AI can make lawyers more productive, but it should never take on the role of a lawyer's judgment and critical thinking. Legal advice that is incorrect or misdirected can result from an over-reliance on AI. There is now controversy over a number of legal issues arising from the employment of AI, including criminal liability and the question of whether an entity can be held accountable for its conduct. Rapid advancements in generative AI are also posing challenges to copyright and patent regulations.
There are numerous legal ramifications related to machine learning. One concern is intellectual property rights; as AI is capable of producing artistic and musical creations, it presents issues with copyright ownership. Privacy is another problem because AI has access to enormous volumes of data and might reveal details about a person's identity that they would not have otherwise known.
Finally, the use of AI systems to make choices in fields like insurance, criminal justice, credit, health, and employment raises ethical questions about fairness, discrimination, and privacy. Specifically, worries exist over the potential for prejudice in algorithms and automated decision-making systems, which could impact marginalized populations such as women, minorities, individuals with disabilities, and the LGBTQIA community. This is made worse by the difficulty in comprehending how AI choices are produced due to a lack of algorithmic openness (Desai & Kroll 2017).
Liability and accountability become legal concerns when AI systems decide and act on behalf of users. According to Gluyas and Day (2018), AI technology can damage property and people when it malfunctions. These occurrences may involve autonomous vehicles colliding with pedestrians, robo-doctor systems misdiagnosing medical conditions, or drones and autonomous weaponry systems causing harm.
Furthermore, there are legal concerns around data protection and the right to privacy. The concepts of purpose specification and use limitation work to guarantee that personal data is only utilized for the purposes disclosed to individuals at the time of acquisition. But as algorithms are frequently opaque and difficult to comprehend, there are questions about whether the data used by AI systems can be said to serve these principles' goals.
Concerns concerning the fairness and transparency of AI tools—especially for disadvantaged populations—are also brought up by the lack of algorithmic openness. For instance, Beduschi (2020) contends that working-class families are disproportionately affected by prediction models that indicate the likelihood of child abuse.
The methods that allow computers to comprehend, manipulate, and express human language are at the center of the legal disputes surrounding natural language processing, or NLP. This has important ramifications for the legal industry and may have a variety of negative effects on society.
Large data sets are necessary for many AI algorithms to learn and forecast. This brings up significant legal issues with the protection and privacy of data, which companies need to handle by implementing policies and abiding by relevant regulations.
The lack of algorithmic transparency is another topic for worry. People might not be able to find out why they were placed on a no-fly list or denied a job, which could result in discrimination. Additionally, because people who develop AI may unintentionally include fundamental human biases into the software, it becomes more difficult to remove bias and discrimination from the technology.
Intellectual property law raises additional legal issues surrounding NLP, such as whether AI-generated content is protected by a patent or copyright. Since the legal systems in the US and other countries have only lately started to create effective frameworks for regulating these kinds of activities, this is a matter that needs to be carefully examined from a legal analytical perspective.
