Tag: accountability

Digital Analogues (part 5): Lessons from Animal Law, Continued

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The last post in this series on “Digital Analogues”–which explores the various areas of law that courts could use as a model for liability when AI systems cause harm–examined animal liability law.  Under traditional animal liability law, the owner of a “wild” animal is strictly liable for any injury or damage caused by that animal.  For domesticated animals, however, an owner is only liable if that particular animal had shown dangerous tendencies and the owner failed to take adequate precautions.

So what lessons might animal liability law offer for AI? Well, if we believe that AI systems are inherently risky (or if we just want to be extra cautious), we could treat all AI systems like “wild” animals and hold their owners strictly liable for harms that they cause. That would certainly encourage safety precautions, but it might also stifle innovation.  Such a blanket rule would seem particularly unfair for AI systems whose functions are so narrow that they do not present much risk to anyone. It would seem somewhat silly to impose a blanket rule that treats AlphaGo as if it is just as dangerous as an autonomous weapon system.

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Digital Analogues (Part 3): If AI systems can be “persons,” what rights should they have?

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Source: “Dilbert” by Scott Adams, January 27, 2012

The last segment in this series noted that corporations came into existence and were granted certain rights because society believed it would be economically and socially beneficial to do so.  There has, of course, been much push-back on that front.  Many people both inside and outside of the legal world ask if we have given corporations too many rights and treat them a little too much like people.  So what rights and responsibilities should we grant to AI systems if we decide to treat them as legal “persons” in some sense?

Uniquely in this series, this post will provide more questions than answers.  This is in part because the concept of “corporate personhood” has proven to be so malleable over the years.  Even though corporations are the oldest example of artificial “persons” in the legal world, we still have not decided with any firmness what rights and responsibilities a corporation should have.  Really, I can think of only one ground rule for legal “personhood”: “personhood” in a legal sense requires, at a minimum, the right to sue and the ability to be sued.  Beyond that, the meaning of “personhood” has proven to be pretty flexible.  That means that for the most part, we should be able decide the rights and responsibilities included within the concept of AI personhood on a right-by-right and responsibility-by-responsibility basis.

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Tay the Racist Chatbot: Who is responsible when a machine learns to be evil?

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Warning

By far the most entertaining AI news of the past week was the rise and rapid fall of Microsoft’s teen-girl-imitation Twitter chatbot, Tay, whose Twitter tagline described her as “Microsoft’s AI fam* from the internet that’s got zero chill.”

(* Btw, I’m officially old–I had to consult Urban Dictionary to confirm that I was correctly understanding what “fam” and “zero chill” meant. “Fam” means “someone you consider family” and “no chill” means “being particularly reckless,” in case you were wondering.)

The remainder of the tagline declared: “The more you talk the smarter Tay gets.”

Or not.  Within 24 hours of going online, Tay started saying some weird stuff.  And then some offensive stuff.  And then some really offensive stuff.  Like calling Zoe Quinn a “stupid whore.”  And saying that the Holocaust was “made up.”  And saying that black people (she used a far more offensive term) should be put in concentration camps.  And that she supports a Mexican genocide.  The list goes on.

So what happened?  How could a chatbot go full Goebbels within a day of being switched on?  Basically, Tay was designed to develop its conversational skills by using machine learning, most notably by analyzing and incorporating the language of tweets sent to her by human social media users. What Microsoft apparently did not anticipate is that Twitter trolls would intentionally try to get Tay to say offensive or otherwise inappropriate things.  At first, Tay simply repeated the inappropriate things that the trolls said to her.  But before too long, Tay had “learned” to say inappropriate things without a human goading her to do so.  This was all but inevitable given that, as Tay’s tagline suggests, Microsoft designed her to have no chill.

Now, anyone who is familiar with the social media cyberworld should not be surprised that this happened–of course a chatbot designed with “zero chill” would learn to be racist and inappropriate because the Twitterverse is filled with people who say racist and inappropriate things.  But fascinatingly, the media has overwhelmingly focused on the people who interacted with Tay rather than on the people who designed Tay when examining why the Degradation of Tay happened.

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Who’s to Blame (Part 5): A Deeper Look at Predicting the Actions of Autonomous Weapons

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Dilbert

Source: Dilbert Comic Strip on 2011-03-06 | Dilbert by Scott Adams

An autonomous weapon system (AWS) is designed and manufactured in a collaborative project between American and Indian defense contractors. It is sold to numerous countries around the world. This model of AWS is successfully deployed in conflicts in Latin America, the Caucuses, and Polynesia without violating the laws of war. An American Lt. General then orders that 50 of these units be deployed during a conflict in the Persian Gulf for use in ongoing urban combat in several cities. One of those units had previously seen action in urban combat in the Caucuses and desert combat during the same Persian Gulf conflict, all without incident. A Major makes the decision to deploy that AWS unit to assist a platoon engaged in block-to-block urban combat in Sana’a. Once the AWS unit is on the ground, a Lieutenant is responsible for telling the AWS where to go. The Lt. General, the Major, and the Lieutenant all had previous experience using this model of AWS and had given similar orders to these in prior combat situations without incident.

The Lieutenant has lost several men to enemy snipers over the past several weeks. He orders the AWS to accompany one of the squads under his command and preemptively strike any enemy sniper nests it detects–again, an order he had given to other AWS units before without incident. This time, the AWS unit misidentifies a nearby civilian house as containing a sniper nest, based on the fact that houses with similar features had frequently been used as sniper nests in the Caucuses conflict. It launches a RPG at the house. There are no snipers inside, but there are 10 civilians–all of whom are killed by the RPG. Human soldiers who had been fighting in the area would have known that that particular house likely did not contain a sniper’s nest because the glare from the sun off a nearby glass building reduces visibility on that side of the street at the times of day that American soldiers typically patrol the area–a fact that the human soldiers knew well from prior combat in the area, but a variable that the AWS had not been programmed to take into consideration.

In my most recent post for FLI on autonomous weapons, I noted that it would be difficult for humans to predict the actions of autonomous weapon systems (AWSs) programmed with machine learning capabilities.  If the military commanders responsible for deploying AWSs were unable to reliably foresee how the AWS would operate on the battlefield, it would be difficult to hold those commanders responsible if the AWS violates the law of armed conflict (LOAC).  And in the absence of command responsibility, it is not clear whether any human could be held responsible under the existing LOAC framework.

A side comment from a lawyer on Reddit made me realize that my reference to “foreseeability” requires a bit more explanation.  “Foreseeability” is one of those terms that makes lawyers’ ears perk up when they hear it because it’s a concept that every American law student encounters when learning the principles of negligence in their first-year class on Tort Law.

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Who’s to Blame (Part 4): Who’s to Blame if an Autonomous Weapon Breaks the Law?

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accountability-joke

The previous entry in this series examined why it would be very difficult to ensure that autonomous weapon systems (AWSs) consistently comply with the laws of war.  So what would happen if an attack by an AWS resulted in the needless death of civilians or otherwise constituted a violation of the laws of war?  Who would be held legally responsible?

In that regard, AWSs’ ability to operate free of human direction, monitoring, and control would raise legal concerns not shared by drones and other earlier generations of military technology.  It is not clear who, if anyone, could be held accountable if and when AWS attacks result in illegal harm to civilians and their property.  This “accountability gap” was the focus of a 2015 Human Rights Watch report.  The HRW report ultimately concluded that there was no plausible way to resolve the accountability issue and therefore called for a complete ban on fully autonomous weapons.

Although some commentators have taken issue with this prescription, the diagnosis seems to be correct—it simply is not clear who could be held responsible if an AWS commits an illegal act.  This accountability gap exists because AWSs incorporate AI technology could collect information and determine courses of action based on the conditions in which they operate.  It is unlikely that even the most careful human programmers could predict the nearly infinite on-the-ground circumstances that an AWS could face.  It would therefore be difficult for an AWS designer–to say nothing of its military operators–to foresee how the AWS would react in the fluid, fast-changing world of combat operations.  The inability to foresee an AWS’s actions would complicate the assignment of legal responsibility.

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