We have all heard the buzz about generative AI and its poised to be a game changer. Is it truly going to skyrocket our businesses and productivity? With Large Language Models such as Open AI Chat GPT, Google Bard, and many more LLMs, the future of artificial intelligence and automation is auspicious. After all, generative AI can potentially revolutionize how we work, live, analyze, and interact. This buzz has resulted in an increased sense of urgency among business leaders to harness this transformative AI into their work or be left behind in the race. I hate to be a buzzkill, but is it as golden as it looks? In this blog, let us understand how we can foster responsible AI in the age of LLMs with apt AI governance in place.
Gen AI comes with a lot of power and responsibility
As Uncle Ben in Spiderman had quoted,” With great power comes great responsibility.” the same applies in the case of harnessing generative AI as well. Gen AI has immense advantages, such as instant churning out texts and images, giving enterprises analytical insights, and streamlining and optimizing processes – which are all valuable in this fast-paced, competitive world fed by innovation. However, there are certain pitfalls that enterprises need to acknowledge and address before charting out the path of Gen AI.
Jumping on the AI bandwagon
According to recent research, 67% of senior IT leaders focus on incorporating Gen AI into their business strategies within the next 18 months, with 33% considering it their top priority. However, at the same time, most of these senior IT leaders have apprehensions about the potential implications. 59% of them believe that the outputs are inaccurate, and 79% are concerned about security.
What can possibly go wrong?
Training data of LLM matters
Many of the issues with Artificial Intelligence stem from poor training data. If the data set that the LLM is trained on is inaccurate or has biases, the results will inevitably mirror them. Moreover, humans are gullible most of the time, and when AI answers with confidence, we tend to accept it without question. For instance, if Gen AI says Henry the Eighth was the king who beheaded all his wives, we accept that answer since we don’t know the exact history. Instead, we believe that it is invariably correct since Gen AI knows and has been trained on the right data sets. This is a fundamental issue with computers that even the founding mainframe programmers had realized early when they coined the acronym GIGO, meaning ‘garbage in, garbage out.’
Critical AI-based decision-making based on poor training data
Similarly, there can be catastrophic consequences too. Who will be accountable if an enterprise makes decisions based on AI-generated data that has resulted in heavy financial losses? In critical sectors such as finance or healthcare, if enterprises rely solely on AI-driven decision-making that goes unchecked or backed by poor data quality, it can be a recipe for disaster.
IP loss, bias, data privacy and security
Artificial Intelligence, including large language models (LLM), poses several challenges that enterprises must carefully consider before integrating them or broadly deploying them within the organization. Concerns such as data privacy, intellectual property loss, bias, security, and an array of other issues loom for organizations that jump on the generative AI bandwagon pushed by their boards and C-suites.
A new issue has emerged from LLMs- that is, model collapse. It is a very genuine and serious threat that researchers warn unsuspecting AI victims about. In simple terms, when AI models are trained on AI-generated data- model collapse is most likely to happen due to the surging amount of AI-generated content being produced through the Large Language Models(LLM)
Let us consider the analogy of photocopying. The first copy is mostly accurate and of decent quality. However, as you replicate a copy multiple times, the result becomes a fuzzy, unrecognizable mess. That is similar to model collapse. The AI model loses its essence, prioritizes higher probability outcomes, and decreases or even erases unlikely events. This completely nullifies the model’s original data distribution and intent, making the model no longer useful.
AI Governance is of utmost importance
As we saw in the above circumstances, it is clear that the quality and integrity of the data that is used to train the AI model is a crucial factor. Equally important are the policies and standards applied to govern this data.
Having AI Governance processes in place
Processes that aim towards ensuring model assessment and benchmarking are also pivotal for each Large Language Model. These processes enable us to identify potential problems and expedite necessary corrective actions when required. Moreover, model users should be able to flag issues, such as biased results, policy infringements, wrong responses, performance discrepancies, etc. Individuals should be assigned to the model responsible for resolving these concerns following predefined protocols.
Human-in-the-loop (HITL) is necessary
Complete reliance on the AI model as a result is not advisable. Organizations should adopt a ‘human-in-the-loop’ (HITL) approach, where humans review and validate the outcomes before integrating them into business operations or making critical business decisions based on them. This HITL approach guarantees accuracy and mitigates potential risks related to sole reliance on AI-generated outputs.
How to implement AI Governance?
AI models are essentially data-driven products aiming to deliver value, boost productivity, and provide a competitive advantage. What is the point of investing in AI models if they don’t fulfill these objectives? They require constant nurturing, validation, and ongoing updates to stay effective. Proper governance is paramount to ensure their optimal performance.
Making AI governance a foundational element of an organization’s AI strategy is essential. Since AI runs on data, it should be integral to your broader data governance initiative. Enterprises can follow a simple yet efficient AI governance framework.
Start with defining your use case
What will be the model’s purpose? What data will power the model- whether it is human-generated or AI-generated? Addressing these questions is crucial to define the results, evaluate the risks, and assign ownership and responsibility.
Identifying and understanding data
The ability to validate and trust the data upfront puts you on a path toward success and minimizes the likelihood of issues such as model collapse. This process also checks whether the use of the data in connection with the use case is legally permissible,
Document and track the model and its results
Documenting the model’s results is vital to understanding the output, mainly to check if the results are logical and unbiased. Collecting this data will support model analysis and reporting and achieve the results required for the use case. It can also help you track and report to regulators if required.
Verify and monitor your model continuously
AI governance is not a single-time project; rather, it is an ongoing process. Regularly assessing outcomes, updating data for retraining, and striving for continuous improvement of the model are part of the process. With this framework, enterprises can mitigate the risks related to AI and experience fast and increased value.
Data intelligence is key to ensuring governance
Documentation, evaluation, and monitoring of the data products that fuel the models form the foundation of AI governance. Data intelligence capabilities play a crucial role at every stage of this framework. Since data drives AI, having AI governance within the broader enterprise data governance plan is essential.
Responsible AI – now and gearing up for the times ahead
Responsible AI should encompass more than just assuring privacy, safety, and fairness- it should explicitly ensure the highest data quality within the model and prevent issues like model collapse without any exceptions. The role of human interference in responsible AI is paramount, as model accuracy and ethical consideration will always require human oversight. The final decision regarding when and how to use AI will always be a human’s call.
If data is the gold, then human-made data will most likely be the diamonds everyone will pursue. At present, the norm is still human-created data sources, but in the future, AI-generated data may dominate the landscape. Taking proactive steps to implement AI governance and understanding your data’s nuances will ultimately make the difference between success and failure in the times ahead.