Showing posts with label Product Liability. Show all posts
Showing posts with label Product Liability. Show all posts

Artificial Intelligence - AI Product Liability.


Product liability is a legal framework that holds the seller, manufacturer, distributor, and others in the distribution chain liable for damage caused by their goods to customers.

Victims are entitled to financial compensation from the accountable corporation.

The basic purpose of product liability legislation is to promote societal safety by discouraging wrongdoers from developing and distributing unsafe items to the general public.

Users and third-party spectators may also sue if certain conditions are satisfied, such as foreseeability of the harm.

Because product liability is governed by state law rather than federal law in the United States, the applicable legislation in each case may change depending on the location of the harm.

In the past, victims had to establish that the firm responsible was negligent, which meant that its acts did not reach the acceptable level of care, in order to prevail in court and be reimbursed for their injuries.

Four components must be shown in order to establish negligence.

  • First and foremost, the corporation must owe the customer a legal duty of care.
  • Second, that responsibility was violated, implying that the producer failed to fulfill the requisite level.
  • Third, the breach of duty resulted in the injury, implying that the manufacturer's activities resulted in the damage.
  • Finally, the victims must have had genuine injuries.

One approach to get compensated for product injury is to show that the corporation was negligent.

Product liability lawsuits may also be established by demonstrating that the corporation failed to uphold its guarantees to customers about the product's quality and dependability.

Express warranties may specify how long the product is covered by the warranty, as well as which components of the product are covered and which are not.

Implied guarantees that apply to all items include promises that the product will function as advertised and for the purpose for which the customer acquired it.

In the great majority of product liability cases, the courts will apply strict liability, which means that the corporation will be held accountable regardless of guilt if the standards are satisfied.

This is because the courts have determined that customers would have a tough time proving the firm is irresponsible since the company has greater expertise and resources.

Instead of proving that a duty was breached, consumers must show that the product contained an unreasonably dangerous defect; the defect caused the injury while the product was being used for its intended purpose; and the product was not substantially altered from the condition in which it was sold to consumers.

Design flaws, manufacturing flaws, and marketing flaws, sometimes known as failure to warn, are the three categories of defects that may be claimed for product responsibility.

When there are defects in the design of the product itself at the planning stage, this is referred to as a design defect.

If there was a foreseeable danger that the product might cause harm when used by customers when it was being created, the corporation would be liable.

When there are issues throughout the production process, such as the use of low-quality materials or shoddy craftsmanship, it is referred to as a manufacturing fault.

The final product falls short of the design's otherwise acceptable quality.

Failure to notify flaws occurs when a product involves an inherent hazard, regardless of how well it was designed or made, yet the corporation failed to provide customers with warnings that the product may be harmful.

While product liability law was created to cope with the advent of more complicated technologies that may cause consumer damage, it's unclear if the present legislation can apply to AI or whether it has to be updated to completely safeguard consumers.

When it comes to AI, there are various areas where the law will need to be clarified or changed.

Product liability requires the presence of a product, and it is not always apparent whether software or an algorithm is a product or a service.

Product liability law would apply if they were classed as such.

When it comes to services, consumers must depend on typical negligence claims.

Consumers' capacity to sue a manufacturer under product liability will be determined by the specific AI technology that caused the injury and what the court concludes in each case.

When AI technology is able to learn and behave independently of its initial programming, new problems arise.

Because the AI's behaviors may not have been predictable in certain situations, it's unclear if a damage can still be linked to the product's design or production.

Furthermore, since AI depends on probability-based predictions and will, at some time, make a decision that causes harm even if it is the optimal course of action, it may not be fair for the maker to bear the risk when the AI is likely to produce harm by design.

In response to these difficult concerns, some commentators have recommended that AI be held to a different legal standard than conventional goods, such as strict responsibility.

They propose, for example, that medical AI technology be regarded as if it were a reasonable human doctor or medical student, and that autonomous automobiles be treated as if they were a reasonable human driver.

Artificial intelligence products would still be liable for customer harm, but the threshold they would have to reach would be that of a reasonable person in the identical circumstance.

Only if a human in the identical scenario would have been unable to avoid inflicting the damage would the AI be held accountable for the injuries.

This raises the issue of whether the designers or manufacturers would be held vicariously accountable since they had the right, capacity, and obligation to govern the AI, or if the AI would be considered a legal person responsible for paying the victims on its own.

As AI technology advances, it will become more difficult to distinguish between traditional and more sophisticated products.

However, because there are currently no alternatives in the law, product liability will continue to be the legal framework for determining who is responsible and under what circumstances consumers must be financially compensated when AI causes injuries.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Accidents and Risk Assessment; Autonomous and Semiautonomous Systems; Calo, Ryan; Driverless Vehicles and Liability; Trolley Problem.

References & Further Reading:

Kaye, Timothy S. 2015. ABA Fundamentals: Products Liability Law. Chicago: American Bar Association.

Owen, David. 2014. Products Liability in a Nutshell. St. Paul, MN: West Academic Publishing.

Turner, Jacob. 2018. Robot Rules: Regulating Artificial Intelligence. Cham, Switzerland: Palgrave Macmillan.

Weaver, John Frank. 2013. Robots Are People Too: How Siri, Google Car, and Artificial Intelligence Will Force Us to Change Our Laws. Santa Barbara, CA: Praeger.

Artificial Intelligence - How Are Accidents and Risk Assessment Done Using AI?


Many computer-based systems' most significant feature is their reliability.

Physical damage, data loss, economic disruption, and human deaths may all result from mechanical and software failures.

Many essential systems are now controlled by robotics, automation, and artificial intelligence.

Nuclear power plants, financial markets, social security payments, traffic lights, and military radar stations are all under their watchful eye.

High-tech systems may be designed purposefully hazardous to people, as with Trojan horses, viruses, and spyware, or they can be dangerous due to human programming or operation errors.

They may become dangerous in the future as a result of purposeful or unintended actions made by the machines themselves, or as a result of unanticipated environmental variables.

The first person to be murdered while working with a robot occurred in 1979.

A one-ton parts-retrieval robot built by Litton Industries hit Ford Motor Company engineer Robert Williams in the head.

After failing to entirely switch off a malfunctioning robot on the production floor at Kawasaki Heavy Industries two years later, Japanese engineer Kenji Urada was murdered.

Urada was shoved into a grinding machine by the robot's arm.

Accidents do not always result in deaths.

A 300-pound Knightscope K5 security robot on patrol at a retail business center in Northern California, for example, knocked down a kid and ran over his foot in 2016.

Only a few cuts and swelling were sustained by the youngster.

The Cold War's history is littered with stories of nuclear near-misses caused by faulty computer technology.

In 1979, a computer glitch at the North American Aerospace Defense Command (NORAD) misled the Strategic Air Command into believing that the Soviet Union had fired over 2,000 nuclear missiles towards the US.

An examination revealed that a training scenario had been uploaded to an active defense computer by mistake.

In 1983, a Soviet military early warning system identified a single US intercontinental ballistic missile launching a nuclear assault.

Stanislav Petrov, the missile defense system's operator, correctly discounted the signal as a false alarm.

The reason of this and subsequent false alarms was ultimately discovered to be sunlight hitting high altitude clouds.

Petrov was eventually punished for humiliating his superiors by disclosing faults, despite preventing global thermonuclear Armageddon.

The so-called "2010 Flash Crash" was caused by stock market trading software.

In slightly over a half-hour on May 6, 2010, the S&P 500, Dow Jones, and NASDAQ stock indexes lost—and then mainly regained—a trillion dollars in value.

Navin Dal Singh Sarao, a U.K. trader, was arrested after a five-year investigation by the US Department of Justice for allegedly manipulating an automated system to issue and then cancel huge numbers of sell orders, allowing his business to acquire equities at temporarily reduced prices.

In 2015, there were two more software-induced market flash crashes, and in 2017, there were flash crashes in the gold futures market and digital cryptocurrency sector.

Tay (short for "Thinking about you"), a Microsoft Corporation artificial intelligence social media chatterbot, went tragically wrong in 2016.

Tay was created by Microsoft engineers to imitate a nineteen-year-old American girl and to learn from Twitter discussions.

Instead, Tay was trained to use harsh and aggressive language by internet trolls, which it then repeated in tweets.

After barely sixteen hours, Microsoft deleted Tay's account.

More AI-related accidents in motor vehicle operating may occur in the future.

In 2016, the first fatal collision involving a self-driving car happened when a Tesla Model S in autopilot mode collided with a semi-trailer crossing the highway.

The motorist may have been viewing a Harry Potter movie on a portable DVD player when the accident happened, according to witnesses.

Tesla's software does not yet allow for completely autonomous driving, hence a human operator is required.

Despite these dangers, one management consulting company claims that autonomous automobiles might avert up to 90% of road accidents.

Artificial intelligence security is rapidly growing as a topic of cybersecurity study.

Militaries all around the globe are working on prototypes of dangerous autonomous weapons systems.

Automatic weapons, such as drones, that now rely on a human operator to make deadly force judgments against targets, might be replaced with automated systems that make life and death decisions.

Robotic decision-makers on the battlefield may one day outperform humans in extracting patterns from the fog of war and reacting quickly and logically to novel or challenging circumstances.

High technology is becoming more and more important in modern civilization, yet it is also becoming more fragile and prone to failure.

An inquisitive squirrel caused the NASDAQ's primary computer to collapse in 1987, bringing one of the world's major stock exchanges to its knees.

In another example, the ozone hole above Antarctica was not discovered for years because exceptionally low levels reported in data-processed satellite images were assumed to be mistakes.

It's likely that the complexity of autonomous systems, as well as society's reliance on them under quickly changing circumstances, will make completely tested AI unachievable.

Artificial intelligence is powered by software that can adapt to and interact with its surroundings and users.

Changes in variables, individual acts, or events may have unanticipated and even disastrous consequences.

One of the dark secrets of sophisticated artificial intelligence is that it is based on mathematical approaches and deep learning algorithms that are so complicated that even its creators are baffled as to how it makes accurate conclusions.

Autonomous cars, for example, depend on exclusively computer-written instructions while they watch people driving in real-world situations.

But how can a self-driving automobile learn to anticipate the unexpected?

Will attempts to adjust AI-generated code to decrease apparent faults, omissions, and impenetrability lessen the likelihood of unintended negative consequences, or will they merely magnify existing problems and produce new ones? Although it is unclear how to mitigate the risks of artificial intelligence, it is likely that society will rely on well-established and presumably trustworthy machine-learning systems to automatically provide rationales for their actions, as well as examine newly developed cognitive computing systems on our behalf.

~ Jai Krishna Ponnappan

You may also want to read more about Artificial Intelligence here.

Also see: Algorithmic Error and Bias; Autonomy and Complacency; Beneficial AI, Asilo mar Meeting on; Campaign to Stop Killer Robots; Driverless Vehicles and Liability; Explainable AI; Product Liability and AI; Trolley Problem.

Further Reading

De Visser, Ewart Jan. 2012. “The World Is Not Enough: Trust in Cognitive Agents.” Ph.D. diss., George Mason University.

Forester, Tom, and Perry Morrison. 1990. “Computer Unreliability and Social Vulnerability.” Futures 22, no. 5 (June): 462–74.

Lee, John D., and Katrina A. See. 2004. “Trust in Automation: Designing for Appropriate Reliance.” Human Factors 46, no. 1 (Spring): 50–80.

Yudkowsky, Eliezer. 2008. “Artificial Intelligence as a Positive and Negative Factor in Global Risk.” In Global Catastrophic Risks, edited by Nick Bostrom and Milan 

M. Ćirković, 308–45. New York: Oxford University Press.

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