Aamir Mirza
Data Scientist and Data Engineer
In an era where generative artificial intelligence (GAI) promises transformative capabilities across diverse sectors, highly regulated industries stand at a unique crossroads.
Businesses in healthcare, finance, and aviation—which are bound by stringent rules and protocols— face the dual challenge of harnessing the potential of these AI models while ensuring they don’t compromise safety, privacy, or trust.
One of the Achilles heels of GAI is it can fabricate non-factual information; in the field of ML (machine learning), this phenomenon is termed ‘hallucination’.
While it can be seen as a pitfall, its implications largely depend on the application at hand.
For a story writer, the ability to generate unique and imaginative ideas can be seen as a boon, a tool to spark creativity. On the contrary, in sectors like regulated finance or banking, where accuracy, factualness, and reliability are paramount, such a trait is a significant drawback. In these domains, any deviation from the truth, even unintentionally, can lead to serious repercussions.
The utility of GAI is a double-edged sword; it’s all about how and where you wield it.
As the innovatory landscape flourishes, finding a harmonious balance between technological advancement and regulatory compliance becomes paramount. The path forward requires careful consideration, collaboration, and strategic implementation to leverage GAI’s benefits without incurring unwarranted risks.
The question remains how regulated industries take advantage of this new-age technology and make use of GAI to provide accurate and factual information to the end user.
Using reinforcement learning to improve relevance and factuality
Reinforcement learning is like teaching a dog tricks. The computer gets ‘treats’ for doing things correctly and learns from rewards and punishments to do its task better.
In essence, we reward the generative model when it produces a relevant and factual outcome and penalise the ML model when it does not. Through this carrot and stick approach, the AI eventually learns what acceptable behaviour is.
Examples of carrot and stick method for GAI
Guided generation
Traditional generative models, like Generative Adversarial Networks (GANs), already have a game-theoretic flavour with the generator and discriminator in a contest. Incorporating reinforcement learning can refine this contest, guiding the generator more intelligently based on rewards to produce better samples.
Sequence generation
In tasks where the generation is sequential, like text or music generation, reinforcement learning can guide the generative process at each step. For instance, using reinforcement learning in conjunction with models like RNNs (Recurrent Neural Networks) can improve the quality of generated sequences by rewarding coherent and contextually relevant outputs.
Customisable outputs
Reinforcement learning can be used to customise the generation process according to specific objectives. For example, in compliance, the generated output can provide new insight into an existing regulation; however, reinforcement learning ensures that the generated insight is still factually grounded.
Safe exploration
In highly regulated industries, GAI mustn’t produce harmful or non-compliant outputs. Reinforcement learning can guide the generative process by penalising undesirable outputs and encouraging compliant and safe generation.
Iterative improvement
As the generative model produces outputs, reinforcement learning can continually refine the model by leveraging feedback. Over time, this iterative process can lead to significant improvements in the quality and relevance of the generated content.
Dynamic environments
For applications where the GAI needs to adapt to changing environments or data distributions, reinforcement learning can provide a mechanism for the model to adjust its generation strategy based on the latest feedback, ensuring relevance and accuracy.
While generative models have shown great promise on their own, integrating them with reinforcement learning can further harness their potential, making them more precise, adaptive, and aligned with specific objectives. However, it’s essential to recognise that this combined approach also requires careful design and validation, especially in sensitive or regulated domains.
Retrieval-based augmentation
Imagine you’re baking a cake, but you only have half the ingredients you need. Instead of giving up, you decide to visit your neighbour’s house (an ‘external source’) to borrow the missing ingredients. By doing this, you’ve augmented your initial set of ingredients, making it possible to bake the cake.
GAI creates new, previously unseen content based on patterns it has learned from existing data. However, the challenge does lie in ensuring the generated content is factual and accurate. This is where ‘retrieval-based augmentation’ can help.
By integrating retrieval-based augmentation, generative AI can:
Reference verified data: Instead of just generating content based purely on patterns, the AI can fetch and reference facts or details from trusted external databases or sources. This acts as a ‘fact-check’ step.
Enhance diversity: If the AI’s training data lacks diversity or has biases, retrieving data from varied sources can introduce a broader range of accurate information.
Stay updated: Generative models might be trained on outdated data. By pulling in real-time or more recent data from external sources, the generated content can be more current.
Retrieval-based augmentation allows GAI to anchor its creations in real, verified data, ensuring a higher degree of accuracy and factual integrity.
Generative AI use in highly regulated industries
The integration of GAI into highly regulated industries presents both tremendous opportunities and significant challenges and has enormously transformative potential.
However, to harness its full potential, it is imperative that its inherent risks, especially in domains where accuracy and factualness are paramount, are addressed strategically.
Combining reinforcement learning with generative AI offers a robust approach to refine and guide the AI’s outputs, ensuring relevance, safety, and compliance.
Similarly, the incorporation of retrieval-based augmentation acts as a foundational pillar to anchor generative content in verifiable facts, enhancing accuracy and currentness.
While both methods provide robust solutions, they are not silver bullets. Stakeholders in regulated industries need to maintain a continuous feedback loop, ensuring that the AI models evolve and adapt to the ever-changing landscape of both technology and regulations.
Through rigorous validation, collaboration, and strategic implementation, the balance between innovation and regulation can be achieved, allowing industries to tap into the power of generative AI while preserving trust, safety, and compliance.
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