Introduction
While Large Language Models (LLMs) represent significant progress in artificial intelligence, they come with inherent risks that businesses must address.
Key concerns include generating incorrect or fabricated information—known as “hallucinations”—producing biased or inconsistent outputs, leaking sensitive data, creating inappropriate or copyrighted content, and being vulnerable to security attacks.
Hallucinations: How to Mitigate Factually Incorrect Outputs
A notable quirk of LLMs is their tendency to “hallucinate”, in other words – generate text that appears plausible, but is actually incorrect and misleading. This happens because LLMs predict words based on patterns in the data they were trained on, not by accessing real-time facts or understanding the content deeply.
For instance, in 2023, a lawyer relied on ChatGPT to prepare a legal brief, which cited court cases that didn’t exist. The AI had fabricated case law, leading to professional embarrassment and potential legal consequences.
- Always verify critical information generated by LLMs with authoritative databases or trusted references. Implement automated systems that flag and compare AI outputs with existing records.
- Crafting precise questions or instructions can guide the AI toward generating more accurate responses. Vague prompts often lead to ambiguous or incorrect answers.
- Have subject matter experts review AI-generated content, especially when used for decision-making or public dissemination.
- Integrate external fact-checking APIs or software that can analyze the AI’s output for inaccuracies before it reaches the end-user.
- Configure the AI to express uncertainty when it is unsure about an answer. This can prompt users to treat the information cautiously and seek verification.
Note: The likelihood of hallucinations increases as the input length grows. Always manually verify AI-generated content, regardless of its complexity, to ensure accuracy and reliability.
Addressing Bias in LLM Outputs
LLMs learn from vast datasets that often contain societal biases related to race, gender, age, and other attributes. Part of the challenge stems from the fact that LLMs are trained on massive text datasets, often sourced from web scraping. Due to the sheer volume of this data, it becomes impossible to thoroughly filter or verify all the information. While there are systems designed to prevent this, even slightly inaccurate or biased data within the training set can significantly impact the model’s output.
A study by the Montreal AI Ethics Institute revealed that LLMs used for job recommendation systems exhibited demographic biases. The AI was more likely to suggest prestigious job opportunities to certain demographic groups while overlooking others, perpetuating inequality and limiting opportunities for underrepresented populations.
- Use datasets that are representative of various demographics and perspectives to minimize inherent biases. During the fine-tuning phase, models from larger AI labs can be adjusted with data tailored to specific use cases and demographics, making this approach accessible even for smaller enterprises.
- Periodically evaluate AI outputs to detect biased responses. Testing the AI with a wide range of inputs can help ensure it treats all groups fairly.
- Apply algorithms designed to reduce bias during the training process, such as reweighting data or incorporating fairness constraints.
- Include team members from different backgrounds in the AI development process to provide broader perspectives on potential biases.
- Develop and enforce policies for ethical AI usage within the organization, ensuring compliance with legal standards and promoting fairness.
Note: Certain standards are always to be defined. The broader the scope of the product, the more complex a solution will get and more filters will be required.
Avoiding Toxic, Harmful, or Inappropriate Content
LLMs can sometimes generate outputs that are offensive, harmful, or inappropriate. The issue is especially noteworthy if you’re thinking about adopting a chatbot since an AI might produce language that is discriminatory or abusive.
- Use moderation tools to detect and block offensive or inappropriate language before it reaches the end-user.
- Retrain the AI using curated datasets that exclude harmful content, reducing the likelihood of undesirable outputs. This is typically done during the fine-tuning phase, as training models from scratch is prohibitively expensive for most organizations—especially with LLMs. However, smaller models can still be trained fully in-house from scratch with tailored data.
- Define clear guidelines for acceptable AI behavior and ensure that these policies are enforced within the model.
- Continuously review the AI’s responses to identify and address any problematic content promptly.
- Enable mechanisms for users to report inappropriate content, facilitating quick action to rectify issues.
Facing challenges with LLMs, AI compliance, or security? Let our team guide you through implementing safe, reliable, and compliant AI solutions for your business.
Avoiding Copyright Infringement
LLMs can inadvertently generate content that infringes on copyrights or includes inappropriate material. Trained on vast amounts of internet data, these models might reproduce copyrighted texts or generate content that is unsuitable for certain audiences.
For example, New York times has sued OpenAI in 2023, citing multiple cases where ChatGPT had responded to users with NYT articles nearly verbatim.”In its complaint against OpenAI, the Times asks not only for monetary damages and a permanent injunction against further infringement but also for “destruction…of all GPT or other LLM models and training sets that incorporate Times Works.”
- Use moderation tools to detect and block the generation of copyrighted or inappropriate content before it reaches users.
- Train AI models on datasets that you have the rights to use, ensuring compliance with intellectual property laws.
- Define acceptable content standards for your AI and configure the model to adhere to these guidelines.
- Work with legal professionals to understand and navigate intellectual property regulations relevant to your industry.
Note: In some regions, AI-generated content may not be seen as copyright infringement, but it raises legal issues in others. Notably, companies like Getty Images and Shutterstock are developing their own image generators and suing those who used their licensed images for AI training.
Protecting Sensitive Information: How to Prevent Data Leakage
LLMs can inadvertently expose confidential or sensitive information, posing significant risks to businesses. This can occur when the models retain data from user inputs or when they’re trained on proprietary information without proper safeguards.
- Instruct employees not to input confidential information into AI models, especially those hosted on external servers or using free services, as the data is often used for training purposes.
- Remove or obfuscate personally identifiable information (PII) from datasets before training or using the model, especially as LLMs can regenerate the data if certain questions are asked.
- Encrypt data transmissions to and from the AI to protect information during processing.
- Limit who can interact with the AI model, especially when dealing with sensitive tasks or data.
- Consider using locally hosted models instead of cloud-based services to maintain greater control over data.
- Develop guidelines for AI usage and train staff on best practices for data security.
- Continuously monitor AI interactions and perform security audits to detect and address potential vulnerabilities.
Improving Security to Prevent Vulnerabilities
LLMs are vulnerable to various security threats, including prompt injection attacks, data poisoning, and unauthorized access. These vulnerabilities can be exploited to manipulate the model’s behavior or extract sensitive information, posing significant risks to businesses.
In a prompt injection attack, an adversary crafts inputs that cause the AI to generate unintended outputs or reveal confidential data. This type of attack, also known as “jailbreaking” the system, can make models generate offensive statements, fabricate scenarios, and produce harmful outputs. While most large AI providers have implemented filters to reduce these risks, vulnerabilities can still arise.
Data poisoning involves injecting malicious data during the training process, leading the model to learn and reproduce harmful behaviors.
- Restrict who can interact with your AI systems using robust authentication and authorization mechanisms. This limits exposure to potential attackers.
- Ensure that all user inputs are properly validated to prevent injection attacks. Reject or neutralize inputs that contain suspicious patterns or code.
- Continuously monitor AI interactions for unusual patterns or anomalies that may indicate a security breach. Maintain detailed logs to audit and analyze activities.
- Vet and curate training data meticulously to avoid incorporating malicious or biased information. Employ techniques to detect and eliminate poisoned data.
- Keep your AI models and underlying systems up to date with the latest security patches to protect against known vulnerabilities.
- Perform regular security assessments and penetration testing to identify and remediate vulnerabilities in your AI infrastructure.
- Use encryption for data at rest and in transit to protect sensitive information from interception or unauthorized access.
- Develop and maintain a response strategy for potential security incidents involving AI systems to minimize impact.
From prompt injections to data poisoning, we offer end-to-end security solutions to safeguard your AI infrastructure.
Bonus section: OpenAI o1 and EU’s First AI Act: changing the LLM game
The EU’s First AI Act
The EU’s AI Act establishes a legal framework for artificial intelligence, aiming to ensure that AI systems are safe, transparent, and respect fundamental rights. This legislation introduces specific obligations for providers and users of AI systems, including LLMs. Key Aspects of the EU AI Act- Risk-Based Classification: The Act categorizes AI systems into different risk levels—unacceptable, high, limited, and minimal. High-risk AI systems, such as those used in critical infrastructure or employment decisions, are subject to stringent requirements.
- Transparency Requirements: Providers must inform users when they are interacting with an AI system.
- Data Governance: Companies are required to use high-quality, representative datasets for training AI models.
- Human Oversight: The legislation mandates that AI systems, especially those deemed high-risk, be designed to allow human intervention.
Implications for Companies Using LLMs
Businesses integrating LLMs must carefully navigate the EU’s new regulatory environment to avoid fines and legal challenges. Key steps include conducting compliance assessments, ensuring data accuracy and governance, maintaining transparency with users, and designing systems with human oversight, especially for high-risk applications. Staying informed on regulations and seeking legal advice is essential. Most companies will need support from AI experts to manage these steps, particularly as AI usage grows and legacy companies face IT staff shortages.Every organization will encounter unique and nuanced risks that need a methodical strategy to ensure AI initiatives align with your business objectives and risk tolerance.
Let us guide you through the complexities of AI integration, so you can leverage advanced technology while minimizing risks.