Riddhi Setty, Bloomberg Law; Online Copyright Piracy Debate Ramps Up Over Proposed Legal Fix
"Sen.
Showing posts with label algorithms. Show all posts
Showing posts with label algorithms. Show all posts
Saturday, March 26, 2022
Online Copyright Piracy Debate Ramps Up Over Proposed Legal Fix; Bloomberg Law, March 23, 2022
Wednesday, February 21, 2018
Patenting the Future of Medicine: The Intersection of Patent Law and Artificial Intelligence in Medicine; Lexology, February 14, 2018
Finnegan, Henderson, Farabow, Garrett & Dunner LLP - Susan Y. Tull, Lexology; Patenting the Future of Medicine: The Intersection of Patent Law and Artificial Intelligence in Medicine
"Artificial intelligence (AI) is rapidly transforming the world of medicine, and the intellectual property directed to these inventions must keep pace. AI computers are diagnosing medical conditions and disorders at a rate equal to or better than their human peers, all while developing their own software code and algorithms to do so. These recent advances raise issues of patentability, inventorship, and ownership as machine-based learning evolves."
"Artificial intelligence (AI) is rapidly transforming the world of medicine, and the intellectual property directed to these inventions must keep pace. AI computers are diagnosing medical conditions and disorders at a rate equal to or better than their human peers, all while developing their own software code and algorithms to do so. These recent advances raise issues of patentability, inventorship, and ownership as machine-based learning evolves."
Sunday, July 16, 2017
How can we stop algorithms telling lies?; Guardian, July 16, 2017
Cathy O'Neil, Guardian;
[Kip Currier: Cathy O'Neil is shining much-needed light on the little-known but influential power of algorithms on key aspects of our lives. I'm using her thought-provoking 2016 Weapons of Math Destruction: How Big Data Increases Inequality And Threatens Democracy as one of several required reading texts in my Information Ethics graduate course at the University of Pittsburgh's School of Computing and Information.]
"A proliferation of silent and undetectable car crashes is harder to investigate than when it happens in plain sight.
I’d still maintain there’s hope. One of the miracles of being a data sceptic in a land of data evangelists is that people are so impressed with their technology, even when it is unintentionally creating harm, they openly describe how amazing it is. And the fact that we’ve already come across quite a few examples of algorithmic harm means that, as secret and opaque as these algorithms are, they’re eventually going to be discovered, albeit after they’ve caused a lot of trouble.
What does this mean for the future? First and foremost, we need to start keeping track. Each criminal algorithm we discover should be seen as a test case. Do the rule-breakers get into trouble? How much? Are the rules enforced, and what is the penalty? As we learned after the 2008 financial crisis, a rule is ignored if the penalty for breaking it is less than the profit pocketed. And that goes double for a broken rule that is only discovered half the time...
It’s time to gird ourselves for a fight. It will eventually be a technological arms race, but it starts, now, as a political fight. We need to demand evidence that algorithms with the potential to harm us be shown to be acting fairly, legally, and consistently. When we find problems, we need to enforce our laws with sufficiently hefty fines that companies don’t find it profitable to cheat in the first place. This is the time to start demanding that the machines work for us, and not the other way around."
How can we stop algorithms telling lies?
[Kip Currier: Cathy O'Neil is shining much-needed light on the little-known but influential power of algorithms on key aspects of our lives. I'm using her thought-provoking 2016 Weapons of Math Destruction: How Big Data Increases Inequality And Threatens Democracy as one of several required reading texts in my Information Ethics graduate course at the University of Pittsburgh's School of Computing and Information.]
"A proliferation of silent and undetectable car crashes is harder to investigate than when it happens in plain sight.
I’d still maintain there’s hope. One of the miracles of being a data sceptic in a land of data evangelists is that people are so impressed with their technology, even when it is unintentionally creating harm, they openly describe how amazing it is. And the fact that we’ve already come across quite a few examples of algorithmic harm means that, as secret and opaque as these algorithms are, they’re eventually going to be discovered, albeit after they’ve caused a lot of trouble.
What does this mean for the future? First and foremost, we need to start keeping track. Each criminal algorithm we discover should be seen as a test case. Do the rule-breakers get into trouble? How much? Are the rules enforced, and what is the penalty? As we learned after the 2008 financial crisis, a rule is ignored if the penalty for breaking it is less than the profit pocketed. And that goes double for a broken rule that is only discovered half the time...
It’s time to gird ourselves for a fight. It will eventually be a technological arms race, but it starts, now, as a political fight. We need to demand evidence that algorithms with the potential to harm us be shown to be acting fairly, legally, and consistently. When we find problems, we need to enforce our laws with sufficiently hefty fines that companies don’t find it profitable to cheat in the first place. This is the time to start demanding that the machines work for us, and not the other way around."
Tuesday, June 13, 2017
When a Computer Program Keeps You in Jail; New York Times, June 13, 2017
Rebecca Wexler, New York Times; When a Computer Program Keeps You in Jail
"The criminal justice system is becoming automated. At every stage — from policing and investigations to bail, evidence, sentencing and parole — computer systems play a role. Artificial intelligence deploys cops on the beat. Audio sensors generate gunshot alerts. Forensic analysts use probabilistic software programs to evaluate fingerprints, faces and DNA. Risk-assessment instruments help to determine who is incarcerated and for how long.
Technological advancement is, in theory, a welcome development. But in practice, aspects of automation are making the justice system less fair for criminal defendants.
The root of the problem is that automated criminal justice technologies are largely privately owned and sold for profit. The developers tend to view their technologies as trade secrets. As a result, they often refuse to disclose details about how their tools work, even to criminal defendants and their attorneys, even under a protective order, even in the controlled context of a criminal proceeding or parole hearing."
"The criminal justice system is becoming automated. At every stage — from policing and investigations to bail, evidence, sentencing and parole — computer systems play a role. Artificial intelligence deploys cops on the beat. Audio sensors generate gunshot alerts. Forensic analysts use probabilistic software programs to evaluate fingerprints, faces and DNA. Risk-assessment instruments help to determine who is incarcerated and for how long.
Technological advancement is, in theory, a welcome development. But in practice, aspects of automation are making the justice system less fair for criminal defendants.
The root of the problem is that automated criminal justice technologies are largely privately owned and sold for profit. The developers tend to view their technologies as trade secrets. As a result, they often refuse to disclose details about how their tools work, even to criminal defendants and their attorneys, even under a protective order, even in the controlled context of a criminal proceeding or parole hearing."
Monday, March 6, 2017
Patent Data – The Modern Investor’s Crystal Ball; Intellectual Property Watch, March 6, 2017
Sirena Rubinoff, Intellectual Property Watch;
"What if there was a crystal ball that could tell you where and when to invest your money? It sounds like science fiction, but engineers at MIT have actually developed a formula that can predict future events in tech development. The formula is based on a combination of big data from patent applications and smart analytics which, when put together, can estimate how fast a technology is advancing.
Patent Data – The Modern Investor’s Crystal Ball
"What if there was a crystal ball that could tell you where and when to invest your money? It sounds like science fiction, but engineers at MIT have actually developed a formula that can predict future events in tech development. The formula is based on a combination of big data from patent applications and smart analytics which, when put together, can estimate how fast a technology is advancing.
Why patent applications?
If you want to know where technology is headed, a great place to look is in a patent application database like the USPTO. One of the qualifications for getting a patent granted is “novelty,” which means new, similar innovations won’t appear anywhere else. Once enough data is collected from the database, it can be used to map out and predict unique advancements in specific areas of technology."
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