Legal Issues With Artificial Intelligence (AI)

Explore the top legal challenges of Artificial Intelligence (AI) in 2024, including inaccuracies, intellectual property disputes, data privacy, workplace bias, and regulatory hurdles. Learn how these issues impact businesses and the legal landscape.

The rapid evolution of Artificial Intelligence (AI) is reshaping multiple sectors, from healthcare to finance, and even the legal profession. However, alongside its promise, AI poses significant legal challenges. In 2024, these challenges have become more pronounced due to the technology’s increasing presence in critical areas. The legal issues with AI require a comprehensive understanding, especially for businesses and legal professionals. This article explores five pressing legal issues, including hallucination and inaccuracy, intellectual property concerns, data protection, bias in AI systems, and the current lack of laws to regulate AI.

Hallucinations and Inaccuracies in AI: A Legal Challenge

In the new world of artificial intelligence (AI)-powered automation, organizations face a troubling problem: AI systems can confidently generate convincing but incorrect knowledge, a phenomenon known as “hallucinations.”

As businesses increasingly rely on AI to drive decision-making, the hazards posed by faked outcomes are becoming more apparent. The AI systems that drive many of the newest technology tools that organizations are implementing, known as large language models (LLMs), are at the core of the problem.

Confident, but wrong.

Because of this reliance on probability, if the AI’s training data is wrong or the system misinterprets the intent of a query, it may give a confident but fundamentally incorrect response – a hallucination.

Businesses can suffer serious consequences if they rely on hallucinated information. Inaccurate outputs can result in poor decisions, financial losses, and reputational damage. There are also challenging questions about responsibility when AI systems are used. “If you remove a human from a process or if the human places its responsibility on the AI, who is going to be accountable or liable for the mistakes?” asked Fernandes. (PYMNTS Intelligence, 2024)

High Hallucination Rates in Legal Contexts

Recent research from Stanford demonstrates how common hallucinations are in AI, particularly in the legal industry. According to the study, hallucination rates range between 69% and 88% when AI models respond to specific legal queries, indicating a concerning level of inaccuracy. Legal systems, with their hierarchical structures and nuanced requirements, provide particular obstacles for AI. For example, in tasks involving the precedential relationship between cases, AI systems frequently fail, performing little better than random guessing. This raises severe concerns for legal professionals who rely on AI-generated legal research. (Stanford Law School, n.d.)

Intellectual Property Issues: Challenges Highlighted by the Spotify AI Case

Another significant legal challenge with AI in 2024 revolves around intellectual property (IP). AI’s ability to generate music, text, and visual art based on vast data sets raises critical questions regarding ownership and potential infringement. The recent Spotify case involving AI-generated music has sparked a debate about whether AI-generated works infringe on existing copyrights, and if so, who holds the responsibility.

Man Charged in AI-Generated Music Fraud on Spotify and Apple Music

In the first criminal case involving AI-generated music, a North Carolina man was charged with stealing royalties by employing AI to create bogus songs and listeners on streaming platforms.

In a September 4 complaint, US Attorney Damian Williams charged Michael Smith, 52, with devising a scheme to illegally receive $10 million in royalty payments from music streaming sites.

Smith is accused of employing AI to create hundreds of thousands of songs, which he then uploaded to various streaming services and fraudulently streamed using automated accounts known as bots. The platforms that were targeted were Amazon Music, Apple Music, Spotify, and YouTube Music. (Info security, 2024)

Data Protection and Privacy Issues when using AI

Organizations using personal information in AI may struggle to comply with the host of state, federal, and worldwide data protection requirements, including those that limit cross-border personal information transfers.

Some countries, particularly those in the EU, have comprehensive data protection rules that restrict AI and automated decision-making with personal information. Other countries, like the United States, lack a single, comprehensive federal legislation governing privacy and automated decision-making. This means that parties must be informed of all applicable sectoral and state regulations, including the California Privacy Rights Act of 2020.

Many of these data protection laws require organizations to limit the amount of personal information they collect about individuals, as well as requiring AI algorithms to be visible, explainable, fair, empirically sound, and accountable. (thomsonreuters, 2024)

AI in the Workplace

AI is becoming increasingly important in modern workplaces for human resource and personnel management responsibilities. All of this poses certain risks, including:

Recruiting and hiring — for example, generative AI screening and recruitment tools may discriminate against people with disabilities if, say, AI technologies assess an individual’s speech pattern in a recorded interview and afterward negatively evaluate someone with a speech or hearing problem.
Employee on boarding– such as AI systems that do background checks on employees, could violate employees’ privacy rights under numerous applicable regulations, such as biometric and password protection. (thomsonreuters, 2024)

Bias and Discriminatory Practices in AI

Bias in AI systems presents another major legal issue in 2024. AI models, trained on large datasets, can inadvertently adopt the biases present in the data, leading to discriminatory outcomes. In fields like employment, finance, and criminal justice, biased AI algorithms can perpetuate stereotypes and unfair practices, resulting in potential legal liabilities under anti-discrimination laws.

Aporia’s 2024 Report on Bias in AI

According to Aporia’s survey, 89% of machine learning engineers working with big language models observed bias in their output. These biases appear as content that reinforces societal stereotypes, which can have a negative impact on decision-making processes, particularly in sensitive areas such as hiring and financing. For example, AI-powered recruitment tools may bias against individuals based on gender or ethnicity, in violation of equal employment opportunity laws. (Unite.AI, 2024)

Legal implications of bias in AI systems

When AI systems are used to make crucial decisions such as hiring or financing, they must follow anti-discrimination laws. Bias in AI tools can result in lawsuits and legal disputes if it is discovered that these technologies promote discriminatory practices. Companies that use AI must develop procedures to constantly audit and monitor their systems for bias, guaranteeing compliance with anti-discrimination laws. (Unite.AI, 2024)

Challenges of Navigating AI Regulatory Frameworks in Healthcare

There are several major concerns with AI in healthcare. One important concern is the fragmented regulatory landscape, especially in the EU. Regulations such as the Medical Device Regulation (MDR), General Data Protection Regulation (GDPR), and the planned EU AI Act overlap and frequently vary in their definitions of “compliant AI technology.” This makes it tough for healthcare companies to manage compliance, especially when launching innovative AI-powered medical equipment.

Lack of integrated regulation

Another concern is the lack of integrated regulation. While the EU takes a cross-sectoral approach, the United States controls AI using sector-specific regulations like HIPAA and FDA guidelines. This sectoralism promotes flexibility and adaptability, but it may overlook areas beyond traditional healthcare, such as data from wearable devices. The variations between these techniques complicate compliance for global businesses. (SLS, 2024)

Rapid technological change

Furthermore, AI healthcare developers have to manage rapid technical development, such as adopting algorithms to real-world application while regulating trade controls on physical components. The rate of AI development is outpacing present regulatory frameworks, creating concerns that regulations will fast become outdated. (SLS, 2024)

How to Address the Legal Challenges of AI in 2024

The rapid expansion of artificial intelligence across various sectors brings significant legal challenges. Issues such as hallucinations and inaccuracies, intellectual property disputes, data protection and privacy concerns, bias, and a fragmented regulatory landscape make compliance increasingly complex.

It’s crucial for businesses and legal professionals to understand these evolving concerns. Addressing these issues is vital for ethical and legal progress. Moving forward, a unified approach is necessary to resolve these challenges and ensure responsible use of AI.