are almost always a result of mistaken entry by the fallible human.

One vision of brain-machine augmentation would be to make us superhuman at arithmetic. So instead of pulling out a calculator or smartphone, we could think of the calculation and receive the answer instantaneously from the “assistive” machine.

Where things get tricky is if we were to try and plug into the more advanced functions offered by machine learning techniques such as deep learning.

Let’s say you work in a security role at an airport and have a brain-machine augmentation that automatically scans the thousands of faces you see each day and alerts you to possible security risks.

Most machine learning systems suffer from an infamous problem whereby a tiny change in the appearance of a person or object can cause the system to catastrophically misclassify what it thinks it is looking at. Change a picture of a person by less than 1%, and the machine system might suddenly think it is looking at a bicycle.

Terrorists or criminals might exploit the different vulnerabilities of a machine to bypass security checks, a problem that already exists in online security. Humans, although limited in their own way, might not be vulnerable to such exploits.

Despite their reputation as being unemotional, machine learning technologies also suffer from bias in the same way that humans do, and can even exhibit racist behaviour if fed appropriate data. This unpredictability has major implications for how a human might plug into – and more importantly, trust – a machine.

Trust me, I’m a robot

Trust is also a two-way street. Human thought is a complex, highly dynamic activity. In this same security scenario, with a sufficiently advanced brain-machine interface, how will the machine know what human biases to ignore? After all, unconscious bias is a challenge everyone faces. What if the technology is helping you interview job candidates?

We can preview to some extent the issues of trust in a brain-machine interface by looking at how defence forces around the world are trying to address human-machine trust in an increasingly mixed human-autonomous systems battlefield.

Research into trusted autonomous systems deals with both humans trusting machines and machines trusting humans.

There is a parallel between a robot warrior making an ethical decision to ignore an unlawful order by a human and what must happen in a brain-machine interface: interpretation of the human’s thoughts by the machine, while filtering fleeting thoughts and deeper unconscious biases.

In defence scenarios, the logical role for a human brain is in checking that decisions are ethical. But how will this work when the human brain is plugged into a machine that can make inferences using data at a scale that no brain can comprehend?

In the long term, the issue is whether, and how, humans will need to be involved in processes that are increasingly determined by machines. Soon machines may make medical decisions no human team can possibly fathom. What role can and should the human brain play in this process?

In some cases, the combination of automation and human workers could increase jobs, but this effect is likely fleeting. Those same robots and automation systems will continue to improve, likely eventually removing the jobs they created locally.

Likewise, while humans may initially play a “useful” role in brain-machine systems, as the technology continues to improve there may be less reason to include humans in the loop at all.

The idea of maintaining humanity’s relevance by integrating human brains with artificial brains is appealing. What remains to be seen is what contribution the human brain will make, especially as technology development outpaces human brain development by a million to one.

Michael Milford, Associate professor, Queensland University of Technology

This article was originally published on The Conversation. Read the original article.