AI Guardrail 

What is an AI Guardrail?

An AI guardrail is a safeguard that is put in place to prevent artificial intelligence (AI) from causing harm. AI guardrails are a lot like highway guardrails – they are both created to keep people safe and guide positive outcomes.

As AI evolves, guardrails are becoming increasingly important for maintaining public trust in AI and ensuring that AI-enabled technology operates safely within ethical and legal boundaries.

Creating AI guardrails that everyone agrees with is challenging, however, due to the rapid pace of technological advancement, different legal systems around the world, and the difficulty of balancing AI innovation with the need for privacy, fairness, and public safety.

Why Do We Need AI Guardrails?

AI guardrails are a critical component of AI governance and the development, deployment and use of responsible AI.

In the past year, guardrails have often been mentioned in the context of generative AI, but it’s important to remember that safeguards are vital considerations for any type of AI system that can make a decision autonomously. 

This includes relatively simple machine learning (ML) algorithms that decide between two choices, as well as multimodal AI systems whose decisions can have literally billions of potential outcomes.

As the world has seen, when guardrails don’t exist, AI technology can perpetuate biases, create new concerns about privacy, make erroneous or unethical decisions that directly impact people’s lives, and get misused for harmful purposes. 

It’s no surprise that this, in turn, has led many people to mistrust AI. 

Who Is Responsible For Creating AI Guardrails?

The creation and implementation of AI guardrails is a collaborative effort that involves a diverse group of stakeholders. This includes:

• Big Tech companies and AI startups;
• Business leaders;
• AI researchers;
• Civic organizations;
• Ethicists;
• Professional organizations;
• Government agencies;
• Prominent global organizations;
• Legal experts. 

Each of these stakeholders brings a unique perspective and skill set to the table, which in theory, should contribute to a more holistic approach to the development of guardrails for specific types of artificial intelligence.

The challenge is that when stakeholder interests are too diverse, it can be difficult to reconcile competing priorities and find a balance that everyone can live with. Too many guardrails can stifle innovation, and too few guardrails can leave the door open for harmful consequences that undermine safety and public trust.

Types of AI Guardrails

AI guardrails can be implemented with technical controls, policies and laws. 

Technical controls are embedded within the AI itself. In contrast, policies are internal or external guidelines, and laws are enforceable regulations enacted by governments.

Each type of guardrail plays an important role in ensuring responsible AI development and deployment, and their combined strength lies in their complementary nature.

Technical Controls

Guardrails that are implemented as technical controls are embedded directly into AI workflows as operational processes that become an integral part of how the AI functions on a day-to-day basis. This type of guardrail includes:

• Watermarks for AI-generated content;
• Validation tests that can verify a complex AI system behaves as intended;
• Business rules that determine how simple AI systems behave in certain scenarios;
• Feedback mechanisms that allow users to report errors;
• Security guidelines and protocols designed to protect AI system components from cyberattacks and misuse;

Policy-Based Guardrails

Unlike technical guardrails, policy-based guardrails are not incorporated into workflows. Instead, their impact can be observed in how AI workflows are designed and managed. 

Policy guardrails are often specific to an organization or industry and can vary widely in terms of scope and detail. This type of guardrail includes:

• Guidelines for how training data should be collected, stored, and shared;
• Best practices for addressing ethical AI concerns like fairness and accountability;
• Frameworks designed to ensure AI systems comply with security or industry-specific regulations;
• Policies that govern intellectual property rights for AI-generated content;
• Safety criteria for using AI in high-risk areas like autonomous vehicles and healthcare;
• Accessibility directives for making AI technology available to individuals with disabilities. 

Legal Guardrails

Legal guardrails consist of laws passed by legislatures, regulations that are used to implement laws and formal standards that are used to assess compliance with laws and regulations. 

Legal guardrails have a strong influence on the creation of both policy-based guardrails and technical guardrails. Examples of legal guardrails for AI include:  

• Legislation that addresses liability for insured automated vehicles in the UK.
• An executive order that requires provisions for government use of generative AI in California.
• A proposed amendment to the EU Product Liability Directive will hold manufacturers liable for AI-related harm under certain conditions.
• A proposed bill to limit the use of biometric surveillance systems by federal, state, and local government entities in the United States.
• An executive order that requires the Big Tech developers to share information with the United States government before releasing algorithms to the public.

The dynamic nature of AI technology, as well as the technical complexity of black box AI, has led to differing opinions about who should be responsible for AI guardrails and how they should be implemented.

Reaching consensus for what types of AI guardrails to legislate is proving to be an even greater challenge that is going to require a willingness on the part of stakeholders to accommodate a wide range of viewpoints about safety, ethics, and governance.