The rapid advancement of artificial intelligence (AI) is transforming industries. As organizations increasingly leverage AI systems, a critical question emerges: Who is accountable when things go wrong, whether it's an ethical oversight, a data privacy breach, or an intellectual property (IP) infringement? We can learn from cloud computing’s Shared Responsibility Model.
Just as the cloud redefined IT infrastructure, AI is reshaping how we think about data, algorithms and applications. The well-established Cloud Shared Responsibility Model, which defines duties between Cloud Service Providers (CSPs) and their customers, provides a highly relevant blueprint for understanding accountability in the AI ecosystem.
Understanding the Layers of Responsibility in AI
To grasp the AI Shared Responsibility Model, let's break down the key players and their roles, drawing parallels to a familiar scenario:
1. The AI Model Provider (e.g., OpenAI, Google Gemini): The “Car Manufacturer”
These are the creators and maintainers of the foundational AI models and the underlying infrastructure, typically the “Provider” role under the EU AI Act. They build the core engine and set its fundamental capabilities and limitations. From design to production, they oversee the entire lifecycle of the AI models. Key responsibilities are:
- Security of the AI Model and Infrastructure: Ensuring the security of their data centers, APIs and the core algorithms themselves. For the infrastructure part, if they are using a Cloud Service Provider (CSP), the cloud shared responsibility model will apply. The AI model part includes security protocols for model training environments and safeguarding against external attacks on their services, alongside strong AI governance throughout lifecycle development, implemented guardrails and rigorous security testing to ensure robustness.
- Ethical AI Principles: Ethical AI principles, such as transparency, fairness, and privacy, should be applied when developing AI models, and these principles are embedded in many aspects. The OECD AI Principles are adopted globally.
- Terms of Use & Disclaimers: Setting the broad terms for the models that can be used per “fit for purpose,” often including disclaimers of warranty regarding input limitation and output accuracy.
- Data Handling: Ensuring that when providers train their model, the training data collected and used comply with legal and ethical standards.
Just like car manufacturers design and build the car, they must ensure the core components of the car are safe and functional, and provide an owner’s manual.
2. The AI Platform (e.g., a SaaS AI application, an AI development platform): The “Car Dealership/Rental Company”
Imagine your platform as a car dealership that sells or rents vehicles built by a major manufacturer.
Your responsibility now works on two sides:
- The Manufacturer's Side (Upstream): You ensure the cars you sell are well-maintained and that they adhere to the manufacturer’s warranty.
- The Customer's Side (Downstream): You set the terms of the rental agreement for your customers (e.g., no off-roading, return fueled). You also provide extra features, like GPS or child car seats, to improve safety and the customer experience.
Just like a dealership, you are responsible for ensuring that the original product and the service you build around it are both secure and reliable.
Adherence to Upstream Model Providers’ Terms: Since you are a customer of the AI model provider, you must comply with their Terms of Service, Acceptable Use Policies (AUPs), and any other agreements. This also includes how to ensure your customers’ use doesn't violate these upstream terms.
How do you do that? You need to manage the security in your platform. This includes securing your application layer, managing user access to your platform, implementing security measures on your platform to protect customer data, and properly configuring your platform and services. For those that act as an integrator, you need to implement mechanisms such as observabilities, content filtering, usage monitoring, audit logging, and tracking that align with ethical AI principles, support explainability, and prevent misuse of the underlying models by your customers.
Responsibilities to Your Downstream Customers:
As an AI platform, you are responsible for flowing down obligations from the model provider to your customers. This means providing clear terms of service that translate the provider's requirements into language your users can easily understand. For example, if the model provider’s terms state that user data may be used for training, you must inform your customers of this and provide a clear way for them to opt out.
In addition to these flow-down obligations, you must also offer features and configurable settings that enable customers to use the AI responsibly. Your platform should empower users to exercise their rights, such as opting out of data usage for model training.
Your responsibility also extends to customer-generated training data. Think of this as the “maintenance log” created by a driver's use of a car—their driving habits and performance data are similar to the customer-generated training data. If your platform uses this data to fine-tune or improve AI models, you must have a clear policy on how it is used, stored, and protected. This includes getting explicit consent from customers for data usage and providing a simple mechanism for them to opt out. Furthermore, you must ensure that any data used to train your models is ethically sourced and properly handled to prevent biases and protect user privacy.
Just as a dealership offers inspection and maintenance services, providing continuous support and guidance helps your customers understand how to use AI tools ethically and compliantly.
3. The Customer/End-User: The "Driver"
The customer is the direct operator of the AI system via your platform. They provide the input data, define the specific use case and interpret/utilize the AI's output.
As end-users, customers must control their input to the AI systems and validate the output from the AI systems. They need to ensure their training data, input prompts and any other data they bring to the platform comply with all applicable intellectual property laws, and privacy regulations (e.g., GDPR, CCPA), and use AI responsibly and ethically.
They also need to take responsibility for the output generated by the AI model. This is the typical Human-in-the-Loop (HITL) control that requires human judgment and oversight over AI-generated content, especially in high-stakes applications. Such controls include verifying output accuracy, performing fact-checks, making sure it doesn't infringe on IP, and avoiding the generation of harmful, illegal, or discriminatory content, as outlined in your platform's AUPs and the underlying model providers' terms.
Just like the driver is ultimately responsible for how they operate the car. They must follow traffic laws, ensure the passengers are safe, and use the car for legal purposes. If they speed or cause an accident, the responsibility is theirs.
Why the Shared Responsibility Model is Helpful for AI
- Clarity and Risk Mitigation: It defines who is responsible for what, reducing ambiguity and helping to mitigate legal, ethical, and security risks, and protecting against threats for all parties involved.
- Trust and Compliance: In an environment of evolving AI regulations (e.g., EU AI Act, various data privacy laws), understanding these layers of responsibility is crucial for demonstrating compliance and adopting a proactive approach to responsible AI development and deployment. It fosters greater trust among users, providers, and regulators.
- Operational Efficiency: By defining roles, organizations can allocate resources effectively, ensuring that security, privacy, ethical guidelines, and IP considerations are addressed at every layer of the AI stack.
AI Shared Responsibility Model: A Foundational Building Block
The AI Shared Responsibility Model is more than a legal exercise; it's a foundational building block for developing and operating AI systems responsibly. As AI capabilities advance, a clear understanding of this model will help with navigating the complex landscape of innovation, ensuring compliance, and fostering a trustworthy AI ecosystem. For platforms that host AI, this means thoroughly reviewing upstream model provider terms, creating strong customer agreements and implementing technical safeguards that empower customers to be responsible "drivers" of AI.
About the author: ShanShan Pa is a compliance and governance leader specializing in AI governance, data privacy, and security, helping global organizations align emerging technologies with regulatory and ethical standards.
