Transparency
This principledemands clarity on how AI systems make decisions or reach conclusions.
For example, a credit scoring AI system should be transparent about the factors it uses to determine someone’s credit score. This means a user should be able to understand which financial behaviors are impacting their score, whether positively or negatively.
Privacy and security
This ensures thatthe personal data used by AI systems are protected and not misused.
For example, an AI-powered health app that tracks users’ physical activities and health metrics must safeguard this sensitive and personal information. The app should have robust security measures to prevent data breaches and should be clear about how it uses and shares user data.
Accountability
Thisprinciple is about taking responsibility for the outcomes of AI systems, including addressing any negative impacts.
For example, if an AI- powered news recommendation system inadvertently spreads fake news, the creators of the system must take responsibility. They should identify the failure in their algorithm, rectify the issue, and take steps to prevent such occurrences in the future.
Addressing LLM challenges with RAI principles
As discussed previously, there are three major challenges we face with LLM outputs: hallucinations, toxicity, and intellectual property issues. Now let’s double-click into each of these challenges and see how we can use RAI principles to address them.
Intellectual property issues (Transparency and Accountability)
The RAI principle that addresses intellectual property (IP) issues is referred to as “Transparency and Accountability.” This principle ensures that AI systems are transparent in their operations and that their creators and operators are accountable for their design and use. This includes the prevention of plagiarism and ensuring compliance with copyright laws.
Transparency involves the clear disclosure of the data sources, algorithms, and training methods used, which can have implications for IP rights.
For instance, if an AI system is trained on copyrighted materials or incorporates proprietary algorithms, it’s crucial to have proper permissions and to acknowledge these sources to avoid IP infringements. We believe new regulations will emerge in the upcoming years to prevent IP issues in generative AI applications.
Moreover, research is being carried out on ways to filter out or block responses that are very similar to protected content. For instance, if a user requests a generative AI to produce a narrative that is like a popular fantasy novel, the AI will analyze the request and either alter the output significantly to avoid direct similarities or deny the request altogether, ensuring it does not infringe on the novel’s intellectual property rights.
Machine unlearning is a relatively recent concept in the field of machine learning and artificial intelligence, which involves the ability to effectively remove specific data from a trained model’s knowledge without retraining it from scratch. This process is particularly relevant in the context of privacy and data protection, especially under regulations such as the GDPR, which advocates for the “right to be forgotten.” Traditional machine learning embeds the training data into a model’s parameters, making selective data removal challenging. Machine unlearning addresses this by developing methods to diminish or reverse the influence of certain data points on the model, thus allowing for compliance with privacy laws and providing greater flexibility in data management. However, implementing this efficiently without compromising the model’s performance is a complex and ongoing area of research.
