Daily Archives: May 2, 2017

Artificial intelligence can predict Supreme Court decisions better than some experts.

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By Matthew HutsonMay. 2, 2017 , 1:45 PM

See you in the Supreme Court! President Donald Trump tweeted last week, responding to lower court holds on his national security policies. But is taking cases all the way to the highest court in the land a good idea? Artificial intelligence may soon have the answer. A new study shows that computers can do a better job than legal scholarsat predicting Supreme Court decisions, even with less information.

Several other studies have guessed at justices behavior with algorithms. A 2011 project, for example, used the votes of any eight justices from 1953 to 2004 to predict the vote of the ninth in those same cases, with 83% accuracy. A 2004 paper tried seeing into the future, by using decisions from the nine justices whod been on the court since 1994 to predict the outcomes of cases in the 2002 term. That method had an accuracy of 75%.

The new study draws on a much richer set of data to predict the behavior of any set of justices at any time. Researchers used the Supreme Court Database, which contains information on cases dating back to 1791, to build a general algorithm for predicting any justices vote at any time. They drew on 16 features of each vote, including the justice, the term, the issue, and the court of origin. Researchers also added other factors, such as whether oral arguments were heard.

For each year from 1816 to 2015, the team created a machine-learning statistical model called a random forest. It looked at all prior years and found associations between case features and decision outcomes. Decision outcomes included whether the court reversed a lower courts decision and how each justice voted. The model then looked at the features of each case for that year and predicted decision outcomes. Finally, the algorithm was fed information about the outcomes, which allowed it to update its strategy and move on to the next year.

From 1816 until 2015, the algorithm correctly predicted 70.2% of the courts 28,000 decisions and 71.9% of the justices 240,000 votes, the authors report in PLOS ONE. That bests the popular betting strategy of always guess reverse, which has been the case in 63% of Supreme Court cases over the last 35 terms. Its also better than another strategy that uses rulings from the previous 10 years to automatically go with a reverse or an affirm prediction. Even knowledgeable legal experts are only about 66% accurate at predicting cases, the 2004 study found. Every time weve kept score, it hasnt been a terribly pretty picture for humans, says the studys lead author, Daniel Katz, a law professor at Illinois Institute of Technology in Chicago.

Roger Guimer, a physicist at Rovira i Virgili University in Tarragona, Spain, and lead author of the 2011 study, says the new algorithm is rigorous and well done. Andrew Martin, a political scientist at the University of Michigan in Ann Arbor and an author of the 2004 study, commends the new team for producing an algorithm that works well over 2 centuries. Theyre curating really large data sets and using state-of-the-art methods, he says. Thats scientifically really important.

Outside the lab, bankers and lawyers might put the new algorithm to practical use. Investors could bet on companies that might benefit from a likely ruling. And appellants could decide whether to take a case to the Supreme Court based on their chances of winning. The lawyers who typically argue these cases are not exactly bargain basement priced, Katz says.

Attorneys might also plug different variables into the model to forge their best path to a Supreme Court victory, including which lower court circuits are likely to rule in their favor, or the best type of plaintiff for a case. Michael Bommarito, a researcher at Chicago-Kent College of Law and study co-author, offers a real example in National Federation of Independent Business v. Sebelius, in which the Affordable Care Act was on the line: One of the things that made that really interesting was: Was it about free speech, was it about taxation, was it about some kind of health rights issues? The algorithm might have helped the plaintiffs decide which issue to highlight.

Future extensions of the algorithm could include the full text of oral arguments or even expert predictions. Says Katz: We believe the blend of experts, crowds, and algorithms is the secret sauce for the whole thing.

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Artificial intelligence prevails at predicting Supreme Court decisions - Science Magazine

Forbes

Does Artificial Intelligence Discriminate? Forbes As the old joke goes, on the internet nobody knows you're a dog. But thanks to the rise of artificial intelligence, not only do today's machines know you're a canine, they can tell what color dog you are -- and may treat you differently as a result. AI ...

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Does Artificial Intelligence Discriminate? - Forbes

Twitter hasn't done anything interesting with AI lately.

I know this because whatever machine learning they use to stop online harassment is more like an email filter to weed out some political fluff from your inbox or kill spam. Users are still able to create fake accounts, send harassing tweets, criticize you over and over again, remain completely anonymous, and come up with a variety of unflattering slams against celebrities that are never caught by the filters and go completely ignored for days or weeks on end.

That's what makes Mark Cuban's comments today about investing in Twitter because of their foray into AI a bit perplexing. What AI? The one that still lets trolls do whatever they want? The one that allows a tweet through that tells me to stick a fork into my front lobe?

There might be some confusion on this topic.

Recently, the company did implement new algorithms that can limit the accounts of users who show a pattern of abuse, something that is not exactly an AI. And, they've talked about using IBM Watson to help, but that's not exactly developing the AI in house.

An AI--designed and developed by Twitter--would identify abusive content based on context and be able to warn the offending user before he or she ever posts it in real time--say, when the user types a message that is obviously hurtful and tries to post it. Facebook has the same issue, because today you can post a revenge porn image or say something derogatory, and it's only when another user identifies the harmful comments or images that any pattern recognition kicks in.

The problem, of course, is that Twitter wants to appear intelligent. They haven't fully addressed the problem, and have let the issue slide since a blog post way back in March. If they are making progress on actual machine learning, they haven't let any of us know. Today, you can still tell someone to commit suicide or send other abusive comments--any real form of artificial intelligence would spot that and block it.

Twitter is walking on a tightrope here. To block very hurtful comments that do not use hate speech (something like "why don't you step in front of a truck") could be perceived as limiting free speech. An AI is not able to tell the difference quite yet. Context--e.g., who said what and why, when they said it, who they know, how often they engage in conversation--is difficult even for humans at times. I've received multiple tweets this last week that I took as hurtful and harmful, and I'd rather not see them, but it's all part of living in the age of trolls. If Twitter made a better AI, I could simply choose to block these tweets. If someone keeps sending death threats, or I report that person, or they use abusive words or hate speech, then Twitter's machine learning might kick in--or it might not. The problem is that, if the AI is only partially effective, is it really effective at all?

Meanwhile, an entire generation of people under 30 have moved over to Instagram and tend to avoid Twitter, which is widely known as a bastion of trolls.

There's often a serious misunderstanding about how an AI works. It's not just a filter or an algorithm. There has to be "intelligence" and understanding, a way to make a decision about context. That's the really hard part. Filters have existed for decades, but computer scientists know that an AI has to be able to deal with fuzzy logic and even moral quagmires. Is someone I know just joking around and telling me to jump off of a bridge? Is it someone who has a bone to pick with me because they invest in the company I'm criticizing? When I've tweeted before about email overload, the people who often send abusive comments just happen to be email marketers. An AI would have to understand that, and it's not exactly humming along nicely at Twitter.

There's no reason to think Twitter won't solve this, but for now--the machine learning is more like a spam filter. That doesn't seem like rocket science to me.

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Why Mark Cuban is Dead Wrong About Twitter and Artificial Intelligence - Inc.com

If your company is not embracing artificial intelligence, it is going to suffer. That's the message from all corners of the tech and business media world these days. CIO Dive is no exception. We've reported on the growing reliance on artificial intelligence everywhere from call centers to cloud computing to the airline industry.

Yet, the state of technology is still a long way from imbuing machines with human-level intelligence. It's even further from the merging of machine and humans that fans of the technological singularity believe will unlock our true potential and, eventually, immortality.

Despite the remarkable victory that Google's AI-powered Alpha computer scored against the world's top Go player, there is healthy debate around when machines will be able to truly attain human-like intelligence. That would mean a machine that could do more than just recognizing patterns and learning from mistakes, but also accurately responding to new information and understanding unstructured data.

Plus, it is no easy task to transfer a given type of AI from one application to another. For example, The Nature Conservancy wants to fight illegal fishing by using facial recognition software, running on cameras mounted over haul-in decks on fishing boats, to mark whenever an endangered or non-target species is brought aboard and not thrown back.

But it's not as simple as uploading a catalog of fish faces and pressing enter. Constantly changing light, variations in the orientation of the fish to the camera, and the movement of the boat all complicate matters. Kaggle, a code crowdsourcing platform, recently held a contest to incentivize coders to write software that addressed those variables.

Yet, the more pressing question around AI is not whether it has truly arrived, but whether the AI features vendors are trying to sell your company actually, and consistently, work as advertised. And if they'll meet your objectives.

The healthcare industry stands to benefit significantly from AI. Wearable devices are being developed to track changes or patterns in a patient's vital signs that could signal an approaching cardiac event. When one is detected, a physician can be alerted automatically. Of course, that's very different than relying on technology to make an actual clinical decision.

But a number of companies are starting to sell digital health assistants. This technology accesses a patient's medical records and analyzes input from the user to assess symptoms and provide a possible diagnosis. But why should we trust these platforms?

That's a question that Zoubin Ghahramani, professor of Information Engineering at the University of Cambridge, has spent a lot of time pondering.

We know that machine learning improves over time, as the software accrues more data and essentially learns from past events. So what if, Ghahramani and his team posit in a University of Cambridge research brief, we designed artificial intelligence with training wheels of sorts? Vehicles with autopilot mode, for instance, might ping a driver for help in unfamiliar territory,if the car's cameras or sensors are not capturing adequate data for processing.

But unless you've actually written the algorithms that power that autopilot, or any other piece of AI technology, it is not clear how the system reached a decision, or the soundness of that decision.

"We really view the whole mathematics of machine learning as sitting inside a framework of understanding uncertainty. Before you see data whether you are a baby learning a language or a scientist analyzing some data you start with a lot of uncertainty and then as you have more and more data you have more and more certainty," Ghahramani said.

"When machines make decisions, we want them to be clear on what stage they have reached in this process," he said. "And when they are unsure, we want them to tell us."

Last year, and with collaborators from the University of Oxford, Imperial College London, and at the University of California, Berkeley, Ghahramani helped launch the Leverhulme Centre for the Future of Intelligence.

One of the center's area of study is trust and transparency around AI, while other areas of focus include policy, security and the impacts that AI could have on personhood.

Adrian Weller, a senior researcher on Ghahramani's team and the trust and transparency research leader at the Leverhulme Centre for the Future of Intelligence, explained that AI systems based on machine learning use processes to arrive at decisions that do not mimic the "rational decision-making pathways" that humans comprehend. Using visualization and other approaches, the center is creating tools that can put AI decision processes in a human context.

The goal is not just to provide tools for cognitive scientists, but also for policy makers, social scientists, and even philosophers, because they will also take roles in integrating AI into society.

But by providing a means for making AI functions more transparent, commercial users of AI tools and their consumers could better understand how it works, determine its trustworthiness, and decide whether it is likely to meet the company's or its customer's needs.

The tech industry has begun collaborating around guiding principles to help ensure AI is deployed in an ethical, equitable, and secure manner. Representatives from Amazon, Apple, Facebook, IBM, Google and Microsoft have joined with academics as well as groups including the ACLU and MacArthur Foundation to form the Partnership on AI.

It seeks to explore the influence of AI on society and is organized around themes that include safety, transparency, labor and social good.

But a system for rating AI features and ensuring compliance with basic quality metrics similar to how Underwriters Lab ensures appliances meet basic safety or performance measures could also go a long way toward helping end users evaluate AI products.

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The skeptic's guide to artificial intelligence - CIO Dive

OXFORD Artificial Intelligence is the next technological frontier, and it has the potential to make or break the world order. The AI revolution could pull the bottom billion out of poverty and transform dysfunctional institutions, or it could entrench injustice and increase inequality. The outcome will depend on how we manage the coming changes.

Unfortunately, when it comes to managing technological revolutions, humanity has a rather poor track record. Consider the Internet, which has had an enormous impact on societies worldwide, changing how we communicate, work, and occupy ourselves. And it has disrupted some economic sectors, forced changes to long-established business models, and created a few entirely new industries.

But the Internet has not brought the kind of comprehensive transformation that many anticipated. It certainly didnt resolve the big problems, such as eradicating poverty or enabling us to reach Mars. As PayPal co-founder Peter Thiel once noted: We wanted flying cars; instead, we got 140 characters.

In fact, in some ways, the Internet has exacerbated our problems. While it has created opportunities for ordinary people, it has created even more opportunities for the wealthiest and most powerful. A recent study by researchers at the LSE reveals that the Internet has increased inequality, with educated, high-income people deriving the greatest benefits online and multinational corporations able to grow massively while evading accountability.

Perhaps, though, the AI revolution can deliver the change we need. Already, AI which focuses on advancing the cognitive functions of machines so that they can learn on their own is reshaping our lives. It has delivered self-driving (though still not flying) cars, as well as virtual personal assistants and even autonomous weapons.

But this barely scratches the surface of AIs potential, which is likely to produce societal, economic, and political transformations that we cannot yet fully comprehend. AI will not become a new industry; it will penetrate and permanently alter every industry in existence. AI will not change human life; it will change the boundaries and meaning of being human.

How and when this transformation will happen and how to manage its far-reaching effects are questions that keep scholars and policymakers up at night. Expectations for the AI era range from visions of paradise, in which all of humanitys problems have been solved, to fears of dystopia, in which our creation becomes an existential threat.

Making predictions about scientific breakthroughs is notoriously difficult. On September 11, 1933, the famed nuclear physicist Lord Rutherford told a large audience, Anyone who looks for a source of power in the transformation of the atoms is talking moonshine. The next morning, Leo Szilard hypothesized the idea of a neutron-induced nuclear chain reaction; soon thereafter, he patented the nuclear reactor.

The problem, for some, is the assumption that new technological breakthroughs are incomparable to those in the past. Many scholars, pundits, and practitioners would agree with Alphabet Executive Chairman Eric Schmidt that technological phenomena have their own intrinsic properties, which humans dont understand and should not mess with.

Others may be making the opposite mistake, placing too much stock in historical analogies. The technology writer and researcher Evgeny Morozov, among others, expects some degree of path dependence, with current discourses shaping our thinking about the future of technology, thereby influencing technologys development. Future technologies could subsequently impact our narratives, creating a sort of self-reinforcing loop.

To think about a technological breakthrough like AI, we must find a balance between these approaches. We must adopt an interdisciplinary perspective, underpinned by an agreed vocabulary and a common conceptual framework. We also need policies that address the interconnections among technology, governance, and ethics. Recent initiatives, such as Partnership on AI or Ethics and Governance of AI Fund are a step in the right direction, but lack the necessary government involvement.

These steps are necessary to answer some fundamental questions: What makes humans human? Is it the pursuit of hyper-efficiency the Silicon Valley mindset? Or is it irrationality, imperfection, and doubt traits beyond the reach of any non-biological entity?

Only by answering such questions can we determine which values we must protect and preserve in the coming AI age, as we rethink the basic concepts and terms of our social contracts, including the national and international institutions that have allowed inequality and insecurity to proliferate. In a context of far-reaching transformation, brought about by the rise of AI, we may be able to reshape the status quo, so that it ensures greater security and fairness.

One of the keys to creating a more egalitarian future relates to data. Progress in AI relies on the availability and analysis of large sets of data on human activity, online and offline, to distinguish patterns of behavior that can be used to guide machine behavior and cognition. Empowering all people in the age of AI will require each individual not major companies to own the data they create.

With the right approach, we could ensure that AI empowers people on an unprecedented scale. Though abundant historical evidence casts doubt on such an outcome, perhaps doubt is the key. As the late sociologist Zygmunt Bauman put it, questioning the ostensibly unquestionable premises of our way of life is arguably the most urgent of services we owe our fellow humans and ourselves.

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Democratizing Artificial Intelligence - Project Syndicate