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Spanien
Spain – Generative artificial intelligence in the legal sector
30 Juli 2024
- Künstliche Intelligenz
Generative artificial intelligence (generative AI) is a variant of artificial intelligence aimed at creating models capable of generating new and original content. These models are trained to learn patterns and features from data sets, and can then generate similar or even completely new content based on those learned patterns.
A specific type of generative model is the generative neural network (GAN). GANs consist of two neural networks, one generative and one discriminative, working together. The generative network creates new content, while the discriminative network evaluates the authenticity of that content. The generative model can produce increasingly realistic results as these networks compete and improve.
Generative AI has applications in various areas, such as art creation, creative text generation, speech synthesis, and so on. It is also used in fields such as image enhancement and machine translation. This approach has advanced significantly in recent years and continues to be an active area of research in artificial intelligence.
Generative artificial intelligence applied to the legal sector involves using generative models to assist in various tasks and processes related to legal practice.
Positive aspects of generative AI applied to the legal sector
The integration of generative artificial intelligence in the legal field has emerged as a transformative catalyst, providing a number of significant benefits that positively impact the efficiency, accuracy, and accessibility of legal services. Throughout this evolution, several aspects highlight the substantial contribution of artificial intelligence to legal practice.
Some of these benefits are highlighted below:
Legal Document Drafting
Generative AI can be used to draft legal documents, contracts and other legal texts. It can generate content based on patterns learned from large sets of legal data, facilitating the creation of standard documents and reducing the workload for legal professionals, also ensuring consistency and accuracy in legal drafting, reducing risks associated with possible human errors.
Analysis of large volumes of data
The ability to process information at a speed and scale that surpasses human abilities enables the identification of patterns, trends and precedents with greater speed and accuracy. This advanced analysis helps strengthen legal arguments, improve strategic decision-making and provide clients with stronger legal representation.
Improved legal research
Generative artificial intelligence systems can perform faster and more accurate searches of legal databases, law libraries and case law. This streamlines the legal research process, providing professionals with access to relevant information more efficiently.
Legal Argument Generation
Generative IA can help generate sound legal arguments. By understanding case law and legal principles, it can help lawyers build better arguments and develop strategies for specific cases.
Automated Legal Advice
Automated legal advice systems can be developed that use generative AI to answer common legal questions and provide basic guidance. This could be useful for simpler legal queries and to improve access to legal information.
Personalized legal advice
Artificial intelligence can analyze case-specific data and provide personalized legal advice. This helps legal professionals make more informed and strategic decisions by considering situation-specific factors.
Legal Scenario Simulation
Generative AI can simulate legal scenarios to help lawyers evaluate possible outcomes and risks in particular cases. This could be useful in strategic decision-making and legal planning.
Automation of repetitive tasks
The ability of artificial intelligence systems to take on the workload related to standard document review and basic information management allows legal professionals to focus on more complex and strategic issues. This automation not only saves time but also decreases the likelihood of human error, thus strengthening the overall quality of legal work.
Optimization of internal processes
Artificial intelligence can significantly improve efficiency in case management, meeting scheduling, and other day-to-day operations in law firms. This optimization not only streamlines internal practices but also enables more efficient resource allocation and more effective workload management.
In short, the application of generative artificial intelligence in the legal sector transcends the mere automation of tasks, encompassing fundamental aspects that improve the quality and efficiency of legal services. From the automation of routine tasks to advanced data analysis and document generation, artificial intelligence is a powerful ally that drives positive developments in legal practice. This advancement not only improves the internal efficiency of law firms, but also strengthens the ability of legal professionals to provide accurate and strategic advice in an ever-changing legal environment.
While generative AI offers many possibilities, its implementation in the legal sector must be approached cautiously to ensure accuracy, ethics, and compliance with applicable laws and regulations. Human intervention and legal oversight remain essential to ensure quality and accountability in using these technologies.
Negative aspects of the application of generative AI to the legal sector
While promising, the integration of generative artificial intelligence in the legal sector poses a number of challenges and negative aspects that require attention and careful consideration. Despite significant advances in automation and process improvement, addressing the following adverse aspects is crucial to ensure an ethical and effective implementation.
Lack of human discernment
Although artificial intelligence systems can analyze data at impressive speed, they lack human understanding and sensitivity. Interpreting legal nuances, understanding emotional contexts, and making decisions based on ethics are skills intrinsic to legal professionals. Over-reliance on technology in interpreting complex situations could result in inadequate or insensitive assessments.
Risk of algorithmic bias
Algorithms used in generative artificial intelligence are trained on historical data, and if that data contains cultural, ethnic, or gender biases, the results generated may reflect and perpetuate those biases. This raises ethical and legal concerns, as automated decisions could be inherently discriminatory, affecting fairness and justice in the legal system.
Data security and privacy
The implementation of artificial intelligence in the legal field involves handling highly confidential information. Systems‘ vulnerability to cyber attacks could expose sensitive data, compromising the confidentiality and integrity of the legal system. Good protection against cyber threats is essential to maintaining confidence in these technologies.
Job displacement
As artificial intelligence takes over routine and repetitive tasks, there is a risk that certain jobs in the legal sector will be affected. This raises questions about role restructuring and the need for legal professionals to acquire new skills to adapt to a changing work environment. The ethics of this displacement and measures to mitigate its impacts must be carefully addressed.
Ethical complexity in decision making
Generative artificial intelligence algorithms often operate opaquely, meaning that the logic behind their decisions can be difficult to understand or explain. This raises ethical questions about accountability and transparency in legal decision-making, especially in critical cases where a clear explanation of decisions is critical.
Costs associated with implementation
From initial development to ongoing training and system maintenance, law firms, especially smaller ones, can face significant financial challenges. This raises the issue of equity in access to these technologies and the need to seek solutions that do not perpetuate inequities in the legal system.
Cultural resistance and adaptation
Cultural resistance and adaptation are factors that should not be overlooked. The introduction of generative artificial intelligence may encounter resistance among legal professionals who may be reluctant to rely on emerging technologies. Organizational culture and acceptance of these tools may require time and effort for successful implementation. Training and effective communication are essential to overcome these barriers.
In conclusion, the application of generative artificial intelligence in the legal sector, while offering significant benefits, is not without its challenges. Addressing the lack of human discernment, mitigating the risk of algorithmic bias, ensuring data security and privacy, managing labor displacement, addressing ethical complexity in decision making, and managing associated costs are imperative for ethical and effective implementation. Careful thought and appropriate regulation are essential to harness the benefits of artificial intelligence without compromising fundamental principles of fairness and justice in the legal system.
Self-driving cars react in a split second: quicker than even the most attentive driver. Self-driving cars don’t get tired, they don’t lose concentration or become aggressive; they’re not bothered by everyday problems and thoughts; they don’t get hungry or develop headaches. Self-driving cars don’t drink alcohol or drive under the influence of drugs. In short, human error, the number one cause of road traffic accidents, could be made a thing of the past in one fell swoop if manual driving were to be banned immediately. Is that right? It would be, if there hadn’t recently been reports about two deaths, one during the test drive for a self-driving car (UBER) and one while a semi-autonomous vehicle was driving on a motorway and using its lane assist system (Tesla), both of which regrettably occurred in the USA in March 2018. In Tesla’s case it seems that the semi-autonomous driving assistant was switched off at the moment of the accident.
Around the globe, people die every day due to careless driving, with around 90% of all accidents caused by human error and just a small percentage due to a technical fault related to the vehicle. Despite human error, we have not banned driving on these grounds. Two accidents with fatal consequences involving autonomous vehicles being test-driven have attracted the full glare of the media spotlight, and call into question the technical development of a rapidly progressing industry. Are self-driving cars now just hype, or a trend that cannot be contained, despite every additional human life that is lost as a result of mistakes made by self-driving technology?
For many, the thought that fully autonomous vehicles (a self-driving car without a driver) might exist in the future is rather unsettling. The two recent deaths in the USA resulting from (semi-) autonomous cars have, rather, my cause fear for others. From a legal perspective, it makes no difference whatsoever for the injured party whether the accident was caused by a careless human or technology that was functioning inadequately. The reason for the line drawn between the two, despite this fact, is probably that every human error represents a separate accident, whereas the failure or malfunction of technology cannot be seen as a one-off: rather, understandably and probably correctly, it is viewed as a system error or series error caused by a certain technology available at a particular point in time.
From a legal angle, a technical defect generally also represents a design defect that affects the entire run of a particular vehicle range. Deaths caused by software malfunctions cause people to quickly lose trust in other vehicles equipped with the same faulty software. Conversely, if a drunk driver injures or kills another road user, it is not assumed that the majority of other drivers (or all of them) could potentially cause accidents due to the influence of alcohol.
The fundamental question for all technological developments is this: do people want self-driving cars?
When we talk of self-driving (or autonomous) vehicles, we mean machines guided by computers. On-board computers are common practice in aviation, without the pilot him – or herself flying the plane – and from a statistical point of view, airplanes are the safest mode of transport. Couldn’t cars become just as safe? However, a comparison between planes and cars cannot be justified, due to the different user groups, the number of cars driven every day, and the constantly imminent risk of a collision with other road users, including pedestrians.
While driver assistance systems, such as lane assist, park assist or adaptive cruise control, can be found in many widespread models and are principally permitted and allowed in Europe, current legislation in Europe and also Austria only permits (semi-) autonomous vehicles to be used for test purposes. Additionally, in Austria these test drives can, inter alia, only take place on motorways or with minibuses in an urban environment following specially marked routes (cf. the test drives with minibuses in the towns of Salzburg and Velden). Test drives have been carried out on Austria’s roads in line with particular legal requirements for a little more than a year, and it has been necessary to have a person in the vehicle at all times. This person must be able to intervene immediately if an accident is on the horizon, to correct wrong steering by the computer or to get the vehicle back under (human) control.
Indeed, under the legislation in the US states that do permit test drives, people still (currently) need to be inside the car (even before the two accidents mentioned above, California had announced a law that would have made it no longer necessary to have a person in the vehicle). As a result, three questions arise regarding the UBER accident which occurred during a test drive in the US state of Arizona, resulting in a fatal collision with a cyclist: 1. Could the person who was inside the vehicle to control it for safety reasons have activated the emergency brake and averted the collision with the cyclist who suddenly crossed the road? 2. Why did the sensors built into the car not recognise the cyclist in time? 3. Why did the vehicle not stick to the legal speed limit?
Currently, driving systems are being tested in Europe and the USA. In the USA, this can take place on national roads and, contrary to European legislation, also on urban streets. As long as we are still in the test phase we cannot talk of technically proven, let alone officially approved, driving systems. The technical development of self-driving cars, however, has already made it clear that legal responsibility is shifting away from the driver and towards vehicle manufacturers and software developers.
Whether, and when, self-driving cars could become an everyday phenomenon is greatly dependent on certain (future) questions: are we right to expect absolute safety from self-driving cars? What decisions should self-driving cars make in the event that one life can only be saved at the cost of another, and how should this dilemma be resolved?
If artificial intelligence (AI) and self-learning systems could also be included within the technology for self-driving cars, vehicles of this type might possibly become one day “humanoid robots on four wheels”, but they could not be compared to a human being with particular notions of value and morality. If every individual personally bears responsibility for their intuitive behaviour in a specific accident situation, the limits of our legal system are laid bare if algorithms using huge quantities of data make decisions in advance for a subsequent accident situation: these decisions can no longer be wholly ascribed to a particular person or software developer if a self-driving car is involved. It will be our task as lawyers to offer legal support to legislators as they attempt to meet these challenges.
Schreiben Sie an Javier
Self-driving cars – Travelling towards the law
26 April 2018
- Künstliche Intelligenz
Generative artificial intelligence (generative AI) is a variant of artificial intelligence aimed at creating models capable of generating new and original content. These models are trained to learn patterns and features from data sets, and can then generate similar or even completely new content based on those learned patterns.
A specific type of generative model is the generative neural network (GAN). GANs consist of two neural networks, one generative and one discriminative, working together. The generative network creates new content, while the discriminative network evaluates the authenticity of that content. The generative model can produce increasingly realistic results as these networks compete and improve.
Generative AI has applications in various areas, such as art creation, creative text generation, speech synthesis, and so on. It is also used in fields such as image enhancement and machine translation. This approach has advanced significantly in recent years and continues to be an active area of research in artificial intelligence.
Generative artificial intelligence applied to the legal sector involves using generative models to assist in various tasks and processes related to legal practice.
Positive aspects of generative AI applied to the legal sector
The integration of generative artificial intelligence in the legal field has emerged as a transformative catalyst, providing a number of significant benefits that positively impact the efficiency, accuracy, and accessibility of legal services. Throughout this evolution, several aspects highlight the substantial contribution of artificial intelligence to legal practice.
Some of these benefits are highlighted below:
Legal Document Drafting
Generative AI can be used to draft legal documents, contracts and other legal texts. It can generate content based on patterns learned from large sets of legal data, facilitating the creation of standard documents and reducing the workload for legal professionals, also ensuring consistency and accuracy in legal drafting, reducing risks associated with possible human errors.
Analysis of large volumes of data
The ability to process information at a speed and scale that surpasses human abilities enables the identification of patterns, trends and precedents with greater speed and accuracy. This advanced analysis helps strengthen legal arguments, improve strategic decision-making and provide clients with stronger legal representation.
Improved legal research
Generative artificial intelligence systems can perform faster and more accurate searches of legal databases, law libraries and case law. This streamlines the legal research process, providing professionals with access to relevant information more efficiently.
Legal Argument Generation
Generative IA can help generate sound legal arguments. By understanding case law and legal principles, it can help lawyers build better arguments and develop strategies for specific cases.
Automated Legal Advice
Automated legal advice systems can be developed that use generative AI to answer common legal questions and provide basic guidance. This could be useful for simpler legal queries and to improve access to legal information.
Personalized legal advice
Artificial intelligence can analyze case-specific data and provide personalized legal advice. This helps legal professionals make more informed and strategic decisions by considering situation-specific factors.
Legal Scenario Simulation
Generative AI can simulate legal scenarios to help lawyers evaluate possible outcomes and risks in particular cases. This could be useful in strategic decision-making and legal planning.
Automation of repetitive tasks
The ability of artificial intelligence systems to take on the workload related to standard document review and basic information management allows legal professionals to focus on more complex and strategic issues. This automation not only saves time but also decreases the likelihood of human error, thus strengthening the overall quality of legal work.
Optimization of internal processes
Artificial intelligence can significantly improve efficiency in case management, meeting scheduling, and other day-to-day operations in law firms. This optimization not only streamlines internal practices but also enables more efficient resource allocation and more effective workload management.
In short, the application of generative artificial intelligence in the legal sector transcends the mere automation of tasks, encompassing fundamental aspects that improve the quality and efficiency of legal services. From the automation of routine tasks to advanced data analysis and document generation, artificial intelligence is a powerful ally that drives positive developments in legal practice. This advancement not only improves the internal efficiency of law firms, but also strengthens the ability of legal professionals to provide accurate and strategic advice in an ever-changing legal environment.
While generative AI offers many possibilities, its implementation in the legal sector must be approached cautiously to ensure accuracy, ethics, and compliance with applicable laws and regulations. Human intervention and legal oversight remain essential to ensure quality and accountability in using these technologies.
Negative aspects of the application of generative AI to the legal sector
While promising, the integration of generative artificial intelligence in the legal sector poses a number of challenges and negative aspects that require attention and careful consideration. Despite significant advances in automation and process improvement, addressing the following adverse aspects is crucial to ensure an ethical and effective implementation.
Lack of human discernment
Although artificial intelligence systems can analyze data at impressive speed, they lack human understanding and sensitivity. Interpreting legal nuances, understanding emotional contexts, and making decisions based on ethics are skills intrinsic to legal professionals. Over-reliance on technology in interpreting complex situations could result in inadequate or insensitive assessments.
Risk of algorithmic bias
Algorithms used in generative artificial intelligence are trained on historical data, and if that data contains cultural, ethnic, or gender biases, the results generated may reflect and perpetuate those biases. This raises ethical and legal concerns, as automated decisions could be inherently discriminatory, affecting fairness and justice in the legal system.
Data security and privacy
The implementation of artificial intelligence in the legal field involves handling highly confidential information. Systems‘ vulnerability to cyber attacks could expose sensitive data, compromising the confidentiality and integrity of the legal system. Good protection against cyber threats is essential to maintaining confidence in these technologies.
Job displacement
As artificial intelligence takes over routine and repetitive tasks, there is a risk that certain jobs in the legal sector will be affected. This raises questions about role restructuring and the need for legal professionals to acquire new skills to adapt to a changing work environment. The ethics of this displacement and measures to mitigate its impacts must be carefully addressed.
Ethical complexity in decision making
Generative artificial intelligence algorithms often operate opaquely, meaning that the logic behind their decisions can be difficult to understand or explain. This raises ethical questions about accountability and transparency in legal decision-making, especially in critical cases where a clear explanation of decisions is critical.
Costs associated with implementation
From initial development to ongoing training and system maintenance, law firms, especially smaller ones, can face significant financial challenges. This raises the issue of equity in access to these technologies and the need to seek solutions that do not perpetuate inequities in the legal system.
Cultural resistance and adaptation
Cultural resistance and adaptation are factors that should not be overlooked. The introduction of generative artificial intelligence may encounter resistance among legal professionals who may be reluctant to rely on emerging technologies. Organizational culture and acceptance of these tools may require time and effort for successful implementation. Training and effective communication are essential to overcome these barriers.
In conclusion, the application of generative artificial intelligence in the legal sector, while offering significant benefits, is not without its challenges. Addressing the lack of human discernment, mitigating the risk of algorithmic bias, ensuring data security and privacy, managing labor displacement, addressing ethical complexity in decision making, and managing associated costs are imperative for ethical and effective implementation. Careful thought and appropriate regulation are essential to harness the benefits of artificial intelligence without compromising fundamental principles of fairness and justice in the legal system.
Self-driving cars react in a split second: quicker than even the most attentive driver. Self-driving cars don’t get tired, they don’t lose concentration or become aggressive; they’re not bothered by everyday problems and thoughts; they don’t get hungry or develop headaches. Self-driving cars don’t drink alcohol or drive under the influence of drugs. In short, human error, the number one cause of road traffic accidents, could be made a thing of the past in one fell swoop if manual driving were to be banned immediately. Is that right? It would be, if there hadn’t recently been reports about two deaths, one during the test drive for a self-driving car (UBER) and one while a semi-autonomous vehicle was driving on a motorway and using its lane assist system (Tesla), both of which regrettably occurred in the USA in March 2018. In Tesla’s case it seems that the semi-autonomous driving assistant was switched off at the moment of the accident.
Around the globe, people die every day due to careless driving, with around 90% of all accidents caused by human error and just a small percentage due to a technical fault related to the vehicle. Despite human error, we have not banned driving on these grounds. Two accidents with fatal consequences involving autonomous vehicles being test-driven have attracted the full glare of the media spotlight, and call into question the technical development of a rapidly progressing industry. Are self-driving cars now just hype, or a trend that cannot be contained, despite every additional human life that is lost as a result of mistakes made by self-driving technology?
For many, the thought that fully autonomous vehicles (a self-driving car without a driver) might exist in the future is rather unsettling. The two recent deaths in the USA resulting from (semi-) autonomous cars have, rather, my cause fear for others. From a legal perspective, it makes no difference whatsoever for the injured party whether the accident was caused by a careless human or technology that was functioning inadequately. The reason for the line drawn between the two, despite this fact, is probably that every human error represents a separate accident, whereas the failure or malfunction of technology cannot be seen as a one-off: rather, understandably and probably correctly, it is viewed as a system error or series error caused by a certain technology available at a particular point in time.
From a legal angle, a technical defect generally also represents a design defect that affects the entire run of a particular vehicle range. Deaths caused by software malfunctions cause people to quickly lose trust in other vehicles equipped with the same faulty software. Conversely, if a drunk driver injures or kills another road user, it is not assumed that the majority of other drivers (or all of them) could potentially cause accidents due to the influence of alcohol.
The fundamental question for all technological developments is this: do people want self-driving cars?
When we talk of self-driving (or autonomous) vehicles, we mean machines guided by computers. On-board computers are common practice in aviation, without the pilot him – or herself flying the plane – and from a statistical point of view, airplanes are the safest mode of transport. Couldn’t cars become just as safe? However, a comparison between planes and cars cannot be justified, due to the different user groups, the number of cars driven every day, and the constantly imminent risk of a collision with other road users, including pedestrians.
While driver assistance systems, such as lane assist, park assist or adaptive cruise control, can be found in many widespread models and are principally permitted and allowed in Europe, current legislation in Europe and also Austria only permits (semi-) autonomous vehicles to be used for test purposes. Additionally, in Austria these test drives can, inter alia, only take place on motorways or with minibuses in an urban environment following specially marked routes (cf. the test drives with minibuses in the towns of Salzburg and Velden). Test drives have been carried out on Austria’s roads in line with particular legal requirements for a little more than a year, and it has been necessary to have a person in the vehicle at all times. This person must be able to intervene immediately if an accident is on the horizon, to correct wrong steering by the computer or to get the vehicle back under (human) control.
Indeed, under the legislation in the US states that do permit test drives, people still (currently) need to be inside the car (even before the two accidents mentioned above, California had announced a law that would have made it no longer necessary to have a person in the vehicle). As a result, three questions arise regarding the UBER accident which occurred during a test drive in the US state of Arizona, resulting in a fatal collision with a cyclist: 1. Could the person who was inside the vehicle to control it for safety reasons have activated the emergency brake and averted the collision with the cyclist who suddenly crossed the road? 2. Why did the sensors built into the car not recognise the cyclist in time? 3. Why did the vehicle not stick to the legal speed limit?
Currently, driving systems are being tested in Europe and the USA. In the USA, this can take place on national roads and, contrary to European legislation, also on urban streets. As long as we are still in the test phase we cannot talk of technically proven, let alone officially approved, driving systems. The technical development of self-driving cars, however, has already made it clear that legal responsibility is shifting away from the driver and towards vehicle manufacturers and software developers.
Whether, and when, self-driving cars could become an everyday phenomenon is greatly dependent on certain (future) questions: are we right to expect absolute safety from self-driving cars? What decisions should self-driving cars make in the event that one life can only be saved at the cost of another, and how should this dilemma be resolved?
If artificial intelligence (AI) and self-learning systems could also be included within the technology for self-driving cars, vehicles of this type might possibly become one day “humanoid robots on four wheels”, but they could not be compared to a human being with particular notions of value and morality. If every individual personally bears responsibility for their intuitive behaviour in a specific accident situation, the limits of our legal system are laid bare if algorithms using huge quantities of data make decisions in advance for a subsequent accident situation: these decisions can no longer be wholly ascribed to a particular person or software developer if a self-driving car is involved. It will be our task as lawyers to offer legal support to legislators as they attempt to meet these challenges.
