Getty Images
By Kate Woolley
2023 will go down in history as a year of important AI experimentation, but now the real excitement begins. We are seeing AI hype mature into something more substantial — where the technology is integrated into the everyday of the enterprise, streamlining business functions and automating repetitive tasks to drive business transformation.
While most companies already recognize the benefits of AI, they still have valid concerns about how to address potential biases, privacy violations, and disinformation. These doubts are driving them to proceed with caution, while also seeking expert guidance on how to navigate the potential risks of AI.
This is why implementing an AI governance strategy should be step one when planning to embed and scale AI across an enterprise. Companies must first ensure their AI will be trustworthy, sustainable, and accessible. They need to be able to track and monitor their AI model, explain what data is being used and why it is making certain decisions, and adjust to new rules and regulations as they come into effect.
Trustworthy AI demands a strong foundation
Generative AI is new for many companies and issues such as explainability, fairness, robustness, transparency, and privacy. These are the inherent challenges that arise from the human element in AI creation and maintenance that could open a company up to audits, fines, and reputational damage.
To mitigate such challenges, organizations need to establish an AI governance foundation that factors in the following three imperatives:
1. Getting rid of the AI black box: Model transparency is paramount, so companies need to capture all inputs (human and otherwise) and outputs in terms of behavior and performance, which will aid in informing and accelerating decision-making. Now, with more AI models becoming available, it's important that organizations have complete visibility into the data and effectively manage, monitor, and govern these models - whether from open-source communities or the various model providers.
2. Turning compliance into an advantage: Meeting regulations is table stakes for doing business, no matter how complex local or global laws may be. With the proper processes and technology in place, the opportunity exists to check all those boxes while also having a head start to address the next wave of regulations as they come. Successful approaches translate AI regulations into policies for automated enforcement and incorporate dashboards to track compliance across policies and regulations.
3. Staying ahead of risks before they become issues: Critical to AI success is taking steps to proactively detect and mitigate risk, which includes monitoring fairness, bias, drift, and metrics surrounding the use of large language models. Automation is key here to establish thresholds and alerts to scale visibility of status and enhance collaboration across geographies.
That's a lot for a company to consider. It is also where trusted partners can help. Whether providing generative AI service offerings, or selling and building AI solutions for clients, such partners have already helped numerous companies on their AI journey, and, in the process, have been developing deep expertise to help clients establish an AI governance strategy. Their unique access to the right AI technology and skills, coupled with hands-on experience putting it to work across industries at scale, make them an invaluable resource.
We're all excited by AI's potential, but if the foundation isn't laid correctly, it will be hard to retrofit the technology to meet essential requirements of explainability, fairness, robustness, transparency, and privacy. On the flip side, once this foundation is in place, enterprises will confidently accelerate their use of this game-changing technology to transform all aspects of their business.
Learn how IBM can help your organization implement trustworthy AI.
This post was created by IBM with Insider Studios.