CoChat
Sign Up
Cited by 26Digitalization, Law, and RegulationEthics and Social Impacts of AIComparative International Legal Studies

In support of “no-fault” civil liability rules for artificial intelligence

Open access
Marchisio, Emiliano·2021·SN Social Sciences
DOI 10.1007/s43545-020-00043-zPMC PMC7851658
Open this paper in CoChat — free accountOpen PDF

Abstract

Civil liability is traditionally understood as indirect market regulation, since the risk of incurring liability for damages gives incentives to invest in safety. Such an approach, however, is inappropriate in the markets of artificial intelligence devices. In fact, according to the current paradigm of civil liability, compensation is allowed only to the extent that “someone” is identified as a debtor. However, in many cases it would not be useful to impose the obligation to pay such compensation to producers and programmers: the algorithms, in fact, can “behave” far independently from the instructions initially provided by programmers so that they can err despite no flaw in design or implementation. Therefore, application of “traditional” civil liability to AI may represent a disincentive to new technologies based on artificial intelligence. This is why I think artificial intelligence requires that the law evolves, on this matter, from an issue of civil liability into one of financial management of losses. No-fault redress schemes could be an interesting and worthy regulatory strategy in order to enable this evolution. Of course, such schemes should apply only in cases where there is no evidence that producers and programmers have acted under conditions of negligence, imprudence or unskillfulness and their activity is adequately compliant with scientifically validated standards.

Verifying this paper

8 of 9 steps done · this page updates itself — you can leave and come back

  1. Registry recordVerified
  2. Full textRead the full text
  3. Reference list103 references found
  4. Reference checkChecking each cited work — 105 of 105
  5. StatisticsRecomputed
  6. Claims88 claims anchored to a quote
  7. Cited sourcesChecked
  8. Self-checkChecked
  9. VerificationFindings verified

Integrity signals

Registry

Crossref · 10.1007/s43545-020-00043-z

Cites

104 of 105 references verified1 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

6 of 64 cited sources checked · 5 supported · 0 flagged · accuracy not yet measured

  • Premisecitation

    The reference to civil medical liability is appropriate when it comes to tort law reform in the wake of artificial intelligence because the two systems show similarities in both incentives and (negative) externalities.

    “It should be noted that the reference to civil medical liability, when it comes to tort law reform in the wake of artificial intelligence, appears appropriate as the two systems show similarities in both incentives and (negative) externalities (Gaine 2003).”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Gaine 2003 source

      The discussion of the current tort system says litigation is presented as a deterrent to substandard care, while its adversarial, blame-oriented nature inhibits openness. The paper also describes the system as costly and prone to delay. It does not discuss artificial intelligence or establish that AI-related liability has similar incentives and negative externalities. (LLM, conf=0.84)

      Experimental — accuracy not yet measured
  • Premisecitation

    Pure no-fault models raise concerns about their appropriateness to limit the risk of moral hazard, as the principal weakness of no-fault schemes is the difficulty of ensuring that the socially optimal amount of care is taken by potential loss-causers.

    “the principal weakness of no-fault schemes is the difficulty of ensuring that the socially optimal amount of care is taken by potential loss-causers, as the links between their potential to cause loss and the costs of their actions are severed”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Howell et al. 2002 source
  • Interpretationreasoning

    AI algorithms can err or produce unpredictable results despite no flaw in design or implementation.

    “they can err despite no flaw in design or implementation.”
    • Also stated in: § Introductory remarks · plain
      “with negative consequences despite no flaw in design or implementation”
    • Also stated in: § AI and civil liability: the problem(s) · hedged
      “The results of the AI activity, therefore, could be unpredictable despite the absence of flaws in the design or implementation.”
    Quote in the paperSubstantial claim
  • Premisecitation

    The paradigm of civil liability based on deterrence has proven reliable and appropriate in several cases, including general consumer legislation.

    “It should be noted that the paradigm of civil liability based on deterrence has proven to be reliable and appropriate in several cases.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Council Directive 85/374/EEC

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Premisecitation

    The current paradigm of civil liability laws is primarily based on the assumption that civil liability plays and should play an important role in deterrence.

    “The current paradigm of civil liability laws is primarily based on the assumption that civil liability plays and should play an important role in deterrence.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Calabresi 1970

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Cooter and Ulen 2008

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Viscusi and Hersh 2013

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim. 35
  • May combine two claims. 18
  • The quote only partly states this claim. 1

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The European Parliament proposed creating a specific legal status for robots, so that at least the most sophisticated autonomous robots could be established as having the status of electronic persons with specific rights and obligations.

    “This possibility led the European Parliament to propose”
    Quote in the paperNot a substantial claim1 of 3 sources checked
    • cites European Parliament 2016

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Solaiman 2017 source
    • cites Bryson et al. 2017 source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Seat belts can cause injuries but it is vastly more likely that they will protect you.

    “seat belts can cause injuries but it is vastly more likely that they will protect you”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Giubilini and Savulescu 2019 source

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisecitation

    No-fault legislation is currently showing shortcomings in terms of safety incentives, in the absence of the deterrent brought about by traditional civil liability.

    “no-fault”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Howell et al. 2002 source

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The principal weakness of no-fault schemes is the difficulty of ensuring that the socially optimal amount of care is taken by potential loss-causers, as the links between their potential to cause loss and the costs of their actions are severed.

    “Since”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites Howell et al. (2002) source

  • May combine two claims.

    Premisecitation

    Current AI algorithms are not limited to executing tasks based on predefined and permanent rules; they are able to collect data and self-learn.

    “Current AI algorithms are not limited to executing tasks based on predefined and permanent rules.They are able to collect data (the so-called data mining: Friedman 1998) and self-learning.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Friedman 1998

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Mitchell 1997

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Koza et al. 1996 source

  • May combine two claims.

    Premisecitation

    AI algorithms can automatically improve through experience and become capable of making predictions and decisions they were not explicitly programmed for.

    “In particular, algorithms can automatically improve through experience and become capable of making predictions and decisions they were not explicitly programmed for (Mitchell 1997; Koza et al. 1996).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Mitchell 1997

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Koza et al. 1996 source

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The proposal to release producers and programmers from liability when they comply with standards is in stark contrast with the current paradigm of allocation of the costs of accidents, which tends to impose strict liability on firms carrying out intrinsically risky activities.

    “It should be noted that this proposal is in stark contrast with the current paradigm of allocation of the”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Provision of incentives for technological innovation, provided that it respects scientifically validated standards, appears a safer strategy than any other.

    “This means that provision of incentives for technological innovation, provided that it respect scientifically validated standards, appears a safer strategy than any other.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Adverse effects of vaccination are very rare compared to the over 2.5 million deaths prevented, only in 2008, by vaccination.

    “adverse effects are very rare compared to the over 2.5 million deaths prevented, only in 2008, by vaccination”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites World Health Organisation 2009

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Looker and Kelly 2011 source

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

    Interpretationreasoning

    It would be necessary to define a third, independent entity in charge of paying compensation to damaged end-users under a no-fault scheme, its operation and financing.

    “It would also be necessary to define a third, independent entity in charge of paying compensation to damaged end-users in application of the”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: A third, independent entity can be defined and financed to pay compensation to damaged end-users under a no-fault scheme.

  • May combine two claims.

    Premisecitation

    New technologies determine an important increase in safety and reduce the overall number and relevance of accidents, as available data show.

    “This would be a significant negative externality, since new technologies determine an important increase in safety and reduce the overall number and relevance of accidents (as available data already show with respect to the current situation 20 ).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites US Department of Transportation, NHTSA (2017)

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites US Department of Transportation (2018)

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Premisenone given

    The only no-fault scheme that shares a common approach to the scope discussed in the paper is that provided for injuries as a result of vaccination.

    “the only”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    In the USA any such scheme would likely be funded privately, while in European countries such as Sweden, Norway and Finland it is more likely to be publicly funded.

    “in the USA any such scheme would likely be funded privately while in European countries such as Sweden, Norway and Finland it is more likely to be publicly funded”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites OECD 2006 source
    • cites Mello et al. 2011
    • cites Dickson et al. 2016

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Premisenone given

    The vaccination injury no-fault scheme embodies the idea that compensation of statistically inevitable injuries should not be imposed on persons who carry out the relevant activities or supply products, to the extent that negligence, imprudence or unskillfulness is not proven and scientifically validated standards are complied with.

    “This scheme, in fact, embodies the idea that compensation of statistically”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Adopting a no-fault scheme would help resolve other weaknesses inherent in the traditional paradigm of civil liability.

    “It would also help resolve other weaknesses inherent in the traditional paradigm of civil liability.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: A no-fault scheme for AI can be designed that maintains compensation for damaged end-users while shifting liability away from producers and programmers.

  • May combine two claims.

    Interpretationreasoning

    Civil liability would not induce virtuous investments in safety within the AI industry because no further investment, fostered by deterrence, could prevent such kind of risks.

    “This is why, in my view, civil liability would (and could) not induce virtuous investments in safety within the AI industry: in fact, no further investment, fostered by deterrence, could prevent such kind of risks.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Under the current paradigm of civil liability, compensation is allowed only if “someone” is identified as a debtor (through fault or under a strict liability rule).

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning

    Condemning a producer to pay compensation for a specific damage suffered by an end-user of AI devices or robots, despite compliance with validated standards and no negligence, imprudence or unskillfulness being ascertained in court, can possibly damage systemic safety if it prevents the market from developing into a more technological and safer system.

    “condemning a producer to pay compensation for a specific damage suffered by an end-user of AI devices or robots, despite compliance with validated standards and no negligence, imprudence or unskillfulness being ascertained in court) can possibly damage systemic safety”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationcitation

    Compliance with predetermined standards constitutes an adapted range of duties of care in the AI environment and represents a more effective form of regulation within mass products.

    “This compliance constitutes, in the AI environment, a sort of”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites European Commission 2019

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Viscusi 1989

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    No-fault schemes differ with respect to six main variables: eligibility criteria for compensation; whether compensation is paid automatically or an avoidability standard is adopted; whether the system prevents continuous access to the courts; how the program is funded; whether compensation is imposed a financial cap; and the definition of the financial entitlement.

    “no-fault”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Dickson et al. 2016

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Premisecitation

    AI makes it questionable whether existing liability rules based on an "anthropocentric and monocausal model of inflicting harm" are adequate.

    “As stated by the EU Expert Group on Liability and New Technologies, AI makes it questionable the adequacy of existing liability rules based on “anthropocentric and monocausal model of inflicting harm” (European Commission 2019).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites European Commission 2019

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Interpretationcitation

    The European Parliament's proposal shows the need to shift obligations away from producers and programmers when robots are capable of acting rather autonomously from their original design.

    “However, this proposal is much relevant within the present discussion, because it clearly shows the need to shift”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Scherer 2016

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    Removing the incentives for AI would mean reducing overall efficiency, safety and security.

    “removing the incentives for AI would mean reducing overall efficiency, safety and security.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    Drafting a no-fault scheme for damages produced by AI algorithms would require a careful definition of the eligibility criteria, especially the definition of scientifically validated standards and modification procedures.

    “the drafting of a”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Scientifically validated standards for AI production and programming can be defined and complied with.

  • May combine two claims.

    Interpretationreasoning

    Application of the traditional paradigm of civil liability to AI might not foster significant improvements of safety and could determine negative externalities.

    “Application of the traditional paradigm of civil liability to AI, however, might not foster significant improvements of safety and could determine negative externalities, instead.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Under the current paradigm of civil liability, compensation is allowed only if “someone” is identified as a debtor (through fault or under a strict liability rule).

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisereasoning

    The aforementioned pieces of no-fault legislation have a much narrower scope than the issues dealt with in the paper.

    “the aforementioned pieces of legislation have a much narrower scope than the issues dealt with here”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    Producers and programmers could not do much to forecast unforeseeable "behaviour" of AI algorithms, which would be influenced by innumerable variables provided by databases, big data gathering and the end-users themselves, which are completely out of the reach and control of anyone.

    “However, producers and programmers could not do much to forecast unforeseeable “behaviour” of AI algorithms, which would be influenced by innumerable variables provided by databases, big data gathering and the end-users themselves, which are completely out of the reach and control of anyone.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Deep learning-based image recognition is currently able to achieve more accurate results than human-based ones.

    “Deep learning-based image recognition is currently able to achieve more accurate results than human-based ones (Cireşan et al. 2012).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Cireşan et al. 2012 source

  • May combine two claims.

    Interpretationreasoning

    Under a mandatory insurance regime, producers and programmers would remain personally liable, may be called to pay redress if insurance coverage is not applicable, and would be subject to deterrence indirectly because insurers would shift the cost of redress onto them through higher premiums.

    “producers and programmers would remain personally liable, may be called to pay redress in case insurance coverage is not applicable and would be subject to deterrence indirectly anyway, since insurers would shift onto producers and programmers (by applying higher insurance premiums) the cost of any redress paid on their behalf.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Artificial intelligence shows, in many sectors, a more secure strategy than any other based on human action.

    “artificial intelligence shows, in many sectors, a more secure strategy than any other based on human action.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Adopting a no-fault scheme would isolate compensation in favour of damaged end-users from liability on producers and programmers of AI devices.

    “Adopting a”
    Quote in the paperSubstantial claim
    • Rests on an assumption: A no-fault scheme for AI can be designed that maintains compensation for damaged end-users while shifting liability away from producers and programmers.

  • May combine two claims.

    Premisecitation

    The adoption of artificial intelligence in carrying out specific activities such as driving determines a significant increase in safety and reduces the overall number and relevance of damage and deaths compared to human action.

    “the adoption of artificial intelligence in carrying out specific activities such as driving (being destined to increase drastically in the next future) determines a significant increase in safety and reduces the overall number and relevance of damage and deaths compared to human action.”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Kizer and Blum (2005)

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Sigmoidal (2017)

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Hernandez (2014)

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The main reason for the European Parliament's proposal is to use legal personality as a technique to impute liability to the robot alone and isolate its obligations, including damages, from those of its producer and programmer.

    “The main reason for this proposal is to use legal personality as a technique to impute liability to the robot alone and, therefore, isolate its obligations (including damages) from those of its producer and programmer.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    All existing pieces of no-fault legislation are targeted to specific sectors and, when implemented with reference to AI, should be properly adjusted.

    “all the existing pieces of”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Existing no-fault legislation can be properly adjusted to be implemented with reference to AI.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    A new paradigm could be built on the basis of no-fault systems available in different jurisdictions.

    “This new paradigm could be built on the basis of”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: A no-fault scheme for AI can be designed that maintains compensation for damaged end-users while shifting liability away from producers and programmers.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given

    Deterrence exists in all legal systems, even if extension and intensity may vary.

    “This is true in all legal systems, even if extension and intensity of such deterrence may vary relevantly.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    In these cases, civil liability would provide no virtuous deterrence to utmost care, but would simply discourage technological progress.

    “In these cases, civil liability would provide no virtuous deterrence to utmost care, but would simply discourage technological progress.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    The intensive use of artificial intelligence in several sectors is very likely to reduce overall risks and harm compared to human action.

    “the intensive use of artificial intelligence in several sectors is very likely to reduce overall risks and harm compared to human action.”
    Quote in the paperNot a substantial claim

  • May combine two claims. The quote only partly states this claim.

    Interpretationreasoning

    Traditional civil liability rules can provide a negative incentive towards technological evolution, as they can impose the obligation to pay compensation on producers and programmers of AI devices despite no design or implementation flaws.

    “traditional”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationnone given

    In all cases in which artificial intelligence is more reliable than human work, a no-fault system would prevent the use of new technologies from being hindered by compensation rules based on an unfair allocation of costs.

    “On the contrary, in all cases in which artificial intelligence is more reliable than human work, it would prevent the use of new technologies from being hindered by compensation rules based on an unfair allocation of costs.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Outdated liability rules discourage the development or use of artificial intelligence.
    • Rests on an assumption: Compensation rules based on an unfair allocation of costs hinder the use of new technologies when artificial intelligence is more reliable than human work.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The introduction of a no-fault system would not determine a particular incentive to adopt artificial intelligence at the expense of human work.

    “The introduction of a no-fault system, therefore, would not determine a particular incentive to adopt artificial intelligence at the expense of human work.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Outdated liability rules discourage the development or use of artificial intelligence.

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    The risks associated with machine-learning and deep-learning capacity of artificial intelligence devices consist of the AI algorithms' ability to act in a rather autonomous way from their original design.

    “the risks associated with machine-learning and deep-learning capacity of artificial intelligence devices, consisting of the AI algorithms ability to act in a rather autonomous way from their original design.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    AI creates new challenges with regard to civil liability, which must balance adequate compensation to victims with the need not to hinder technological innovation.

    “AI creates new challenges with regard to civil liability, which must balance adequate compensation to victims with the need not to hinder technological innovation”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites EU Commission 2020

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Premisereasoning

    In other cases, traditional civil liability rules would have a valid deterrent function.

    “In other cases, traditional civil liability rules would have a valid deterrent function.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning

    From a systemic point of view, the overall benefits of artificial intelligence outweigh the resulting costs.

    “From a systemic point of view, the overall benefits of artificial intelligence outweigh the resulting costs.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Risks depend upon the intrinsic complexity of products, of markets, of the technological development itself.

    “In these cases, risks depend upon the intrinsic complexity of products, of markets, of the technological development itself:Reason (1990).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Reason (1990) source

  • Classified as probably not a substantial claim.

    Premisecitation

    Robots cannot be considered, in legal language, as Personen.

    “Since robots cannot be considered, in legal language, as Personen.See, from the very title: Wagner (2019a, b).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Wagner (2019a, b)

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

46 of 46

  • The paper relies on this without support.

    Under the current paradigm of civil liability, compensation is allowed only if “someone” is identified as a debtor (through fault or under a strict liability rule).

    “according to the current paradigm of civil liability, compensation is allowed only to the extent that “someone” is identified as a debtor.”
    “according to the current paradigm of civil liability based on deterrence, compensation is allowed only to the extent that “someone” is identified as a debtor (either through fault or under a strict liability rule).”

  • The paper relies on this without support.

    Scientifically validated standards for AI production and programming can be defined and complied with.

    “My proposal requires, of course, solution of many variables, such as the definition of what”

  • The paper relies on this without support.

    A third, independent entity can be defined and financed to pay compensation to damaged end-users under a no-fault scheme.

    “It would also be necessary to define a third, independent entity in charge of paying compensation to damaged end-users in application of the”

  • The paper relies on this without support.

    A no-fault scheme for AI can be designed that maintains compensation for damaged end-users while shifting liability away from producers and programmers.

    “This is why I believe that a new regulation of the matter should be developed, inspired by a new paradigm, aimed at maintaining compensation for damages on the patient's side, but shifting away from producers and programmers of AI devices”

  • The paper relies on this without support.

    Existing no-fault legislation can be properly adjusted to be implemented with reference to AI.

    “all the existing pieces of”

  • The paper relies on this without support.

    Outdated liability rules discourage the development or use of artificial intelligence.

    “It should be noted, in the first place, that the proposal I have developed in this article is not aimed at artificially increasing the growth of artificial intelligence but, on the contrary, is aimed at preventing it from being discouraged by outdated liability rules.”

  • The paper relies on this without support.

    Compensation rules based on an unfair allocation of costs hinder the use of new technologies when artificial intelligence is more reliable than human work.

    “On the contrary, in all cases in which artificial intelligence is more reliable than human work, it would prevent the use of new technologies from being hindered by compensation rules based on an unfair allocation of costs.”
7 of 7

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Current fault-based standards already attach responsibilities to people based on special relationships they may have with an object, such as where the person is in control of a potentially dangerous animal or thing.

    “Current fault-based standards already attach responsibilities to people based on special relationships they may have with an object, such as where the person is in control of a potentially dangerous animal or thing (Marchisio, 2021).”

    From Liability for harm caused by AI in healthcare: an overview of the core legal concepts (2023) · cites this paper as Marchisio, 2021

    The Introductory remarks (page unavailable) say that civil liability is primarily based on fault. The ‘AI and civil liability: the problem(s)’ section (page unavailable) mentions proposals to extend rules concerning animals under human care, but does not say that current fault-based standards impose liability because a person controls a dangerous animal or thing. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Defendants who are penalised are incentivised to prevent harm in the future.

    “Defendants who are penalised are incentivised to prevent harm in the future (Marchisio, 2021).”

    From Liability for harm caused by AI in healthcare: an overview of the core legal concepts (2023) · cites this paper as Marchisio, 2021

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Florida and Virginia injury-compensation statutes are swapped

    Minor

    The text introduces the Virginia statute as a reference for Florida, then introduces the Florida statute as a reference for Virginia.

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 20

  1. Amidei A (2017) Roboticaintelligente e responsabilità: profili e prospettiveevolutivedelquadronormativoeuropeo. In: Ruffolo U (ed) Intelligenzaartificiale e responsabilità. Milano, Giuffrè, pp 63–106
  2. Comporti M (1965) Esposizione al pericolo e responsabilitàcivile. Morano, Napoli
  3. Cooter R, Ulen T (2008) Law & economics, V. Pearson/Addison Wesley, Boston
  4. Costantino M, Coletti P (2008) Information extraction in finance. Wit Press, Southampton
  5. De Cupis A (1979) Ildanno: teoriageneraledellaresponsabilitàcivile. Milano, Giuffrè
  6. Dickson K, Hinds K, Burchett H, Brunton G, Stansfield C, Thomas J (2016) No-fault compensation schemes: a rapid realist review, London, EPPI-Centre, Social Science Research Unit, UCL Institute of Education, University College London
  7. Hubbard FP (2014) Sophisticated robots: balancing liability, regulation and innovation, in Fla. Law Rev 66:1803–1872
  8. IEEE Standards Association (2017) Ethically aligned design, version 2. https://standards.ieee.org/news/2017/ead_v2.html. Accessed 10 Nov 2020
  9. Kizer KW, Blum LN (2005) Safe practices for better health care. In: Henriksen K, Battles JB, Marks ES (eds) Agency for Healthcare Research and Quality (US), Rockville (MD), Advances in patient safety: from research to implementation, vol IV, programs, tools, and products. https://www.ncbi.nlm.nih.gov/books/NBK20613/. Accessed 10 Nov 2020
  10. Luntz H (2010) Torts and insurance: the effect on deterrence (conference paper). https://perma.cc/GH6A-2JG9. Accessed 10 Nov 2020
  11. Mitchell T (1997) Machine learning. McGraw Hill, New York
  12. Montagnani ML, Cavallo M (2020) Liability and emerging digital technologies: an EU perspective. https://www.academia.edu/43696325/Liability_and_emerging_digital_technologies_an_EU_perspective. Accessed 10 Nov 2020
  13. Montinaro R (2012) Dubbioscientifico e responsabilitàcivile. Milano, Giuffrè
  14. Ruffolo U (2017) Per Ifondamenti di un dirittodellarobotica self-learning; dalla machinery produttivaall’auto driverless: verso una “responsabilità da algoritmo”? In: Ruffolo U (ed) Intelligenzaartificiale e responsabilità. Milano, Giuffrè, pp 1–30
  15. Sperl H (1902) Über das SchadenersatzrechtnachdemdeutschenbürgerlichenGesetzbuche. ManzVerlag, Wien
  16. Surden H (2014) Machine learning and law. Wash law rev 89:87–115
  17. Virk GS (2013) The role of standardisation in the regulation of robotic technologies. In: Palmerini E, Stradella E (eds) Law and Technology. The challenge of regulating technological development. Pisa University Press, Pisa, pp 311–334
  18. von Jhering R (1867) Das ShuldmomentimrőmischenPrivatrecht. Giessen, Brühl
  19. Wagner G (2019a) RoboteralsHaftungssubjekte? KontureneinesHaftungsrechtsfürautonomeSysteme. In: Faust F, Schäfer H-B (eds) Zivilrechtliche und rechtsökonomischeProbleme des Internets und der künstlichenIntelligenz. Tübingen, Mohr Siebeck, pp 1–40
  20. Wagner G Robot liability (2018) Münster Colloquium on EU Law and Digital Economy, Liability for Robotics and the Internet of Things 12.3.2018. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3198764. Accessed 10 Nov 2020

Cited by

Sorted by citations

Related

Authors
Marchisio, E.
Affiliations
Giustino Fortunato University (IT)
Corresponding author
Marchisio, Emiliano
ORCID
Marchisio, Emiliano
Venue
SN Social Sciences
Publisher
Springer Science and Business Media LLC
Published
2021-01-11
Volume
1
Issue
2
Pages
54-54
ISSN
2662-9283 (ISSN-L)
DOI
10.1007/s43545-020-00043-z
PMC
PMC7851658
OpenAlex
W3126710572
Cited by
25
References
103
Citations by year
2026: 2 · 2025: 4 · 2024: 7 · 2023: 4 · 2022: 5 · 2021: 3
FWCI
7.03
Type
journal article
Language
en
Open access
Licence
Locations
  • SN Social Sciences (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Political Science and International Relations › Comparative International Legal Studies
Keywords
no-fault liability, civil liability, artificial intelligence, AI safety, algorithmic errors, negligence, AI regulation, compensation for damages

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-11.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-11.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review