The adoption of third-party models introduces many risks to both the organization and its users, necessitating stringent validation and ongoing monitoring. But do organizations possess the right tools to effectively mitigate these risks and validate the performance of these solutions?
Amidst the global wave of AI regulations, a concerning trend emerges: the rise of third-party AI models. As regulatory frameworks solidify, these mysterious algorithms pose an escalating threat to organizations, risking hefty fines for non-compliance.
Many businesses opt for third-party AI tools over internally developed models, due to the allure of flexibility and competitive edge. Yet, this convenience comes with its own set of challenges. Third-party models demand rigorous oversight to ensure alignment with evolving regulatory standards.
Risks of Third-Party AI
It’s easy to see why many organizations in various sectors are turning towards third-party AI models to keep pace in their industries. Not only do “home-grown” models take significant time and effort to develop and deploy, but some organizations don’t have the necessary internal capabilities or IT infrastructure. Third-party AI is more cost-effective and convenient, allows organizations to focus on their core business, and gives them access to tools and services that are not available in-house.
While there are many benefits, third-party AI tools expose organizations to various hazards, such as reputational damage, financial losses, regulatory challenges, and lawsuits. It can be challenging to assess and evaluate the risks associated with third-party AI tools, and many organizations fail to evaluate them.
Mitigating Third-Party Risks
Now that you understand third-party AI comes with inherent risks, how can you mitigate the problem? When organizations adopt third-party AI systems, having the tools to validate these models is critical. Validation can help identify and address biases, errors, and unintended consequences as well as ensure you are meeting legal and regulatory requirements.
How to Validate a Third-Party Model
Citrusˣ’s solution can better assess and address the risks associated with third-party AI tools by employing diverse evaluation approaches to give you a better sense of the third-party model’s capabilities, limitations, and vulnerabilities.
Validation can help identify and address biases, errors, and unintended consequences as well as ensure you are meeting legal and regulatory requirements.
Citrusˣ, an end-to-end risk management platform, helps companies compare models, mitigate biases and vulnerabilities, and monitor the model in production while complying with regulations.
Citrusˣ's rigorous validation process ensures accurate, reliable, and bias-free models. Using various performance and proprietary robustness metrics to identify problems on a local, global, and cohort level, we provide you with an understanding and clear interpretation of how your model is working on a broader level.
Regulations Change the Game
Addressing the risks associated with AI use, including those arising from third-party AI tools, is crucial because the regulatory landscape is rapidly evolving alongside the technology. New regulations targeting AI are being implemented at various levels, including national, state, and local.
For instance, in the EU, the AI Act will impose strict requirements on AI systems considered "high risk," including general-purpose systems like AI chatbots, and hold vendors accountable for any harm caused to consumers.
Regulatory bodies worldwide are also evaluating the compliance of AI tools with existing laws. Additionally, even without specific AI regulations, existing laws such as consumer protection, non-discrimination, and data protection laws apply to AI use.
Be Proactive with Third-Party AI
Validating third-party AI models and mitigating risks is essential for organizations to navigate the evolving regulatory landscapes and ensure organizational resilience. By leveraging solutions like Citrusˣ and staying ahead of regulatory changes, organizations can reduce risks and foster trust in their AI-driven decision-making processes when opting to use third-party tools.
If you want to learn more about how we can help you comply with regulations and reduce risks for your third-party AI model, book a demo with our team!
Comments