Civil Liability in the Age of Artificial Intelligence Assignment Sample Examining Legal Responsibilities Emerging AI Challenges and Analysis of Modern Liability Issues.
The term AI or artificial intelligence is an umbrella term which refers to an ability, task, achievement or process that will be completed or accomplished by a computer machine without the need for human input. The study of Civil Liability in the Age of Artificial Intelligence explores the legal responsibilities arising from such autonomous systems. This development has its advantages but at the same time, it poses legal issues. A key issue that arises with the increased use of AI-powered tools is the question: who is liable? Present negligence laws are in connection with human action, intention, and control. However, AI operates differently. It is data-based, self-employing, and can carry out sudden actions. This leads to some problems in the process of applying the legal rules. This dissertation aims at finding out whether current negligence laws are adequate enough to address AI related cases. This paper aims to investigate the question of how it is possible to assign legal responsibility when an AI system is at fault. Nevertheless, this should be held responsible – the owner, developer or the user. These questions make the question of AI liability a more intricate question.
It must be noted that some opinions state that current laws can accommodate even the cases of AI. Others prefer that some new regulations should be implemented. Current court rules do not seem applicable when it comes to AI, as courts have managed to adjust to previous technological advancements. In this paper, the author analyzes AI liability and how it relates to previous legal issues and can be amended. Through it, it will be possible to review judiciary decisions, scholars’ comments, and policy recommendations.
Through these discoveries this dissertation seeks to find out if the current laws are sufficient or the governments need to come up with new laws. For legal policymakers, business entities, and users of AI, the equities and consequences available from the findings will be valuable and beneficial.
Assignment samples are provided to explain coursework requirements and core concepts. With out uk assignment support, guidance is shared while ensuring originality. The Civil Liability in the Age of Artificial Intelligence Assignment Sample explores negligence law, AI liability, comparative legal approaches for regulatory reform in modern AI applications. These materials are intended solely for study and reference purposes.
Negligence is a legal remedy in civil wrongs denoting failure to take appropriate care in handling something causing harm to others. It is perhaps one of the leading causes of lawsuits especially in product liability lawsuits, and medical malpractice. Negligence is the failure to exercise that amount of care that is required by and of a reasonable man which leads to causing some harm to another. Tort law of negligence in the community aims at preventing avoidable risks. It can also be used in many fields such as in medical malpractice, on the roads, and in workplace injuries.
In the negligence cases, three elements that need to be established by a claimant are:
Negligence is a legal term that can best be described as a civil wrong, as well as a failure to take proper care resulting in someone else’s loss or harm in as far as the common law is concerned. Courts decide on the negligence of a person or an organization based on the law that applies to such a case as well as previous cases that have been heard and determined by other courts of law. It is for this reason that negligence law is under pressure given these technological trends with particular reference to artificial intelligence (AI)[1]. Some of these principles do not adequately cover AI harm for traditional AVMs. Part one of the chapter examines the groundwork of negligence to provide an understanding of how it ties to the current issues.
Tort law specifically negligence law has been developed through case laws over the years on a case by case basis with regards to civil liability. Previously, liability only extended to contractual scenarios whereby a person had to turn to the tortfeasor and claim damages when that person had a contractual deal with the latter. But as the population grew and people became involved in more complicated business and transactions, it became important for courts to award damages for failing to act as a reasonable man even though no agreement had been made.
This case has remained one of the most prominent cases of negligence law that was tried in the UK’s house of lords at AC 562 in 1932. This case also marked the beginning of the development of the modern principle of the duty of care that is owed by the defendant. In this particular case, May Donoghue consumed ginger beer which she had purchased from a café owned by Donald Davidson. One night she had drunk a part of it to find a decomposed snail within the bottle which caused her to get sick. Since she had not bought the drink herself, she could not claim under the contract law. As a result of this, she resorted to filing a negligence case against the manufacturing company.
The position stated that the House of Lords ruled in her favor where Lord Atkin the case introduced what was to refer to as the ‘neighbour principle’. He said that individuals have to exercise caution so as not to cause harm or not to fail to prevent harm to their neighbors or those who could be easily impacted by the actions of the individuals[2]. This principle brought inclusiveness in negligence law by going further to define that negligence liability had the potential to go beyond contractual law. This case provided reference on holdings and elements that manufactures, and service providers have the responsibility to ensure that their products do not make clients remain harmed.
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After that, negligence law was advanced much further in the case of Caparo Industries Plc v Dickman [1990] UKHL 2. This case brought into the somewhat more rigid test of defining when exactly duty of care arises. The three questions stated under the Caparo test are:
This test is still used to this present day in determining negligence liability of an individual or organization. It is used in cases of workplace accidents, road accidents, medical malpractice among others. To respond to various and more sorted aspects of negligence, other tests have been developed over the years to cater for professional negligence, pure economic loss among others.
However, the breakthrough of AI brings new issues on negligence law. It is still hard to establish foreseeability and proximity issues when organizations unleash the AI systems to run without human intervention. Negligence law has a long history and still has a powerful base, but to some extent, the courts can and may have to develop new rules that would adequately address the topic of AI harm.
The common facets that are always required in negligence action include: Duty of care, breach of duty and causation. The elements are used to assess whether a particular defendant's handling or lack of handling a situation caused harm to someone and if there exists a remedy for such an act.
Duty of Care
The Defendant must have owed a legal duty of care to the Defendant. A duty of care means that it is incumbent on a person to act in a reasonable manner to avoid loss that is reasonably foreseeable. It makes sure that actors and entities do not harm others and instead take the necessary precautions in any decision they make. In deciding whether an actor owes a duty of care the following principles are applied:
The principle of fairness in this context was raised in the case of Donoghue v Stevenson [1932] AC 562, where the court held that manufacturers owe the consumers a duty of care. This principle has been used in several areas of practice as in Employees’ Compensation, Professional Practices, and Public Risk [3]. The Occupiers Liability Act of 1957 has since codified the principle.
Breach of Duty
Breach of duty occurs when an individual or company is under a legal obligation to meet a standard of care and fails to do so. This is determined by looking at what another person would have done in similar circumstances on behalf of the defendant. This concept is defined in Blyth v Birmingham Waterworks Co [1856] 11 Ex Ch 781 whereby negligence is defined as the lack of duty of care that a reasonable man is expected to discharge.
There is therefore a higher degree of care expected in professional practice. In Bolam v Friern Hospital Management Committee [1957] 1 WLR 582 the doctors in the society held that the standard of the treatment was the applicable law. Basically speaking, admitting harm and not following those procedures constitute negligence for a medical professional. Other professions which fall under this principle are legal professionals, engineers and the financial advisors for they must meet the expected standards of the profession.
Causation and Damage
For a negligence claim, a claimant must demonstrate that negligence caused harm under the law, it is said that the mere fact that harm occurred is not sufficient and instead, must be caused as a result of the breach of duty of care. This method is known as the but for test, which need only be satisfied as per the decision in Barnett v Chelsea & Kensington Hospital Management Committee [1969] 1 QB 428.
Moreover, there are two categories of causation; factual causation, whereby, except for hypersomnia, which was not reasonably foreseeable, all other incidents occurred and, legal causation, whereby, the damage was foreseeable [4]. As held in The Wagon Mound (No 1) [1961] AC 388, a defendant is only responsible for loss which could have reasonably been foreseen.
Negligence and AI
These principles apply to cases of negligence and while applying these principles in negligence in relation to artificial intelligence (AI), it involves some considerations. Due to the fact that AI lacks intent, foreseeability, and the capability to be directly controlled by a human mind, it becomes complicated to prove duty, breach, or even causation. Subsequently, the courts must consider whether traditional concepts of negligence encompass the potential liabilities within the application of AI or if completely new legal frameworks should be developed.
The law of negligence is no longer limited to the conventional tort cases where it is seen to prosper across all fields of law. With time, other issues that emerge in consumer protection, professional negligence law, new technologies and innovations, environment, and contracts from an international perspective. Negligence laws remain functioning and evolving in courts as well as law making systems with the aim of balancing justice and fairness.
Negligence in Consumer Protection
Negligence, as it will be pointed out, has a significant role to play in ensuring consumers do not fall victim to either faulty goods or misleading commercial activities. It is the responsibility of the companies to ensure that their products are safe and as well meets requirements of the regulation standards. In case of harm, manufacturers or sellers of a product can be held accountable for negligence in the production of a particular product. The use of Consumer protection Act 1987 of UK can be beneficial in this.
For instance, in the case of Donoghue v Stevenson (1932), the court concluded that manufacturers always have the responsibility of ensuring that consumers are protected from harm. It holds true to this date particularly in product liability or in any situation where there is a contest between the manufacturer of complex products and consumers. So, one more reason why businesses must not lie about their products is that the business has to make sure that other parties concerned are well informed of the truth about the product these firms are selling [5]. Non-disclosure or misleading information to the consumers makes them sue the producers for negligence. Laws regarding consumer protection aim at protecting people from being exploited and ensure that companies will be made responsible for what they do.
Negligence in Professional Liability
Experts have the legal responsibility to discharge their mandate with reasonable skill and competency. They make themselves liable to being sued for negligence in case they do not meet the industry standards. This applies to doctors, lawyers, accountants, financial advisors, self-employed and all civil servants inclusive.
For instance in the case of Bolam v Friern Hospital Management Committee in the same year of 1957, the court held that the medical practices adopted by the practitioners needed to be reasonable by way of meeting some necessary standard. If they fail to do so, and harm is as a result realized, they will be held responsible for the actions. The same may be said of other occupations. Credible lawyers may find themselves being sued for negligence especially if the information they offered was misleading while accurate accountants may be sued for negligence if their calculations were incorrect. Therefore, it can be said that Consumer Protection Act 1987 (UK) can be useful in this case.
Negligence in Emerging Technologies
One of the issues arising from new technologies include use of artificial intelligence and self-driving cars. Responsibility for damages caused by the AI system is hard to place since the system operates with autonomy. For instance, when an accident is occasioned by an autonomous car, it should be referred to the manufacturer, Silicon Valley software engineers. In this case, negligence laws are applicable and it becomes the function of the courts to determine how they apply. Critics however have a reason to say that there exists a need for special laws that regulate AI.
Negligence in Environmental Law
Negligence of Companies that cause harm to the environment could be attributed mostly to. Environmental laws describe measures one needs to undertake in the business activities to avoid negative impacts on people and the natural environment [6]. For instance, in a case of Cambridge Water Co v Eastern Counties Leather (1994), the accused company contaminated water and was held liable for negligence. Judicial systems tend to evaluate the efforts made by corporations to minimize pollution in their processes. Tort law also has the ability through negligence to sanction corporate documentation and hence contribute to preventing environmental degradation.
Conclusion
This chapter aims at introducing the theoretical frameworks of negligence law, in the following pages we will look at the following topics. Negligence is one of the exemptions of civil liability. It therefore calls for establishing the existence of duty of care, the breach of that duty and causation. The significant facts about the evolution of negligence were mentioned. The rule which was Donoghue v Stevenson (1932) established and brought a new principle known as the neighbor principle whereby manufacturers are under an obligation to protect consumers. Later, Caparo Industries v Dickman (1990) refined negligence with a three-stage test for duty of care. Judicial rationale, courts apply this test to determine parties’ negligence.
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On this subject, the chapter also explained what negligence comprises. Negligent means failure to take reasonable care to avoid harm in carrying out one’s business or executing a contractual responsibility. In this sense of the word, a breach is used to refer to a situation in which one has been comprehended to have farted from expectations. Courts compare actions to a reasonable person’s behavior. Where a person is carrying out professional duties then Bolam v Friern Hospital (1957) states that the standard to be expected is much higher. The effect is ascertained by using the but for test which comes from the case of Barnett v Chelsea & Kensington Hospital (1969). Consequently, the law’s approach to legal causation asks whether harm was reasonably foreseeable as in The Wagon Mound case in 1961.
Last but not the least, the chapter discussed the issues arising with the application of negligence law to AI. Because of the absence of intent, the liability determinations differ when dealing with AI. This poses the issues regarding the sufficient protection offered by the current negligence rules or the need for fresh legal provisions.
The rapid development and deployment of artificial intelligence (AI) present new challenges for traditional negligence law. The current laws which were developed over the years take root from principles which are established in human endowment environments. Yet, the individuality of AI systems, the AI systems’ ability to learn, and the multi-party operation all make the concepts like duty of care, breach of duty, and causation hard to apply. This chapter focuses on the application of the traditional negligence concept in the context of AI and the problems that stems from it due to AI’s distinct properties.
It is thus necessary that for the duty of care element in any AI cases, the recognized criteria that need to be met are the one laid down in Caparo Industries Plc v Dickman (1990). The discussion of Civil Liability in the Age of Artificial Intelligence emphasizes how foreseeability, proximity, and fairness are challenged by autonomous AI systems. This test consists of foreseeability, proximity, and fairness. Specifically, AI raises issues with regard to the predictability of the event in the context of foreseeability. The ability to predict the subsequent actions of AI specular systems is also challenging because, through the process of machine learning, they may change over time [7]. For example, an autonomous medical diagnostic tool may improve with decision making capacity and cause harm because of the bias that was built into the training set. The problem emanates when judges try to define foreseeability – whether the damage caused by an AI was within the anticipation of the developer or the user. The Environmental Protection Act 1990 of the UK is beneficial in this case.
It just noted that distance also creates challenges in AI-related cases. Most of the AI systems work as a mediator in execution where there are multiple counterparts that encompass developers, vendors, deployment organizations, and customers. In the situation of using a diagnostic tool, this study was unable to ascertain which group is most accountable for the harm minimization, the software developer, the business entity, or the user. However, there is a considerable challenge in the application of fairness in AI dependent liability. AI systems may contain open source codes and therefore it will not be easy to identify which entity is liable. This paper argues that broad duties on developers may slow down the development process and stifle product innovation while: failure to clarify who bears the costs of repairing faulty technologies also presents potential risks of unjust outcomes. Thus, even though Caparo provides a starting framework, legislative adjustment could be required to address what AI brings.
The rule of the reasonable person that was set in Blyth v Birmingham Waterworks Co (1856) proves to be unsuitable in the face of an AI since it indicates a human standard of care. Autonomous type of AI is a system that has no capacity for intent, or in other words, no way of thinking like a human does. For this reason, it is difficult to use this test for determining the behavior of an AI model. For example, if the diagnostic tool of artificial intelligence developed to make a right diagnosis erroneously arrives at a decision owing to biased program, can the developer be held negligent under the Bolam v Friern Hospital Management Committee (1957) standard that applies to professionals?[8] Since AI does not have a reference in professional standards, questions arise in implementing reasonable person standards in regulating the conduct of AI in courts.
Besides, there are questions of cause and effect with AI becoming complicated. Consequently, in the traditional negligence, the “but for” test that was set in Barnett v Chelsea & Kensington Hospital Management Committee (1969) aids in figuring out whether the harm unfolded due to the actions of the defendant. However, in AI, we have developers, users and third party service providers, hence it becomes difficult to establish direct correlation. The nature of AI failures will require the courts to adopt what is known as a probabilistic causation model as is the case with medical malpractice.
Compared with traditional negligence, for the plaintiff to succeed in the client's claim, it is required that the defendant’s actions were the proximate cause of harm. However, it should be noted that, more often than not, AI systems do not behave as expected, especially when these are based on machine learning. There is a gradual shift as these systems mature, to assign a direct blame on the developer for harm done. This decentralization of decision-making, alongside the involvement of multiple actors in AI systems, challenges traditional legal causation principles.
In the event that AI contributes to an injury, there are other forms of what was referred to as proximate cause such as the probabilistic causation, which establishes the chances that a failure of AI was the main cause of the loss. This would afford the courts a better opportunity to deal with the circumstances where AI decisions that are based on data and changeable algorithms are made, thereby providing a better framework with regard to AI related negligence proceedings.
This is an employer’s accountability for the torts of his employees within the course and scope of their duties. It has been used as a basis for ascertaining who is responsible for negligence related incidents. Nonetheless, it is crucial to note that extended vicarious liability to AI systems has its drawbacks, especially when applied to an AI machine that may be fully or partly independent from human control and supervision.
In other common examples of vicarious liability such as the case of Lister v Hesley Hall Ltd [2001] 2 AC 215, the employer will be held liable for the torts of the employees when they are committed in the course of their work. The problem of applying this doctrine with reference to AI is that AI solutions, particularly autonomous ones, are not employees [9]. They do not take orders from human beings but work on data and computer programs to make their decisions. This gives rise to a sovereignty issue about when the AI system incurs in a loss, to become liable.
For example, there can be an AI-controlled delivery service through which the company delivers its products to the clients. If an accident occurs due to the mishap in the system, the performance of the artificial intelligence, whether the company responsible for the performance. There the artificial intelligence can be considered as the responsible but the stages after that are more vital. The company might argue that the contents of the message were delivered by the AI assistant without the company’s prior consent and possibly beyond the capabilities for which the AI was programmed for. However, the victim may counter that in any case the company should still be held fully responsible for the consequences which stem from the use of the AI.
This raises the problem even further when it comes to the AI systems, especially when the system involves machine learning and has the ability to achieve growth and change in ways that can be unplanned by the programmers. This arouses the degree to which vicarious liability should apply to artificial intelligence systems, and if it should, under which conditions.
Another way to approach the problem could be the appearance of a new form of legal liability which would contemplate the fact that artificial intelligence is set on its own. This may include extending the liability and making the company liable for actions of the AI system as in the case of Vicarious liability though with some differences due to the autonomy of the Algorithm. It could be a new form of liability which may rest with the proposition that since companies are the user of AI hence liable in their entirety for the actions of the AI systems they employ even if these are autonomous systems.
In conclusion, the paper indicates that, though vicarious liability continues to be an indispensable aspect of negligence law, it needs a proper approach with reference to AI [10]. The absence of direct human control, the ability of AI to make decisions independently, and the changes in AI characteristics with the growth of machine learning algorithms require creating new legal rules for AI accountability in case of violation of legal norms.
Conclusion
The incorporation of artificial intelligence solutions across the industries tends to have unpredictable impacts on the application of negligence principle, more so in duty of care, breach and causation. But the prevailing legal principles in such a system do not squarely fit these systems since these AI systems are self-running and change with time. It is therefore important to start legislative amendments to pave the way for the improvement of rules and law to ensure developers and users are made to meet certain standards without hindering innovation. Thus, the proper legislation will also evolve and shall be influenced by the evolution of artificial intelligence, along with judicial interpretation helping it better manage the complexities introduced by AI.
The opportunities of accidental usage of AI technology has brought into consideration the need for nations to provide for the legal issues of Artificial Intelligence in their legal framework. This paper aims at examining how different jurisdictions address the issue of legal liability in connection with AI. Thus, only by evaluating the approaches utilized in the European Union, the United States and Australia one can get an idea of the positive and negative aspects in each. Every region has its accounts to monitoring AI technologies and it is crucial to study each country’s methods in usage of this technology. It also underscores further where current laws may lack or need to be filled by legislation reforms in some ways or the other.
The paper under analysis states that the European Union has formulated the AI Liability Directive because of their increased usage of AI systems. The directive seeks to provide legal frameworks for the setting responsibility for the owners and creators of AI systems in case of losses caused by such technologies irrespective of negligence. However, a strict liability regime is meant to offer clarity and fairness for victims and they must be protected and be able to seek redress from those who have been injured by AI systems.
The control is targeted at high-risk AI applications, which are most dangerous in operation and utilizing in healthcare, transport sectors and in financial business. For instance, self-driving cars and the use of artificial intelligence in the medical field may lead to negative consequences in case of a problem [11]. Through imposing strict liability, the EU makes sure that the developers or operators of the technologies bear liability for the harm that may result from instantiating the technologies.
Another characteristic of the AI Liability Directive concerns its view or intention to contribute to the general policy of encouraging innovation while addressing the need to protect the public interest. It affords proper legal redress to the wrong done by the AI while at the same time opening new opportunities for future advancement in technology. This directive will assist affected persons to claim compensation with easy means hence putting pressure on AI companies to be responsible for their inventions. Despite the fact that the directive is not yet fully set in place, the perspective is considered to be one of the main achievements in the endeavor to put a check on AI technology at the international level. The AI Act of the EU is effective for this factor.
At the moment, AI liability is regulated under the provisions of product liability and sector-specific legislation that is applicable in the United States of America. Product liability laws state that the manufacturers are legally responsible for the defects or unsafe product that ends up causing harm to the users. This may apply to the products like self-driving vehicles or the health-related contexts like, for instance, an AI diagnostic software. In the event that an AI system produces an adverse outcome, the developer or manufacturer may be regarded as negligent akin to other faulty products. The use of Health and Safety Act 1974 can be used in this case.
Nonetheless, there is no comprehensive federal regulation for AI responsibility in the United States. However, in the United States, responsibility is absolute on varying state laws, and there has been some specializations with regards to sectors [12]. For example, self-driving cars have different sets of safety requirements depending on the region and application, and in the case of health-related AI solutions, the approach is provided by agencies such as the FDA. Such a system is very unsatisfactory because it leads to legal nuances that depend on the country in which the company will operate.
Various policies suggest that there is not a strong and definite strategy in the United States so that artificial intelligence developers and producers may stay anxious regarding to what extent they are legal in the long-term and as the level of AI increases. Nevertheless, legislation on product liability can provide a certain level of protection to the customers, however, it does not appear to be sufficient enough to address the dynamics of AI [13]. Some believe that there must be stronger and more concrete measures to protect from the threats that relate to AI.
However, there is relative freedom and creativity in the American system of regulating online innovation and gambling. But it also brings about issues on the degree to which such a disjointed system shields the customers and penalizes the AI firms. As technology advances in AI, the US could have to look into the issue of these, which would involve the emergence of a unified organized system of AI.
The use of AI in Australia is integrated into the Australian Consumer Law (ACL) concerning the protection of customers and consumers. That is, based on the ACL, organizations can be charged with liability for negligence resulting from the operation of the said product where it is determined that the product is recalcitrant. This implies that when a system that has been manufactured poses a threat or is destructive and does not conform to the accepted safety standards, the manufacturer is held to account for any harm that may ensue from the same.
Australia takes an approach that applies existing consumer protection rules in the new technologies including AI [14]. In its approach, the country does not aim at enacting a new legislation on AI, but rather fit it into the existing legal regime of the country. This enables fair implementation of other ongoing laws as well as protecting the consumer in view of the developments in technology.
Even AI goods are regulated by the ACL which prescribes that no goods must be supplied by the manufacturer that is not safe, of good quality and reasonably fit for the purpose for which they were intended to be used. from these provisions it can be indicated that, if an AI System does not meet the standards provided herein and causes harm, the consumer is protected under the ACL and may seek compensation. This entails the use of AI technologies in different industries like health sector, automotive, and industrial industries. The use of Health and Safety at Work Act 1974 (UK) is effective for this.
Nevertheless, this kind of protection seems to satisfy certain critics since it is argued that AI is too complicated and sophisticated to call for an extra layer of legal protection. It has been effective to some extent with the particular version of ACL but as AI technologies are becoming more autonomous in nature and are capable of updating themselves, it may not capture these aspects [15]. Hence, there might be a need to periodically review the ACL to ascertain that it is up to date and relevant to address current AI related risks and issues.
According to the current legal circumstances in the United Kingdom, AI Liability has no consistent approach. The country mainly relies on negligence law to tackle any grievances connected with artificial intelligence systems. However, there is increasing concern that the current format of the system will not be adequate to address the issues and the dynamics of AI technologies. The current laws are not adequate so as to address the challenges associated with AI especially in the self-operating or hazardous systems.
These challenges have not been lost on the UK government which is very much alive to the issue and is even looking for ways to rectify it. Among the ideas in question is the idea of promulgation of new AI-specific legislation in order to have a more clear and exhaustive regulation of AI liability. They could give specific directions to the developers of the AI and the consumers as to their legal requirements and who will be held accountable in case of loss or damages by an AI.
This is true particularly in applications that were input in healthcare, transport, as well as in the financial sector since handling these positions involves inordinately risky operations whereby errors might lead to consequences such as loss of life or other adverse effects[16]. Therefore, there is the attempt to find a way on how the UK government will allow innovations within AI while at the same time ensuring that there is adequate legislation that will protect consumers and users of the technology. There might be a need for an effective law to state more guidance on who is legally responsible in cases of harm caused by an AI system especially when decision-making is distributed or where the AI system is fully autonomous.
The UK is not yet there in the process of defining a clear legal framework, however, it has started addressing issues that are legal in relation to AI[17]. The current framework is being assessed, and improvements are expected to be made concerning the new advancements in the AI technologies.
Conclusion
Every jurisdiction’s approach to AI liability is proportional to the balance between risk and awarding the innovation that is based on artificial intelligence. The EU has a proposed legal approach of the strict liability regime for high-risk artificial intelligence while the US and Australia employ the adaptation of the existing laws such as the product liability laws and consumer protection laws when it comes to artificial intelligence. Nevertheless, as we see all the progress going forward in the sphere of AI technologies, all the jurisdictions have to modify and renew the legal systems of their countries. The question presenting itself is to predict the progression of such laws, in order to achieve the goal of protecting consumers sufficiently while enabling further advancements in the field of artificial intelligence.
Artificial intelligence (AI) is actually steadily revolutionizing the different sectors of the global economy. It is being applied in the health sector, in transportation, in the financial industry and in many other fields. However, there are large legal risks associated with them, especially when it comes to responsibility for untoward occurrences. There currently is no clear legal regulation to cater for aspects that AI may have. The old traditional concepts of liability laws were formulated in the historical context and in relation with human activity and mass-produced goods. AI is independently running and self-improving its performance over the time. This creates a problem when AI programs are direly accountable in the event they cause damage (Saraswat, 179).
The law of negligence is based on the premise of the human being owing duty of care. But, this understanding is wrong as AI systems do not have judgment or an intent to harm anyone. It is not only businesses that feel the effects of the current product liability laws but these are as follows; It should be noted that AI does not possess the characteristics of traditional products. It varies with the time and this makes it complicated especially when handling the cases involving defect based claims. It may be required to incorporate the principles of judicial interpretation to address these challenges without success. Courts may have difficulties in applying passing doctrines on the cases of artificial intelligence. Another disadvantage of using AI is that it presents issues of unpredictable judgements arising from the complicated nature of AI systems.
Thus, it is apposite to examine the issues of the need for legal reform in this chapter. It looks at the ability of judicial adaptation in handling the issue of AI liability or whether there is need for new legislation. The identified prospective reforms include developing activities peculiar liability regime, defining the strict liability for certain challenging AI applications, or developing the oversight framework. They could promote development of a better and more equitable legal culture.
Current legislation might not sufficiently regulate who is to blame in case of problems arising from AI. They include considering, liability, and faulty human behavior. However, AI applications work on their own, which means that there is no direct intervention of people in such a system. The debate doesn’t rely on a consistency between columns but it makes independent decisions and sometimes in a very unpredictable manner. There it becomes difficult to consider the way of decision-making and also the ways this can be developed for better response. These factors make proving liability quite a challenge.
It is however important to note that again and as much as product liability laws may be beneficial they also have some limitations as well. Nonetheless, traditional products remain stagnant once they are up for sale in the market. Thus, in case if there is a defect in the product then the manufacturer is fully liable for the defects up to the time of sale. AI is different [18]. The third AI ability of the system is that it learns and updates itself in its knowledge based on new data which may come in. A good system at one point may be designed to be beneficial, yet in future, it may contain some negative influences. This brings much confusion on when and where liability should apply.
Thus, the introduction of the specific liability rules concerning AI can eliminate these issues. More of them could be used to define responsibility to new levels. AI developers could be needed to provide transparency of their creations. In turn, operators could have certain responsibilities to supervise the actions of an AI entity. Certain guidelines given to users that could help them in getting the appropriate methods of using AI safely. This would help to avoid negligence by ensuring adequate provision of dictates that contain areas that need to be covered in situations whereby some are missed.
One of the solutions is to propose a concept of a shared liability scheme. This will ensure that fair responsibilities are assigned to developers, those who deploys the AIs and the users of the same. It can make sure that all the concerned stakeholders do undertake proper precautions. Some opinions include, preventing AI systems from being opaque. Another option is to recommend for AI companies to keep records of the AI decision making[19]. This would enhance exposure to the general characteristics, hence assisting courts in identifying responsibility in cases of liability.
It goes further to elaborate that AI-specific rules would also improve people’s trust in AI technologies. However, if the legal provisions are well defined, the business actors can engage in innovation fearlessly. Consumers will also perceive that the AI products they use to be safer. Citizens should have legal certainty for the proper development of artificial intelligence.
For borderline AI, such as self-driving cars or AI in the healthcare market, the legal structure of strict liability could be put into practice. According to this model, the creators and producers of artificial intelligence would be blameworthy and have the ability to be held legally responsible for liabilities even in absence of negligence on their part. By doing this it will consequently shift the focus on the work done from personal gain and dependency to innovation and concern for the general public.
To balance and manage the risks, AI needs some preventative measures where the high-risk areas include self-driving cars, health applications, and financial programs or any other programs that makes crucial decisions [20]. These systems function autonomously most of the time, and therefore it is hard to apply the doctrines of negligence when dealing with the AI systems. In such situations, there should be a simpler and more efficient way based on the principle of strict liability.
This is mainly held on a standard where AI developers, manufacturers, and/or deployers would be made to pay for any damages that had been caused by their AI regardless of whether or not negligence could be proven. This would guarantee compensation for the victims without hitches up the law courts through complications and this would also ensure that justice is served as soon as possible. This would also ensure that organizations develop and maintain safety as a priority and conduct proper testing before they deploy AI systems.
For example, if an autonomous vehicle collided with another car, many states adopting the regime of strict liability eliminated victims’ burden to demonstrate defects or erroneous codes of the vehicle. This would offer better protection to the consumers especially in sectors involving the use of AI systems in key aspects of human lives. Nevertheless, there are some disadvantages of strict liability which one must understand [21]. It may lead to the manufacture discouraging growth among the small-scale firms since the risks involved in facing liability claims are very expensive. Regarding this, the governments could create compensation funds for AI developers or set obligatory insurance for companies working in AI.
By adopting strict liability there will be need for provisions that define what constitute as “high-risk AI applications” and the criteria for ascription of blame. In this case, there would be some rules that would ensure the protection of the public from dangers while at the same time promoting the development of technology. It is imperative that there is a balance reached so as World find ways Through which AI could progress and at the same time individuals would not be at the risk of so much harm.
A clear cut strict liability regime would also ensure that AI corporations are held responsible, motivate for improved development of safe artificial intelligence and increase the level of confidence the public has for Artificial Intelligence systems. It would also bring legal certainty for both the business and consumers encouraging innovation of artificial intelligence.
AI systems operate autonomously. These steadily acquire and analyze a considerable amount of information and are capable of making decisions autonomously. In many cases, these systems operate in distributed systems. Unfortunately, this results into a situation where it becomes so hard to note who is to be blamed for instance in case of an error made in the business. Disagreements or differences arising in an AI decision, do not follow the traditional way of being traceable. It is very often possible to trace no kind of judgement as to why one certain decision was reached out of other possible options. These issues are very much legal and ethical in nature since the public is unfairly deprived of the information.
Thus, the creation of a mandatory oversight system would be useful in solving such problems. Continual supervision by experts would make it possible to maintain organisational decision-making for AI as safe and ethical [22]. It could involve independent regulatory bodies in an oversight system. These bodies would evaluate the effects of AIs before they are released in society. They would also perform periodic surveillance to the AI systems that were already implemented. This would also help to assess the risks at an early stage.
There are a number of issues regarding Artificial Intelligence, one of which is the problem of bias. When an AI system is trained with some data then it will pick unfair decisions, regarding the data with which it has been trained. It is especially fatal in situations such as employment, health, and justice systems. It will enable the regulators to identify the biases at their infancy and hence stop them before they go to the next level. It would also enhance the continual updating of the models used in AI to eliminate any event of discrimination for the desired strategies to work effectively.
This is so in the sense that oversight is capable of protecting consumers against malpractices by service providers. A clear indication to managers is that most of the users who engage with AI are usually unaware of how it perfectly operates. Some of these Plant Employees may not have knowledge of risks. If AI is made mandatory then it would be positive because mandatory oversight would assist to check against any unlawful and unwarranted actions made by the system. This would go a long way in increasing people’s trust on AI products and services.
It could also involve such aspects as; rigorous paperwork. AI developers should ensure that there is documentation of how the system works at its best. These ones could be subjected to auditing. In this case there involves an AI system, the system’s logs can identify the party responsible for damages [23]. Likewise, it would also help them in detecting issues with algorithms containing AI.
Perhaps the governments could use a multi-level system whereby a number of groups overseeing them is superior to the previous one and is responsible for checking them. Low-risk applications may not need constant monitoring of the process. However, AI applications with higher risk, like self-driving cars and healthcare AI, should have stronger regulation. All these systems should have periodic checkups so that if there is a weakness it can be detected and worked on. It is recommended that the developers be made to report any major failure with AI as soon as possible.
Most of them would provide an oversight that is mandatory. It would ensure appropriate functioning of AI and ensure it functions in an ethical and acceptable manner. It would also help in promoting responsible AI development. Thus, it means that through checking, lawmakers will be able to minimise risks while at the same time permitting innovation to take place.
The AI legislation should therefore not be only about legal responsibilities. Ethical considerations are equally important. AI systems affect people’s lives in various aspects as they are everywhere and involved in several activities. It plays the role of determining decisions from banking sector, healthcare, law enforcement authorities, social media among others. If AI is not planned and being developed with ethics in its consideration then it turns out to be a cause of harm. AI laws should enforce ethical standards of justice and protect unbiased, transparent and accountable artificial intelligence.
One ethical concern is fairness. Race, gender and economic class among the populace should not be a barrier to AI. Nevertheless, it is alarming that many AI systems contribute or inherit prejudices from their training data sets. This causes prejudice where individuals or groups are treated unfairly especially in crucial sectors such as employment and justice systems. The ethical laws created with AI must demand that bias be checked on companies must use diverse and inclusive datasets for testing the AI models.
There are few more issues, such as the issues of transparency; many AI systems are so-called black boxes, where the user does not know how the decision is being made. Such means of evaluating the efficiency of AI laws should insist on one or several known factors pertaining to the algorithms in use by companies. The public should be allowed to explain themselves in situations that AI affects them.
Accountability is also crucial. Since an AI system has the ability to incur losses, someone should bear the loss in the event of occurrence of such losses. For the avoidance of doubt, ethical laws should always provide a clear line of accountability. Related to that, the various stakeholders involved in artificial intelligence implementation and/or management should evenly distribute responsibilities [24]. The AI systems should thus have certain inbuilt precautions that check the operations of the system. In case of error, there must be ways of rectifying it.
The legal frameworks relating to AI should also include the aspect of human supervision. To this end, one should not exclude the use of AI as a way of automating human decision making but as a tool of enhancing the decision making system. For instance, in the field of health, AI has an ability to diagnose a disease. From these articles it is quite clear that the final decision must be arrived at by the doctors. As per the ethical AI laws, rules should be made which mandate human supervision especially in sensitive areas.
The state is another way that governments can use to ensure that developers of AI take an ethics course. Ethics has to be integrated into AI from the onset of the project. Business organizations should implement and periodically update their ethical policies in artificial intelligence. It has to be understood that ethical development should always go hand in hand with developing artificial intelligence. However, it can and must be interfaced with throughout the whole life cycle of artificial intelligence.
It means that common law should observe the principles of goodness and justness in order to fit into the ethical consideration. It should occupy the optimal position between innovative job creation and job security [25]. In this way, it will be beneficial to society to embrace ethical laws against the use of AI since it will provide equal return to the society without any negative impacts on the society. Thus, incorporating ethics in the legislation surrounding AI will not only create an intelligent world that relies on AI but will also ensure that it is a responsible one.
Conclusion
Based on the analysis of the current trends, it can be identified that AI is continually developing at an unprecedented rate. There are concerns that the current set laws have been unable to cope with the advancement of technology. This shows that solely relying on judicial interpretation is an inadequate way of solving the questions related to AI liability. Courts rely on common law which is a concept that may not be well applicable in situations where decision making is driven by AI. Unless there are legal changes made, such legislative loopholes will still persist.
Therefore, it is required political and legislative action to develop a sound and responsive legal mechanism. The development of AI-specific rules of liability would be beneficial to demarcate the roles and duties between the creators, managers and consumers. Mandatory liability for high-risk applications would also mean that those who are using AI systems would be liable for all the possible harm that might be caused. The second violation adds a mandatory oversight system to improve the accountability of the AI decision-making process and is designed to detect errors. Therefore, the AI Act (EU, Proposed) can be effective for this factor.
This also applies to ethical issues that must be included in the legislation touching on the use of Artificial Intelligence. In addition, persons and stakeholder interests should work towards fairness, openness, and accountability that can govern AI regulations. Members in this field should exercise relative ethical practices to avoid having poisonous preferences to their results. It's regarded that important legal guidelines must be implemented with corresponding ethical principles in order to advance the functionality of artificial intelligence to have a positive social impact while minimizing its drawbacks.
Seemingly, the creation of such reforms will allow lawmakers to better address the needs of the society and create an adequate environment for the support of innovations. Heightened legal action will guarantee that AI is not an instrument of calamity but a means of advantageous advancement.
Recommendation
From the material of this thesis, it is suggested that governments should undertake legislative reform to cover the area of AI liability. The analysis provided in Civil Liability in the Age of Artificial Intelligence supports the creation of AI-specific rules and oversight mechanisms to ensure accountability. First of all, it is necessary to create specific rules governing AI’s liability. These rules we have outlined above would limit the circumstances under which it would be difficult to attribute responsibility wherever AI systems become destructive thus removing all viable excuses from developers, deployers and users of such infrastructures [26]. The rules should be sufficiently dynamic in nature due to the constant advancements in the field of AI; it is important that each stakeholder possesses certainty with regard to the law.
However, in certain cases and particularly for high-risk AI, it is recommended that a system of ‘strict liability’ be adopted for AI and a given application such as self-driving cars, use of artificial intelligence in the health sector, and robotics. This would make sure that developers and deployers are made liable for harm no negligent was committed. This ensures the safety of individuals within the society and user safe AI because the risks involved in artificial intelligence technologies are also prevented.
Moreover, there needs to be an obligatory monitoring mechanism regarding the decisions made by AI [27]. Continuous supervision of the systems by professional personnel would assist in eliminating some forms of negligence and even bias in the functioning of the artificial systems.
Lastly, there is the need to adopt bills on AI that will include ethical standards to the use of AI. This would ensure that developers embraced fairness, transparency and accountability because they would know that people would hold them accountable for any lousy technology that is being initiated into the society.
References
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