LLM Risks for Application Development

As large language models (LLMs) like ChatGPT, Llama, Gemini, Claude (just to name a few) continue to advance and become more powerful and intelligent, they are currently being used to build various applications such as chatbots and virtual assistants to content generation tools and code autocompletion.

While LLMs offer incredible capabilities, it's important to understand the potential risks involved in using them to develop applications.

One of the biggest risks of using LLMs is the potential for bias and toxicity in their outputs. These models are trained on vast datasets scraped from the internet, which can include biased, offensive, or factually incorrect content. If not properly filtered and curated properly or if not properly trained with a diverse set of factual data, this "data bias" can manifest in the model's generations in the form of discrimination, racist, sexist, or otherwise harmful language and perspectives. Deploying applications built with biased LLMs could propagate harm and discrimination, have a negative impact on your customers and render your application inappropriate for public use.

AI-enabled applications with bias and toxicity in the data are no different to applications with security vulnerabilities. Customers will eventually shy away from biased and toxic AI-enabled applications.

While Generative LLMs are incredibly fluent at generating human-like text, images, videos and speech, their information isn't necessarily grounded in truth or facts. LLMs can confidently state falsehoods, makeup statistics, or misrepresent information based on what patterns they've picked up in their training data. Applications relying on LLM outputs as a key information source run the risk of disseminating misinformation.

Another major risk is the potential for LLMs to inadvertently expose sensitive information in their outputs based on what data they were trained on. There have been instances of LLMs like GPT-3 generating outputs containing personal names, phone numbers, addresses, and other private data from their training sets. If this private information makes its way into application outputs, it could lead to data privacy violations and breaches.

While LLMs can produce highly coherent and contextually relevant text, images, video and speech, their outputs can often be inconsistent across different prompts and generations. The same prompt could receive wildly different responses each time it is given to an LLM, making the results unreliable for applications requiring stable and predictable performance.

A core challenge with LLMs is that they are essentially opaque black boxes that provide little insight into how they arrive at their outputs. For those building applications with LLMs, it can be difficult to fully understand, validate, explain and maintain oversight over the model's reasoning process, making it harder to prevent harmful or undesirable behaviours from emerging. The "Black Box" in LLM's using Deep Learning Methods to train their models is a risk and concern because Humans cannot explain how the output was derived.

The challenge with LLMs is that they contain billions of parameters and require immense computational resources to run. Efficiently scaling and deploying LLM-powered applications cost-effectively and sustainably presents major engineering challenges, especially for resource-constrained organizations.

Finally, building applications that rely on LLMs raises broader ethical questions around issues like automation and job displacement, intellectual property, consent over training data, and centralization of AI capabilities among a few big tech companies. Using LLMs could contribute to the concentration of power and lack of public accountability.

While large language models offer immense potential for building powerful applications, developers must carefully weigh and manage these risks. Some possible risk mitigation strategies include but not limited to:

As LLMs continue advancing, developers will need to prioritize responsible development practices and security to harness their capabilities while mitigating potential harm. Open communication and collaborations between AI labs, tech companies, academia, and policymakers will be key for building safe, responsible and trustworthy LLM-powered applications.

IRM Consulting & Advisory provides Strategic and Tactical Advice on how to mitigate risks associated with using AI to build responsible applications, products and services.

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