EFF Seeks Greater Public Access to Patent Lawsuit Filed in Texas

1 week 1 day ago

You’re not supposed to be able to litigate in secret in the U.S. That’s especially true in a patent case dealing with technology that most internet users rely on every day.

 Unfortunately, that’s exactly what’s happening in a case called Entropic Communications, LLC v. Charter Communications, Inc. The parties have made so much of their dispute secret that it is hard to tell how the patents owned by Entropic might affect the Data Over Cable Service Interface Specifications (DOCSIS) standard, a key technical standard that ensures cable customers can access the internet.

In Entropic, both sides are experienced litigants who should know that this type of sealing is improper. Unfortunately, overbroad secrecy is common in patent litigation, particularly in cases filed in the U.S. District Court for the Eastern District of Texas.

EFF has sought to ensure public access to lawsuits in this district for years. In 2016, EFF intervened in another patent case in this very district, arguing that the heavy sealing by a patent owner called Blue Spike violated the public’s First Amendment and common law rights. A judge ordered the case unsealed.

As Entropic shows, however, parties still believe they can shut down the public’s access to presumptively public legal disputes. This secrecy has to stop. That’s why EFF, represented by the Science, Health & Information Clinic at Columbia Law School, filed a motion today seeking to intervene in the case and unseal a variety of legal briefs and evidence submitted in the case. EFF’s motion argues that the legal issues in the case and their potential implications for the DOCSIS standard are a matter of public concern and asks the district court judge hearing the case to provide greater public access.

Protective Orders Cannot Override The Public’s First Amendment Rights

As EFF’s motion describes, the parties appear to have agreed to keep much of their filings secret via what is known as a protective order. These court orders are common in litigation and prevent the parties from disclosing information that they obtain from one another during the fact-gathering phase of a case. Importantly, protective orders set the rules for information exchanged between the parties, not what is filed on a public court docket.

The parties in Entropic, however, are claiming that the protective order permits them to keep secret both legal arguments made in briefs filed with the court as well as evidence submitted with those filings. EFF’s motion argues that this contention is incorrect as a matter of law because the parties cannot use their agreement to abrogate the public’s First Amendment and common law rights to access court records. More generally, relying on protective orders to limit public access is problematic because parties in litigation often have little interest or incentive to make their filings public.

Unfortunately, parties in patent litigation too often seek to seal a variety of information that should be public. EFF continues to push back on these claims. In addition to our work in Texas, we have also intervened in a California patent case, where we also won an important transparency ruling. The court in that case prevented Uniloc, a company that had filed hundreds of patent lawsuits, from keeping the public in the dark as to its licensing activities.

That is why part of EFF’s motion asks the court to clarify that parties litigating in the Texas district court cannot rely on a protective order for secrecy and that they must instead seek permission from the court and justify any claim that material should be filed under seal.

On top of clarifying that the parties’ protective orders cannot frustrate the public’s right to access federal court records, we hope the motion in Entropic helps shed light on the claims and defenses at issue in this case, which are themselves a matter of public concern. The DOCSIS standard is used in virtually all cable internet modems around the world, so the claims made by Entropic may have broader consequences for anyone who connects to the internet via a cable modem.

It’s also impossible to tell if Entropic might want to sue more cable modem makers. So far, Entropic has sued five big cable modem vendors—Charter, Cox, Comcast, DISH TV, and DirecTV—in more than a dozen separate cases. EFF is hopeful that the records will shed light on how broadly Entropic believes its patents can reach cable modem technology.

EFF is extremely grateful that Columbia Law School’s Science, Health & Information Clinic could represent us in this case. We especially thank the student attorneys who worked on the filing, including Sean Hong, Gloria Yi, Hiba Ismail, and Stephanie Lim, and the clinic’s director, Christopher Morten.

Related Cases: Entropic Communications, LLC v. Charter Communications, Inc.
Aaron Mackey

【おすすめ本】後藤 秀典『東京電力の変節 最高裁・司法エリートとの癒着と原発被災者攻撃』―6・17判決の裏に何があったのか 闇の深淵部に切り込んだ=坂本充孝(ジャーナリスト)

1 week 1 day ago
 福島第一原発の事故からまもなく13年。現在も2万6千人を超える人々が故郷に戻れず避難生活を続けている。そんな人々に対して元々事故の当事者である東京電力の対応は不誠実極まりなかった。尊重すると誓いを立てた原子力損害賠償紛争解決センター(ADR)の和解案を4年以上も拒絶し続け、仲介打ちと切りとなった。 さらに2020年ごろから損害賠償を争う法廷で露骨な出し渋りの論理を展開し始める。一企業が弱者に対してこうまで攻撃的になるのはなぜなのか。裏側に迫ったのが本書である。 興味深いのは..
JCJ

The Tech Apocalypse Panic is Driven by AI Boosters, Military Tacticians, and Movies

1 week 1 day ago

There has been a tremendous amount of hand wringing and nervousness about how so-called artificial intelligence might end up destroying the world. The fretting has only gotten worse as a result of a U.S. State Department-commissioned report on the security risk of weaponized AI.

Whether these messages come from popular films like a War Games or The Terminator, reports that in digital simulations AI supposedly favors the nuclear option more than it should, or the idea that AI could assess nuclear threats quicker than humans—all of these scenarios have one thing in common: they end with nukes (almost) being launched because a computer either had the ability to pull the trigger or convinced humans to do so by simulating imminent nuclear threat. The purported risk of AI comes not just from yielding “control" to computers, but also the ability for advanced algorithmic systems to breach cybersecurity measures or manipulate and social engineer people with realistic voice, text, images, video, or digital impersonations

But there is one easy way to avoid a lot of this and prevent a self-inflicted doomsday: don’t give computers the capability to launch devastating weapons. This means both denying algorithms ultimate decision making powers, but it also means building in protocols and safeguards so that some kind of generative AI cannot be used to impersonate or simulate the orders capable of launching attacks. It’s really simple, and we’re by far not the only (or the first) people to suggest the radical idea that we just not integrate computer decision making into many important decisions–from deciding a person’s freedom to launching first or retaliatory strikes with nuclear weapons.


First, let’s define terms. To start, I am using "Artificial Intelligence" purely for expediency and because it is the term most commonly used by vendors and government agencies to describe automated algorithmic decision making despite the fact that it is a problematic term that shields human agency from criticism. What we are talking about here is an algorithmic system, fed a tremendous amount of historical or hypothetical information, that leverages probability and context in order to choose what outcomes are expected based on the data it has been fed. It’s how training algorithmic chatbots on posts from social media resulted in the chatbot regurgitating the racist rhetoric it was trained on. It’s also how predictive policing algorithms reaffirm racially biased policing by sending police to neighborhoods where the police already patrol and where they make a majority of their arrests. From the vantage of the data it looks as if that is the only neighborhood with crime because police don’t typically arrest people in other neighborhoods. As AI expert and technologist Joy Buolamwini has said, "With the adoption of AI systems, at first I thought we were looking at a mirror, but now I believe we're looking into a kaleidoscope of distortion... Because the technologies we believe to be bringing us into the future are actually taking us back from the progress already made."

Military Tactics Shouldn’t Drive AI Use

As EFF wrote in 2018, “Militaries must make sure they don't buy into the machine learning hype while missing the warning label. There's much to be done with machine learning, but plenty of reasons to keep it away from things like target selection, fire control, and most command, control, and intelligence (C2I) roles in the near future, and perhaps beyond that too.” (You can read EFF’s whole 2018 white paper: The Cautious Path to Advantage: How Militaries Should Plan for AI here

Just like in policing, in the military there must be a compelling directive (not to mention the marketing from eager companies hoping to get rich off defense contracts) to constantly be innovating in order to claim technical superiority. But integrating technology for innovation’s sake alone creates a great risk of unforeseen danger. AI-enhanced targeting is liable to get things wrong. AI can be fooled or tricked. It can be hacked. And giving AI the power to escalate armed conflicts, especially on a global or nuclear scale, might just bring about the much-feared AI apocalypse that can be avoided just by keeping a human finger on the button.


We’ve written before about how necessary it is to ban attempts for police to arm robots (either remote controlled or autonomous) in a domestic context for the same reasons. The idea of so-called autonomy among machines and robots creates the false sense of agency–the idea that only the computer is to blame for falsely targeting the wrong person or misreading signs of incoming missiles and launching a nuclear weapon in response–obscures who is really at fault. Humans put computers in charge of making the decisions, but humans also train the programs which make the decisions.

AI Does What We Tell It To

In the words of linguist Emily Bender,  “AI” and especially its text-based applications, is a “stochastic parrot” meaning that it echoes back to us things we taught it with as “determined by random, probabilistic distribution.” In short, we give it the material it learns, it learns it, and then draws conclusions and makes decisions based on that historical dataset. If you teach an algorithmic model that 9 times out of 10 a nation will launch a retaliatory strike when missiles are fired at them–the first time that model mistakes a flock of birds for inbound missiles, that is exactly what it will do.

To that end, AI scholar Kate Crawford argues, “AI is neither artificial nor intelligent. Rather, artificial intelligence is both embodied and material, made from natural resources, fuel, human labor, infrastructures, logistics, histories, and classifications. AI systems are not autonomous, rational, or able to discern anything without extensive datasets or predefined rules and rewards. In fact, artificial intelligence as we know it depends entirely on a much wider set of political and social structures. And due to the capital required to build AI at scale and the ways of seeing that it optimizes AI systems are ultimately designed to serve existing dominant interests.” 

AI does what we teach it to. It mimics the decisions it is taught to make either through hypotheticals or historical data. This means that, yet again, we are not powerless to a coming AI doomsday. We teach AI how to operate. We give it control of escalation, weaponry, and military response. We could just not.

Governing AI Doesn’t Mean Making it More Secret–It Means Regulating Use 

Part of the recent report commissioned by the U.S. Department of State on the weaponization of AI included one troubling recommendation: making the inner workings of AI more secret. In order to keep algorithms from being tampered with or manipulated, the full report (as summarized by Time) suggests that a new governmental regulatory agency responsible for AI should criminalize and make potentially punishable by jail time publishing the inner workings of AI. This means that how AI functions in our daily lives, and how the government uses it, could never be open source and would always live inside a black box where we could never learn the datasets informing its decision making. So much of our lives is already being governed by automated decision making, from the criminal justice system to employment, to criminalize the only route for people to know how those systems are being trained seems counterproductive and wrong.

Opening up the inner workings of AI puts more eyes on how a system functions and makes it more easy, not less, to spot manipulation and tampering… not to mention it might mitigate the biases and harms that skewed training datasets create in the first place.

Conclusion

Machine learning and algorithmic systems are useful tools whose potential we are only just beginning to grapple with—but we have to understand what these technologies are and what they are not. They are neither “artificial” or “intelligent”—they do not represent an alternate and spontaneously-occurring way of knowing independent of the human mind. People build these systems and train them to get a desired outcome. Even when outcomes from AI are unexpected, usually one can find their origins somewhere in the data systems they were trained on. Understanding this will go a long way toward responsibly shaping how and when AI is deployed, especially in a defense contract, and will hopefully alleviate some of our collective sci-fi panic.

This doesn’t mean that people won’t weaponize AI—and already are in the form of political disinformation or realistic impersonation. But the solution to that is not to outlaw AI entirely, nor is it handing over the keys to a nuclear arsenal to computers. We need a common sense system that respects innovation, regulates uses rather than the technology itself, and does not let panic, AI boosters, or military tacticians dictate how and when important systems are put under autonomous control. 

Matthew Guariglia