U.S. Military Announces Development of Drones That Decide to Kill Using AI

Drone warfare (with its state terrorism causing numerous civilian casualties) is already horrifying enough — this AI drone development would likely be even worse. This announcement also raises the question of how much accountability those who write the algorithms that determine how the drone functions will face.

The US Army recently announced that it is developing the first drones that can spot and target vehicles and people using artificial intelligence (AI).

Whereas current military drones are still controlled by people, this new technology will decide who to kill with almost no human involvement.

Once complete, these drones will represent the ultimate militarisation of AI and trigger vast legal and ethical implications for wider society.

There is a chance that warfare will move from fighting to extermination, losing any semblance of humanity in the process.

At the same time, it could widen the sphere of warfare so that the companies, engineers and scientists building AI become valid military targets.

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Even with these drone killings, human emotions, judgements and ethics have always remained at the centre of war.

The existence of mental trauma and post-traumatic stress disorder (PTSD) among drone operators shows the psychological impact of remote killing.

And this actually points to one possible military and ethical argument by Ronald Arkin, in support of autonomous killing drones. Perhaps if these drones drop the bombs, psychological problems among crew members can be avoided.

The weakness in this argument is that you don’t have to be responsible for killing to be traumatised by it.

Intelligence specialists and other military personnel regularly analyse graphic footage from drone strikes. Research shows that it is possible to suffer psychological harm by frequently viewing images of extreme violence.

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The prospect of totally autonomous drones would radically alter the complex processes and decisions behind military killings.

But legal and ethical responsibility does not somehow just disappear if you remove human oversight. Instead, responsibility will increasingly fall on other people, including artificial intelligence scientists.

The legal implications of these developments are already becoming evident.

Under current international humanitarian law, “dual-use” facilities – those which develop products for both civilian and military application – can be attacked in the right circumstances. For example, in the 1999 Kosovo War, the Pancevo oil refinery was attacked because it could fuel Yugoslav tanks as well as fuel civilian cars.

With an autonomous drone weapon system, certain lines of computer code would almost certainly be classed as dual-use.

Companies like Google, its employees or its systems, could become liable to attack from an enemy state.

For example, if Google’s Project Maven image recognition AI software is incorporated into an American military autonomous drone, Google could find itself implicated in the drone “killing” business, as might every other civilian contributor to such lethal autonomous systems.

Ethically, there are even darker issues still.

The whole point of the self-learning algorithms – programs that independently learn from whatever data they can collect – that technology uses is that they become better at whatever task they are given.

If a lethal autonomous drone is to get better at its job through self-learning, someone will need to decide on an acceptable stage of development – how much it still has to learn – at which it can be deployed.

In militarised machine learning, that means political, military and industry leaders will have to specify how many civilian deaths will count as acceptable as the technology is refined.

Recent experiences of autonomous AI in society should serve as a warning.

Polisis AI Developed to Help People Understand Privacy Policies

It looks as though this AI development could be quite useful in helping people avoid the exploitation of their personal information. Someone reading this may also want to look into a resource called Terms of Service; Didn’t Read, which “aims at creating a transparent and peer-reviewed process to rate and analyse Terms of Service and Privacy Policies in order to create a rating from Class A to Class E.”

But one group of academics has proposed a way to make those virtually illegible privacy policies into the actual tool of consumer protection they pretend to be: an artificial intelligence that’s fluent in fine print. Today, researchers at Switzerland’s Federal Institute of Technology at Lausanne (EPFL), the University of Wisconsin and the University of Michigan announced the release of Polisis—short for “privacy policy analysis”—a new website and browser extension that uses their machine-learning-trained app to automatically read and make sense of any online service’s privacy policy, so you don’t have to.

In about 30 seconds, Polisis can read a privacy policy it’s never seen before and extract a readable summary, displayed in a graphic flow chart, of what kind of data a service collects, where that data could be sent, and whether a user can opt out of that collection or sharing. Polisis’ creators have also built a chat interface they call Pribot that’s designed to answer questions about any privacy policy, intended as a sort of privacy-focused paralegal advisor. Together, the researchers hope those tools can unlock the secrets of how tech firms use your data that have long been hidden in plain sight.

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Polisis isn’t actually the first attempt to use machine learning to pull human-readable information out of privacy policies. Both Carnegie Mellon University and Columbia have made their own attempts at similar projects in recent years, points out NYU Law Professor Florencia Marotta-Wurgler, who has focused her own research on user interactions with terms of service contracts online. (One of her own studies showed that only .07 percent of users actually click on a terms of service link before clicking “agree.”) The Usable Privacy Policy Project, a collaboration that includes both Columbia and CMU, released its own automated tool to annotate privacy policies just last month. But Marotta-Wurgler notes that Polisis’ visual and chat-bot interfaces haven’t been tried before, and says the latest project is also more detailed in how it defines different kinds of data. “The granularity is really nice,” Marotta-Wurgler says. “It’s a way of communicating this information that’s more interactive.”

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The researchers’ legalese-interpretation apps do still have some kinks to work out. Their conversational bot, in particular, seemed to misinterpret plenty of questions in WIRED’s testing. And for the moment, that bot still answers queries by flagging an intimidatingly large chunk of the original privacy policy; a feature to automatically simplify that excerpt into a short sentence or two remains “experimental,” the researchers warn.

But the researchers see their AI engine in part as the groundwork for future tools. They suggest that future apps could use their trained AI to automatically flag data practices that a user asks to be warned about, or to automate comparisons between different services’ policies that rank how aggressively each one siphons up and share your sensitive data.

“Caring about your privacy shouldn’t mean you have to read paragraphs and paragraphs of text,” says Michigan’s Schaub. But with more eyes on companies’ privacy practices—even automated ones—perhaps those information stewards will think twice before trying to bury their data collection bad habits under a mountain of legal minutiae.