A Bright Side to the Dark Web – Cryptonews

Source: Adobe/manuelhuss

The dark web typically conjures up thoughts of criminal activity. While there are undoubtedly unsavory characters leveraging the darknet for illegal pursuits, there are also virtuous uses of the hidden internet.

As reported, per data from blockchain analytics provider Chainalysis, over USD 600 million worth of bitcoin (BTC) moved on darknet markets in Q4 of 2019. (However, the darknet still accounts for less than 1% of all BTC transactions.)

In this article, we will highlight the bright side of the dark web and discuss instances where it has played a role in making positive change.

In the last decade, there have been several high-profile whistleblowers who have exposed criminal actions by governments, such as spying on their own citizens. While whistleblowers have played pivotal roles in exposing the crimes of governments, they typically do so at great risk.

For example, in June 2013, The Guardian published an explosive article revealing that the American government, through the National Security Agency (NSA), had been spying on its citizens by accessing records pertaining to their phone calls. At the time, the data was said to be from an anonymous informer. However, former government contractor, Edward Snowden, eventually outed himself as the source of the leak.

Laura Poitras, the journalist who Snowden reached out to in order to share the information with the public, credits Tor, the infamous dark web browser, as one of the main tools which made the entire endeavor possible. In a Reddit AMA, she stated:

"It would have been impossible for us to work on the NSA stories and make "Citizenfour" without many encryption tools that allowed us to communicate more securely. In fact, in the credits, we thank several free software projects for making it all possible. It's definitely important that we support these tools so the creators can make them easier to use. They are incredibly underfunded for how important they are. You can donate to Tails, Tor and a few other projects at the Freedom of the Press Foundation."

Additionally, In 2015, President Obama signed an Executive Order barring donations to people or parties that may affect national security. The loose wording of the Order led to beliefs that it applied to whistleblowers like Snowden.

In response, people began to donate to The Snowden Defence Fund in bitcoin, exceeding any amount previously donated to the fund over a similar period of time. WikiLeaks also accepts donations in bitcoin and anonymous submissions over Tor.

While the dark web is considered to be a playground for shadowy figures peddling illegal wares, a University of Surrey research paper found that only 60% of websites on the dark web were of an illegal nature.

The rest of the dark web is utilized by law-abiding citizens who simply want to protect their privacy. This is especially true in the case of citizens living in repressive regimes where there is significant censorship. Totalitarian regimes typically limit access to the internet by throttling the bandwidth or more severe blanket website restrictions and blockages.

Cindy Cohn, the executive director of US e-rights campaign group EFF, explained the connection between legitimate sites with dark web mirrors and censorship resistance saying:

Facebook has a Tor instance for people in repressive regimes. We see Tor use go up whenever a dictatorship takes over or a coup occurs. Tibetans, United Arab Emirates, Tunisia, Egypt. The list goes on and on.

The dark web, accessed through Tor, can be an invaluable tool for those seeking to counter repressive regimes and their machinations. For instance, grassroots efforts leading to the adoption of the Tor browser in Mauritania led to the government abandoning the filtering of websites in 2005.

Additionally, when the government of Venezuela began to impose internet restrictions, citizens took to Tor to communicate. While the government eventually banned access to Tor through the state-owned internet service provider, some citizens were able to leverage their access to crypto to hedge themselves from inflation, conserve the value of their assets, and provide themselves with some liquidity on an as-needed basis.

Chainalysis noted in a recent report focused on the relationship between the COVID-19 pandemic, the bitcoin price correction, and activity on darknet markets that spending has slowed down.

"Darknet market revenue has fallen much more than wed expect following bitcoins recent major price drop...Perhaps darknet market customers arent buying as many drugs given the public health crisis," the Chainalysis report explained.

This state of affairs is not unique to darknet marketplaces as businesses across the world are reporting significant dips in profit. However, given the lockdown effective in many areas of the world, people are not frequenting bars, clubs, pubs, festivals or other such social situations where drugs are typically consumed. Thus, people just likely don't have the need for drugs, which is one of the biggest revenue drivers for the darknet vendors.

Despite the marked fall in profits for darknet vendors, many seem to be standing in solidarity with the rest of the world. The cybersecurity firm Digital Shadows reveals that many of the actors on dark web marketplaces are echoing information found on the surface web in regards to staying safe and flattening the curve.

Additionally, some hackers are even rejecting calls for ideas through which they can exploit the general public during the pandemic. Digital Shadows states:

As weve seen time and time again, cybercriminals will find ways to take advantage of peoples fears and uncertainties in the wake of major disasters and emergencies. However, the gravity of the COVID-19 pandemic has shown some benevolent reasoning has emerged on some platforms that are typically used for crime: Users urging others to avoid taking advantage of an already dire situation.

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A Bright Side to the Dark Web - Cryptonews

Government Surveillance Is a Dangerous Path to Trek – American Greatness

Big Brother is watching you appeared on billboards in George Orwells acclaimed novel, 1984, first published in 1949. Today, that ominous warning is rapidly becoming a reality.

In the past two decades, roughly since the tragic events of September 11, 2001, the U.S. government has increased its surveillance of Americans to a level unforeseen, even by Orwell.

Whether it be the National Security Administration, Central Intelligence Agency, Federal Bureau of Investigation, or your supposedly benign local government, you are being watched and tracked at almost every turn.

Making matters even more harrowing, the advent of smartphones, GPS tracking devices, social media, and countless other new technologies has made it easier than ever for private corporations to track your every move, thought, and desire. Just think about it: your every text message, Google search, phone call, and email is stored somewhere.

And as we have seen in the recent past, the government is not shy about forcing companies to hand over their users most private data should they deem it necessary. Although there is a need for the work of the NSA, CIA, FBI, or whichever organization claims jurisdiction to access sensitive personal data in extreme cases, such as an imminent terrorist attack, common sense and reality shows that these omnipotent agencies have been more than willing to seek data and information that is well outside the bounds of these strict guidelines.

Fortunately, whistleblowers such as Edward Snowden, a former NSA employee, have exposed some of the surveillance-state actions perpetrated by his ubiquitous former employer. Who knows what is actually happening in the deep corridors of these agencies, however, under the auspices of national security?

Although technology per se is not the primary driver of the increased government surveillance weve all come to expect and accept today, it certainly makes it much easier for governments to monitor their citizens. From a historical perspective, the scourge of surveillance has been alive and well for centuries. The Soviet KGB and the East German Stasi are just two examples of the sordid history of government surveillance.

In perhaps the most ominous current case, consider Communist Chinas massive surveillance apparatus, where every citizen is monitored constantly. Chinas social credit system is the most all-encompassing and terrifying surveillance program in world history.

For better or worse, most Americans are not overly concerned with government surveillanceyet. When we hear about things like city cameras capturing our every move, however, we should definitely raise our eyebrows. Even worse, stories abound over seemingly innocuous government surveillance operations that continue to push the envelope while trampling upon privacy rights and individual liberty.

The U.S. Constitution protects our privacy rights for a reason. Before our victory over Great Britain in the War of Independence, Americans (or colonists as they were then known) were victims of British surveillance and spy networks. Indeed, a primary reason for going to war against the mighty British military was so Americans would be free from surveillance.

Yet, here we are, more than two centuries later, struggling to avoid the onset of our own surveillance state. Although numerous officials from NSA, CIA, FBI, etc. will claim surveillance is necessary to protect the homeland, this is a false choice.

Benjamin Franklin warned us, Those who give up liberty for security deserve neither. This may be more relevant today than ever before.

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Government Surveillance Is a Dangerous Path to Trek - American Greatness

Google Engineers ‘Mutate’ AI to Make It Evolve Systems Faster Than We Can Code Them – ScienceAlert

Much of the work undertaken by artificial intelligence involves a training process known as machine learning, where AI gets better at a task such as recognising a cat or mapping a route the more it does it. Now that same technique is being use to create new AI systems, without any human intervention.

For years, engineers at Google have been working on a freakishly smart machine learning system known as theAutoML system(or automatic machine learning system), which is already capable of creating AI that outperforms anything we've made.

Now, researchers have tweaked it to incorporate concepts of Darwinian evolution and shown it can build AI programs that continue to improve upon themselves faster than they would if humans were doing the coding.

The new system is called AutoML-Zero, and although it may sound a little alarming, it could lead to the rapid development of smarter systems - for example, neural networked designed to more accurately mimic the human brain with multiple layers and weightings, something human coders have struggled with.

"It is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks," write the researchers in their pre-print paper. "We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space."

The original AutoML system is intended to make it easier for apps to leverage machine learning, and already includes plenty of automated features itself, but AutoML-Zero takes the required amount of human input way down.

Using a simple three-step process - setup, predict and learn - it can be thought of as machine learning from scratch.

The system starts off with a selection of 100 algorithms made by randomly combining simple mathematical operations. A sophisticated trial-and-error process then identifies the best performers, which are retained - with some tweaks - for another round of trials. In other words, the neural network is mutating as it goes.

When new code is produced, it's tested on AI tasks - like spotting the difference between a picture of a truck and a picture of a dog - and the best-performing algorithms are then kept for future iteration. Like survival of the fittest.

And it's fast too: the researchers reckon up to 10,000 possible algorithms can be searched through per second per processor (the more computer processors available for the task, the quicker it can work).

Eventually, this should see artificial intelligence systems become more widely used, and easier to access for programmers with no AI expertise. It might even help us eradicate human bias from AI, because humans are barely involved.

Work to improve AutoML-Zero continues, with the hope that it'll eventually be able to spit out algorithms that mere human programmers would never have thought of. Right now it's only capable of producing simple AI systems, but the researchers think the complexity can be scaled up rather rapidly.

"While most people were taking baby steps, [the researchers] took a giant leap into the unknown," computer scientist Risto Miikkulainen from the University of Texas, Austin, who was not involved in the work, told Edd Gent at Science. "This is one of those papers that could launch a lot of future research."

The research paper has yet to be published in a peer-reviewed journal, but can be viewed online at arXiv.org.

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Google Engineers 'Mutate' AI to Make It Evolve Systems Faster Than We Can Code Them - ScienceAlert

How AI Is Expanding The Applications Of Robo Advisory – Forbes

For the last couple of years, Artificial Intelligence (AI) has been changing many fields and increasing efficiency by using improved datasets. One of those areas where AI has accelerated evolution is the robo-advisory, which is a field having extensive financial big data to analyze.

Robo-advisors are the systems that use algorithms to automatically perform investment decisions or tasks which are mostly done by human advisors. Robo advisors are a potential solution to the complexities of financial decision making, said Jill E. Fisch, a law professor at the University of Pennsylvania at a conference of Pension Research Council.

In the main scheme, robo-advisors are merging customers information such as their financial goals, risk tolerances, timeframes, with the right asset allocation that qualifies customers needs. While making this merge, they use many algorithms including machine learning models to create the best fit for the customer. In the process of timeframe, they take lots of actions as well such as rebalancing the portfolio or performing tax-loss harvesting. This automatically increases efficiency while taking decisions at the right time for the portfolio.

AI usage in enterprises

Numerous enterprises have started to use AI in the robo-advisory field. Betterment is one of these robo-advisor enterprises that uses AI to reduce taxes on transactions where machine learning algorithms select the specific tax consequences of the transactions.Similar to Betterment, SigFig also uses its AI engine automatically to allocate assets and determines which investments will result in minimum taxes.

Another enterprise that uses AI is Wealthfront. Former CEO Adam Nash says, Were firm believers that artificial intelligence applied to your actual behavior will provide far more powerful advice than what traditional advisors offer today.

Also, Fidelity has already started its robo-advisory service in 2016 as Fidelity Go and as the beginning of 2019, Fidelity Go took top ranking as the best overall robo-advisor in the 2019 winter edition of The Robo Ranking report from Backend Benchmarking.

Efficiency side

The biggest impact of AI might be the time-saving base for human advisors. With AIs deep learning capabilities which relieve advisors from having to perform much of the rote or mundane monitoring and administrative tasks that currently allocate a significant portion of their time. When allocations fall outside of certain parameters for the specific clients, an AI-based system can trigger it into the monitor of the human advisor.

To increase efficiency, AI requires vast amounts of data to give more accurate results. Analysis of vast quantities of historical and financial data will uncover alpha opportunities that traditional analysis would otherwise overlook and give robo-advisors an edge over passive strategies and AI can process big data swiftly, allowing robo-advisors to adapt to changing market conditions and consumer behaviors much quicker in order to make better investment decisions. Time saved is key here, says John Zhang, founder of a robo-advisory startup WealthGap which explores AI in hedge funds-like portfolios.

Enterprises that offer robo-advisory services may not abandon the human component completely, but it seems the adoption of artificial intelligence is enhancing the platforms and they will be more able to give clients the big picture in the course of time.

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How AI Is Expanding The Applications Of Robo Advisory - Forbes

Using artificial intelligence against the spread of COVID-19 – JD Supra

Updated: May 25, 2018:

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Using artificial intelligence against the spread of COVID-19 - JD Supra

Navigating Artificial Intelligence and Consumer Protection Laws In Wake of the COVID-19 Pandemic – JD Supra

Updated: May 25, 2018:

JD Supra is a legal publishing service that connects experts and their content with broader audiences of professionals, journalists and associations.

This Privacy Policy describes how JD Supra, LLC ("JD Supra" or "we," "us," or "our") collects, uses and shares personal data collected from visitors to our website (located at http://www.jdsupra.com) (our "Website") who view only publicly-available content as well as subscribers to our services (such as our email digests or author tools)(our "Services"). By using our Website and registering for one of our Services, you are agreeing to the terms of this Privacy Policy.

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JD Supra's principal place of business is in the United States. By subscribing to our website, you expressly consent to your information being processed in the United States.

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Navigating Artificial Intelligence and Consumer Protection Laws In Wake of the COVID-19 Pandemic - JD Supra

Pentagon Needs Tools to Test the Limits of Its Artificial Intelligence Projects – Nextgov

The Pentagon is shopping around for ideas from industry regarding how it might better test and evaluate future artificial intelligence products to ensure they are safe and effective.

In a request for information this week, the Pentagons Joint Artificial Intelligence Center, or JAIC, seeks input on cutting-edge testing and evaluation capabilities to support the full spectrum of the Defense Departments emerging AI technologies, including machine learning, deep learning and neural networks.

According to the solicitation, the Pentagon wants to augment the JAICs Test and Evaluation office, which develops standards and conducts algorithm testing, system testing and operational testing on the militarys many AI initiatives.

The Pentagon stood up the JAIC in 2018 to centralize coordination and accelerate the adoption of AI and has been building out its ranks in recent months, hiring an official to implement its new AI ethical principles for warfare.

The JAIC is requesting testing tools and expertise in planning, data management, and analysis of inputs and outputs associated with those tools. The introduction of AI-enabled systems is bringing changes to the process, metrics, data, and skills necessary to produce the level of testing the military needs, and that is why the JAIC is requesting information, Dr. Jane Pinelis, Chief, Test, Evaluation and Assessment at the JAIC, said in a statement. Testing and Evaluation provides knowledge of system capabilities and limitations to the acquisition community and to the warfighter. The JAIC's T&E team will make rigorous and objective assessments of systems under operational conditions and against realistic threats, so that our warfighters ultimately trust the systems they are operating and that the risks associated with operating these systems are well-known to military acquisition decision-makers."

The solicitation indicates it plans to use feedback from the solicitation to guide how it further builds out its capabilities. Specifically, the Pentagon is interested in tech testing tools that focus on:

In addition, the Pentagon wants feedback regarding evaluation services in five mission areas: dataset curation, test harness development, model output analysis, test reporting and testing services. Lastly, it seeks other technologies it may not be aware of that may be beneficial to testing and evaluation efforts.

Responses to the RFI are due May 10.

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Pentagon Needs Tools to Test the Limits of Its Artificial Intelligence Projects - Nextgov

Artificial Intelligence and the Integrated Review: The Need for Strategic Prioritisation – RUSI Analysis

The governments Integrated Review comes at a time of considerable technological change. The UK has entered a Fourth Industrial Revolution (4IR), which promises to fundamentally alter the way we live, work, and relate to one another. This new era will be characterised by scientific breakthroughs in fields such as the Internet of Things, Blockchain, quantum computing, fifth-generation wireless technologies (5G), robotics, and artificial intelligence (AI), which together are expected to deliver transformational changes across almost every sector of the economy.

Of particular note are recent developments in AI, specifically advances in the sub-field of machine learning. Progress over the last decade has been driven by an exponential growth in computing power, coupled with increased availability of vast datasets with which to train machine learning algorithms. While machine learning technology can be traced back to at least the 1950s, investment has increased substantially in recent years, and as a result AI is rapidly becoming ubiquitous across the economy.

AI is often described as a general purpose technology its potential applications are manifold, ranging from mundane administrative tasks through to complex individual-level behavioural analysis, for instance to forecast consumer demand based on purchasing history, or to recommend music and films based on users personal interests. The ability of machine learning algorithms to rapidly derive insights from previously unexamined data has far-reaching ethical and societal implications, which are particularly pertinent in high risk contexts such as healthcare, law enforcement or defence.

There are countless ways in which the UKs defence and security sector could conceivably seek to deploy AI. Given its diverse applications, it will be essential to strategically prioritise the areas where AI is expected to provide the most immediate benefits, while being realistic about areas where its utility remains unproven. This strategic prioritisation process should be guided by the following three principles.

There is a natural tendency to overestimate the effects of new technology in the short term while underestimating the long-term impacts; the phenomenon is known as Amaras Law. While AI is likely to have a transformative impact on defence and security in the long term, any specific forecasts looking beyond the next decade are likely to be highly speculative. There is a risk that policy decisions are guided by hypothetical future uses and hyperbolic worst-case scenario outcomes, rather than focusing on realistic near-term applications. In reality, the immediate short-term benefits of AI will be an incremental augmentation of existing processes, rather than the creation of novel, futuristic capabilities. This will need to be appropriately reflected in development and procurement strategies.

Moreover, AI investment is often hampered by a lack of technical understanding, and customers are all too easily seduced by media hype and marketing buzzwords. Rates of predictive accuracy are often misinterpreted or misrepresented, making it difficult for the buyer to assess a tools real-world benefits. A focus on statistical accuracy may also distract from fundamental questions regarding the operational value of AI products when deployed in the field. In many cases, a non-AI solution may be more appropriate to the task at hand, and there will be situations in which use of AI will be undesirable or counterproductive.

Poor data quality or data availability can also pose major challenges. Developing effective machine learning systems requires access to large, well-curated datasets. Lack of access to clean, operationally relevant data can lead to frustration and delay during software development, particularly in sensitive contexts such as defence and security, where datasets often require additional protections and restrictions. Data requires substantial preparation, cleaning and pre-processing before it is suitable for machine analysis, which will need to be taken into account in the resourcing of government AI projects.

For these reasons, it is essential to ensure a sufficient degree of data analytics literacy among senior decision-makers responsible for AI procurement. The UK government should adopt a cautious and sceptical approach to the procurement of commercial AI technology, and refrain from committing to long-term contractual agreements before assessing a products real-world benefits. The importance of data quality and testing should not be underestimated: many products will fail to deliver as advertised when deployed in the field, and AI applications require iterative trialling, evaluation, verification and validation to maintain their efficacy.

AI is often characterised in terms of the ability to perform tasks normally requiring human intelligence. With organisations under increasing pressure to do more with less, AI can be viewed as an attractive option to minimise the human resources required to deliver certain business functions. But there are limits to the human processes that can be effectively automated. Existing AI is most useful when applied to narrowly defined, repetitive tasks. The more abstract the problem, the less useful AI becomes.

For this reason, the most immediate benefit from AI will be the ability to automate organisational, administrative and data-management processes, freeing up staff time to focus on more complex or abstract cognitive tasks. There are countless more innovative, experimental uses which will be of interest to the defence and security sector, but in many cases these remain at an early stage of development and their potential benefits are yet to be proven. Moreover, mundane uses of AI to automate repetitive administrative processes will typically not give rise to the same complex ethical challenges associated with more innovative applications.

In the short term, the main focus for AI investment should be the automation of organisational, administrative and data-management processes. Alongside this, efforts should focus on repurposing existing AI technology that is already widely used in other sectors (such as audiovisual analysis and natural language processing). To support innovation in the medium to long term, research funding should be made available for technology providers and academic institutions to co-develop proofs of concept and pilot projects for the more experimental, cutting-edge capabilities which are yet to be evaluated operationally.

Human expertise is the single most important component of any AI project. Cultivating technical expertise and developing a workforce of data-literate practitioners must therefore be a core objective of any future AI development strategy.

The UK government should invest in developing a core cell of data-science expertise to lead the development and deployment of new AI applications in the defence and security sector. This should be achieved by recruiting new talent, retraining current practitioners and partnering with academic institutions. Many of the AI capabilities the defence and security sector may wish to implement could be developed in-house without reliance on third-party providers, minimising costs and enabling a more agile approach to testing, evaluation and validation. The initial investment of developing this in-house technical expertise will therefore be more than recuperated by the cost savings made in the long term. At the same time, the skills required may often be more readily available outside the public sector, and there is a need to develop more agile models of strategic collaboration with external stakeholders to take full advantage of this expertise.

In addition to this core cell of technical expertise, it is essential to ensure a high level of data literacy among practitioners across the defence and security sector. When AI is integrated into a decision-making process overseen by a human operator, the user must sufficiently understand the limitations inherent in the system to be able to use the output in conjunction with their professional judgement. This is important not just to ensure accountable decision-making, but also to build trust between human operators and AI systems. Senior decision-makers must also have a foundational understanding of the benefits and shortcomings of different AI systems in order to maintain accountability at all levels of the decision-making chain.

Finally, any future AI development will need to be governed by a clear ethical and regulatory framework. Public discourse is increasingly focused on the governance and regulation of data analytics, and there are high expectations of transparency in how new technologies are developed and deployed. Despite an abundance of high-level data ethics principles, it remains unclear how these should be operationalised in different contexts. Additional sector-specific guidance should be developed to ensure ethical and proportionate use of AI for defence and security, including mechanisms for independent scrutiny and ethical oversight.

The views expressed in this Commentary are the author's, and do not represent those of RUSI or any other institution.

BANNER IMAGE: Representation of an artificial brain. Public domain

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Artificial Intelligence and the Integrated Review: The Need for Strategic Prioritisation - RUSI Analysis

Zoom is cracking down on virtual sex parties with artificial intelligence – Dazed

Now that were a month into lockdown, youve probably spent a considerable amount of your social life (read: all) on video messaging platforms. While its admittedly a great way to stay connected with friends when youre most likely cooped up in a cramped London flatshare, or enjoying a second wave of teenage angst at your parents house, its also led to some pretty raunchy gatherings: introducing the virtual sex party.

According to Rolling Stone, Zoom the popular teleconferencing app has become an unlikely gathering place for COVID-19 era millennials wanting to partake in play parties (AKA virtual chats where you can jerk off in the company of other socially-distanced people).

In short, Zooms not happy about it, and its using machine learning to identify accounts in violation of its policies, which strictly prohibit displays of nudity, violence, pornography, sexuality explicit material, or criminal activity.

We encourage users to report suspected violations of our policies, and we use a mix of tools, including machine learning, to proactively identify accounts that may be in violation, a spokesperson for Zoom told Rolling Stone.

While the platform hasnt specified what sort of machine-learning tools its using, or how the technology alerts the platform to pornographic content, a spokesperson said that itll take a number of actions against those caught in the act.

Meanwhile, rival video platform Houseparty is offering $1 millionfor info on an alleged smear campaign, which claims users have been getting their accounts hacked and personal information stolen. Basically, the internets reverted into the Wild West, and we love it.

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Zoom is cracking down on virtual sex parties with artificial intelligence - Dazed

Third Circuit Weighs In On Strict Products Liability for Artificial Intelligence – Lexology

In Rodgers v. Christie, a recent non-precedential decision, the United States Court of Appeals for the Third Circuit examined whether traditional strict products liability doctrines apply to artificial intelligence-based software. 2020 WL 1079233 (3d Cir. Mar. 6, 2020). There, plaintiffs asserted claims under the New Jersey Products Liability Act (PLA), arising from the States Public Safety Assessment (PSA). Id. at *1. The PSA is a data-based risk assessment algorithm which provides quantitative scores and a decision-making framework to assist courts in assess[ing] the risk that [a] criminal defendant will fail to appear for future court appearances or commit additional crimes and/or violent crimes if released. See Roders v. Laura and John Arnold Foundation, 2019 WL 2429574, at *1 (D.N.J. June 11, 2019), affd sub nom. Roders v. Christie, 2020 WL 1079233. Plaintiffs strict products liability claims put the PSA at issue, claiming the algorithm had assigned an erroneously low score to a convicted felon, who allegedly murdered their son three days after he was released from detention on non-monetary conditions. 2020 WL 1079233, at *1.

The trial court granted defendants motion to dismiss those claims on the basis that an algorithm, such as the PSA, cannot be considered a product subject to the PLA. In so holding, the trial court looked to the Restatement (Third) of Torts, which articulates two categories of products: (1) tangible personal property distributed commercially; and (2) [o]ther items, such as property and electricity . . . when the context of their distribution and use is sufficiently analogous to . . . tangible personal property. Id. citing Restatement (Third) of Torts 19. Thus, the court reasoned, because the PSA does not fit into either of these categories, it is not a product subject to the PLA, and plaintiffs claims could not proceed. 2019 WL 2429574, at *2-3.

On appeal, the Third Circuit upheld the dismissal of plaintiffs claims, holding that the PSA does not fit the definition of a product for purposes of the PLA for two reasons. First, the PSA, as a tool designed to assist courts, is not distributed commercially, and second, because information, guidance, ideas, and recommendations are not products under the Third Restatement, both as a definitional matter and because extending strict liability to the distribution of ideas would raise serious First Amendment concerns. 2020 WL 1079233, at *2 (internal quotations omitted). Importantly, the Court did not adopt a bright line rule barring the application of strict products liability claims for all artificial intelligence-based software only those which do not fit the Restatements definition.

While the Third Circuits decision Rodgers is non-precedential, it addresses a question many have flagged as central to the development of legal norms around emerging artificial intelligence-based products: whether artificial intelligence software is a product at all? As the Court astutely noted, this is a thorny question, which implicates concerns, such as the First Amendment, far beyond standard tort claims. All manner of commercial and consumer products are incorporating artificial intelligence, and courts around the country will be forced to answer this same question to determine how laws can appropriately address injuries arising from such products.

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Third Circuit Weighs In On Strict Products Liability for Artificial Intelligence - Lexology