Can a machine be ethical? Why teaching AI ethics is a minefield.
Artificial intelligence will soon be powerful enough to operate autonomously, how should we tell it to act? What kind of ethics should we teach it?
We are rapidly approaching the day when an autonomous artificial intelligence may have to make ethical decisions of great magnitude without human supervision. The question that we must answer is how it should act when life is on the line.
Helping us make our decision is philosopher James H. Moor, one of the first philosophers to make significant inroads into computer ethics. In his 2009 essay Four Kinds of Ethical Robots, he examines the possible ethical responsibilities machines could have and how we ought to think about it.
Dr. Moor categorizes machines of all kinds into four ethical groups. Each group has different ethical abilities that we need to account for when designing and responding to them.
Ethical impact agents
These are devices like watches which could have a positive or negative impact on humans. While a watch is unable to do anything but tell me what time it is, the timepiece could be wrong and therefore cause me to be late.
Implicit ethical agents
These are machines like ATMs that have certain ethical concerns addressed in their very design. ATMs, for example, have safeguards to assure they give out the proper amount of money and are just to both you and the bank.
Other machines can be implicitly vicious, such as a torture device which is designed to assure maximum pain and is failsafe against comfort. While these machines have distinct ethical features, they are part of the machine’s very being; and not the result of a decision process.
Explicit ethical agents
These are closer to what most of us think of when we think of programmable robots and artificial intelligence. These devices and machines can be “thought of as acting from ethics, not merely according to ethics.”
To use the example of an ATM again, while an ATM has to check your balance before you run off with all of the bank’s money, it doesn’t decide to do that because the programmer gave it an ethical code, it was explicitly told to check.
An explicit ethical agent would be an ATM which was told to always prevent theft and then decided to check your balance before giving you the one million dollars you asked it for so it might reach that end.
Full ethical agents
These are beings which function just like us, including free will and a sense of self. An entirely moral being, biological or not.
It is safe to say that no machine currently qualifies for this designation, and the bulk of academic and popular discussion focuses on explicit ethical agents. The idea of an entirely ethical device is a fascinating one, however, which is found in works such as 2001: A Space Odyssey.
So, if we have to worry about explicit agents, how should we tell them to act?
A major issue for computer ethics is what kind of algorithms an explicit ethical agent should follow. While many science fiction authors, philosophers, and futurists have proposed sets of rules before, many of them are lacking.
Dr. Moor gives the example of Isaac Asimov’s three rules of robotics. For those who need a refresher, they are:
- A robot may not injure a human being or, through inaction, allow a human being to come to harm.
- A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
- A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.
The rules are hierarchical, and the robots in Asimov’s books are all obligated to follow them.
Dr. Moor suggests that the problems with these rules are obvious. The first rule is so general that an artificial intelligence following them “might be obliged by the First Law to roam the world attempting to prevent harm from befalling human beings” and therefore be useless for its original function!
Such problems can be common in deontological systems, where following good rules can lead to funny results. Asimov himself wrote several stories about potential problems with the laws. Attempts to solve this issue abound, but the challenge of making enough rules to cover all possibilities remains.
On the other hand, a machine could be programmed to stick to utilitarian calculus when facing an ethical problem. This would be simple to do, as the computer would only have to be given a variable and told to make choices that would maximize the occurrence of it. While human happiness is a common choice, wealth, well-being, or security are also possibilities.
However, we might get what we ask for. The AI might decide to maximize human safety by making all risky technology it has access to stop dead. It could determine that human happiness is highest when all unhappy people are sent into lakes by self-driving cars.
How can we judge machines that make no choices? What would make an ethical machine a good one?
This is a tricky one. While we do hold people who claim they were “just following orders” as responsible, we do so because we presume they had the free will to do otherwise. With A.I. we lack that ability. Dr. Moor does think we can still judge how well a machine is making a decision, however.
He says that: “In principle, we could gather evidence about a robot’s ethical competence just as we gather evidence about the competence of human decision-makers, by comparing its decisions with those of humans, or else by asking the robot to provide justifications for its decisions.”
While this wouldn’t cover all aspects of ethical decision making, it would be a strong start for a device that can only follow an algorithm. This element isn’t all bad though, Dr. Moor is somewhat optimistic about the ability of such machines to make hard choices, as they might make difficult decisions “more competently and fairly than humans.”
As artificial intelligence gets smarter and our reliance on technology becomes more pronounced the need for a computer ethics becomes more pressing. If we can’t agree on how humans should act, how will we ever decide on how an intelligent machine should function? We should make up our minds quickly since the progress of AI shows no signs of slowing down.
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Is this proof of a dramatic shift?
- Map details dramatic shift from CNN to Fox News over 10-year period
- Does it show the triumph of "fake news" — or, rather, its defeat?
- A closer look at the map's legend allows for more complex analyses
Dramatic and misleading
Image: Reddit / SICResearch
The situation today: CNN pushed back to the edges of the country.
Over the course of no more than a decade, America has radically switched favorites when it comes to cable news networks. As this sequence of maps showing TMAs (Television Market Areas) suggests, CNN is out, Fox News is in.
The maps are certainly dramatic, but also a bit misleading. They nevertheless provide some insight into the state of journalism and the public's attitudes toward the press in the US.
Let's zoom in:
- It's 2008, on the eve of the Obama Era. CNN (blue) dominates the cable news landscape across America. Fox News (red) is an upstart (°1996) with a few regional bastions in the South.
- By 2010, Fox News has broken out of its southern heartland, colonizing markets in the Midwest and the Northwest — and even northern Maine and southern Alaska.
- Two years later, Fox News has lost those two outliers, but has filled up in the middle: it now boasts two large, contiguous blocks in the southeast and northwest, almost touching.
- In 2014, Fox News seems past its prime. The northwestern block has shrunk, the southeastern one has fragmented.
- Energised by Trump's 2016 presidential campaign, Fox News is back with a vengeance. Not only have Maine and Alaska gone from entirely blue to entirely red, so has most of the rest of the U.S. Fox News has plugged the Nebraska Gap: it's no longer possible to walk from coast to coast across CNN territory.
- By 2018, the fortunes from a decade earlier have almost reversed. Fox News rules the roost. CNN clings on to the Pacific Coast, New Mexico, Minnesota and parts of the Northeast — plus a smattering of metropolitan areas in the South and Midwest.
Image source: Reddit / SICResearch
This sequence of maps, showing America turning from blue to red, elicited strong reactions on the Reddit forum where it was published last week. For some, the takeover by Fox News illustrates the demise of all that's good and fair about news journalism. Among the comments?
- "The end is near."
- "The idiocracy grows."
- "(It's) like a spreading disease."
- "One of the more frightening maps I've seen."
- "LOL that's what happens when you're fake news!"
- "CNN went down the toilet on quality."
- "A Minecraft YouTuber could beat CNN's numbers."
- "CNN has become more like a high-school production of a news show."
Not a few find fault with both channels, even if not always to the same degree:
- "That anybody considers either of those networks good news sources is troubling."
- "Both leave you understanding less rather than more."
- "This is what happens when you spout bullsh-- for two years straight. People find an alternative — even if it's just different bullsh--."
- "CNN is sh-- but it's nowhere close to the outright bullsh-- and baseless propaganda Fox News spews."
"Old people learning to Google"
Image: Google Trends
CNN vs. Fox News search terms (200!-2018)
But what do the maps actually show? Created by SICResearch, they do show a huge evolution, but not of both cable news networks' audience size (i.e. Nielsen ratings). The dramatic shift is one in Google search trends. In other words, it shows how often people type in "CNN" or "Fox News" when surfing the web. And that does not necessarily reflect the relative popularity of both networks. As some commenters suggest:
- "I can't remember the last time that I've searched for a news channel on Google. Is it really that difficult for people to type 'cnn.com'?"
- "More than anything else, these maps show smart phone proliferation (among older people) more than anything else."
- "This is a map of how old people and rural areas have learned to use Google in the last decade."
- "This is basically a map of people who don't understand how the internet works, and it's no surprise that it leans conservative."
A visual image as strong as this map sequence looks designed to elicit a vehement response — and its lack of context offers viewers little new information to challenge their preconceptions. Like the news itself, cartography pretends to be objective, but always has an agenda of its own, even if just by the selection of its topics.
The trick is not to despair of maps (or news) but to get a good sense of the parameters that are in play. And, as is often the case (with both maps and news), what's left out is at least as significant as what's actually shown.
One important point: while Fox News is the sole major purveyor of news and opinion with a conservative/right-wing slant, CNN has more competition in the center/left part of the spectrum, notably from MSNBC.
Another: the average age of cable news viewers — whether they watch CNN or Fox News — is in the mid-60s. As a result of a shift in generational habits, TV viewing is down across the board. Younger people are more comfortable with a "cafeteria" approach to their news menu, selecting alternative and online sources for their information.
It should also be noted, however, that Fox News, according to Harvard's Nieman Lab, dominates Facebook when it comes to engagement among news outlets.
CNN, Fox and MSNBC
Image: Google Trends
CNN vs. Fox (without the 'News'; may include searches for actual foxes). See MSNBC (in yellow) for comparison
For the record, here are the Nielsen ratings for average daily viewer total for the three main cable news networks, for 2018 (compared to 2017):
- Fox News: 1,425,000 (-5%)
- MSNBC: 994,000 (+12%)
- CNN: 706,000 (-9%)
And according to this recent overview, the top 50 of the most popular websites in the U.S. includes cnn.com in 28th place, and foxnews.com in... 27th place.The top 5, in descending order, consists of google.com, youtube.com, facebook.com, amazon.com and yahoo.com — the latter being the highest-placed website in the News and Media category.
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