AI Guardrail 

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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:

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:

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:

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:  

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. 


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Margaret Rouse

Margaret jest nagradzaną technical writerką, nauczycielką i wykładowczynią. Jest znana z tego, że potrafi w prostych słowach pzybliżyć złożone pojęcia techniczne słuchaczom ze świata biznesu. Od dwudziestu lat jej definicje pojęć z dziedziny IT są publikowane przez Que w encyklopedii terminów technologicznych, a także cytowane w artykułach ukazujących się w New York Times, w magazynie Time, USA Today, ZDNet, a także w magazynach PC i Discovery. Margaret dołączyła do zespołu Techopedii w roku 2011. Margaret lubi pomagać znaleźć wspólny język specjalistom ze świata biznesu i IT. W swojej pracy, jak sama mówi, buduje mosty między tymi dwiema domenami, w ten…