Responsible AI (RAI) is the practice of designing, building, deploying and using artificial intelligence systems in ways that are safe, fair, transparent and accountable. It covers both the principles an organization commits to and the processes it uses to put them into effect, such as risk assessments, testing, documentation and human oversight.
There’s no single authoritative definition. Governments, standards bodies and companies each publish their own, though they overlap heavily. Most include some version of the same commitments: fairness and avoidance of harmful bias, transparency and explainability, privacy, safety and security, and accountability for outcomes.
Three reference points are cited most often. The OECD AI Principles, adopted in 2019 and updated in May 2024, were the first intergovernmental standard on AI. The NIST AI Risk Management Framework, published by the U.S. National Institute of Standards and Technology in January 2023, is a voluntary framework for managing AI risk. ISO/IEC 42001, published in 2023, is an international standard for AI management systems that organizations can be certified against.
See also: AI Governance Board (AIGB), Fairness, Accountability, and Transparency (FAT), Algorithmic Bias, (4) Principles of Explainable AI
Why it matters for marketing
Marketing uses AI in ways that touch customers directly. Audience targeting decides who sees an offer. Personalization decides what price or product someone is shown. Generative AI writes copy and produces images that go out under the brand’s name. Chatbots and AI agents speak to customers on the company’s behalf.
Each of those uses carries a responsible AI question. A targeting model trained on past purchase data may exclude a demographic group from seeing a credit or housing offer. A generative tool may produce a product claim that isn’t true. A chatbot may not disclose that it isn’t human. A team may feed customer personal data into a model without a lawful basis for doing so.
The consequences land on the brand. Customers and regulators hold the company responsible for what its AI does, whether or not the marketing team built the system. Privacy laws such as the GDPR and AI-specific rules such as the EU AI Act add legal obligations on top of the reputational ones.
The core principles
Frameworks use different wording for a similar set of ideas.
| Principle | What it means | Marketing example |
|---|---|---|
| Fairness | AI shouldn’t produce unjustified differences in outcomes across groups | Checking that an ad delivery model doesn’t exclude people by age or gender |
| Transparency | People should know when AI is in use and understand its role | Disclosing that a chat assistant is automated |
| Explainability | It should be possible to explain how a system reached an output | Being able to say why a lead received a low score |
| Privacy | Personal data should be collected and used lawfully and minimally | Not entering customer records into unapproved tools |
| Safety and reliability | Systems should perform as intended and fail safely | Testing a generative tool for false product claims before launch |
| Security | Systems should resist misuse and attack | Restricting who can change an AI agent’s instructions |
| Accountability | A named person or group is answerable for the system | Assigning an owner for each AI use case |
How the main frameworks compare
| Framework | Publisher | Type | Structure |
|---|---|---|---|
| OECD AI Principles | OECD | Intergovernmental recommendation | Five values-based principles and five policy recommendations |
| NIST AI Risk Management Framework (AI RMF 1.0) | NIST (U.S.) | Voluntary framework | Seven characteristics of trustworthy AI and four functions |
| ISO/IEC 42001 | ISO and IEC | Certifiable international standard | Requirements for an AI management system |
| EU AI Act | European Union | Binding regulation | Obligations tiered by risk level |
The OECD’s five values-based principles are inclusive growth, sustainable development and well-being; human rights and democratic values, including fairness and privacy; transparency and explainability; robustness, security and safety; and accountability. The OECD lists 47 adherents.
NIST describes trustworthy AI through seven characteristics: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair with harmful bias managed. Its four functions are Govern, Map, Measure and Manage. Govern is cross-cutting and applies throughout the other three.
These frameworks are designed to work together. NIST publishes crosswalks that map its framework to ISO/IEC 42001 and the OECD principles, and many organizations use the NIST framework as their risk process inside an ISO 42001 management system.
How to measure responsible AI
Responsible AI has no single score. Organizations track process measures and, where possible, outcome measures.
One simple process measure is review coverage.
Review coverage = (AI use cases with a completed risk review ÷ total AI use cases) × 100
An illustrative example: a marketing department has 15 AI use cases in operation, and 9 have gone through a documented risk review. Coverage is 9 ÷ 15 × 100 = 60%.
Other measures in common use:
- Bias testing results. Differences in model outcomes across demographic groups, checked before launch and periodically afterward.
- Incident counts. The number of reported cases of inaccurate, harmful or non-compliant AI output.
- Human review rate. The share of AI-generated customer-facing content checked by a person before release.
- Inventory completeness. Whether every AI system in use is recorded with an owner and a risk level.
- Training completion. The share of staff who’ve completed required AI training.
How to utilize responsible AI
Adopt a set of principles. Use the company’s existing principles if it has them. If it doesn’t, base them on an established framework such as the OECD principles or the NIST AI RMF.
Inventory AI use. List every AI tool and use case in the department, including AI features inside existing platforms.
Classify by risk. Sort use cases into tiers. An AI tool that suggests email subject lines is lower risk than one that makes pricing or eligibility decisions.
Review before launch. Run a documented assessment for higher-risk uses, covering data sources, bias, accuracy and disclosure.
Keep people in the process. Define where human review is required, especially for customer-facing content and decisions.
Set disclosure rules. Decide when customers are told that content or a conversation is AI-generated.
Assess vendors. Ask AI vendors how they test for bias, what data they train on and how customer data is handled.
Monitor after launch. Check outputs on a schedule, and give staff and customers a way to report problems.
Assign governance. Give oversight to an AI Governance Board or equivalent group with marketing represented.
Comparison with related concepts
| Term | Focus | How it relates to responsible AI |
|---|---|---|
| Responsible AI | Principles and practices for developing and using AI well | The subject of this entry |
| AI ethics | The moral questions AI raises | The philosophical basis. Responsible AI is the applied practice |
| Trustworthy AI | Properties an AI system should have | Often used interchangeably. NIST and the OECD prefer this term |
| AI governance | The structures, roles and policies that oversee AI | The mechanism through which responsible AI is enforced |
| AI safety | Preventing AI systems from causing harm, including from advanced systems | A narrower technical field. One part of responsible AI |
| Explainable AI | Making AI outputs understandable to people | One principle within responsible AI |
| AI compliance | Meeting legal and regulatory requirements | The legal minimum. Responsible AI usually goes further |
| Fairness, Accountability, and Transparency (FAT) | Three core principles for algorithmic systems | An earlier, narrower formulation of overlapping ideas |
“Responsible AI” and “trustworthy AI” describe the same territory from two sides. Trustworthy describes the system. Responsible describes the conduct of the people and organizations behind it.
Best practices
Start with an inventory. Principles can’t be applied to systems nobody has listed.
Scale effort to risk. A light review suits low-risk uses. Decisions that affect people’s access to offers, prices or services need more.
Write principles in operational terms. “We value fairness” doesn’t guide anyone. “Targeting models are tested for disparate outcomes before launch” does.
Give each use case a named owner. Accountability needs a person.
Test with real data and real edge cases. Include inputs the system is likely to handle badly.
Document decisions. Record what was reviewed, what was found and who approved it.
Train the whole team. People using AI tools need to recognize a responsible AI issue when they see one.
Review vendors as carefully as internal builds. Most marketing AI is bought, and the deploying company remains answerable for its use.
Avoid overstating it. Publishing principles without the processes behind them is sometimes called “ethics washing.”
Future trends
Regulation is making parts of it mandatory. The EU AI Act’s obligations are phasing in, with transparency rules for AI-generated content and chatbots applying from August 2026. What was voluntary practice is becoming a legal requirement in some jurisdictions.
Certification is growing. ISO/IEC 42001 gives organizations something to be audited against, and enterprise buyers have started asking AI vendors for it.
Agentic AI raises new questions. Systems that take actions, such as sending messages or spending budget, make accountability and oversight harder to define than they are for tools that only produce content.
Content provenance. Labeling and watermarking of AI-generated content is developing as both a technical practice and a regulatory expectation.
Generative AI guidance. NIST published a Generative AI Profile in July 2024 as a companion to its framework, and the OECD’s 2024 update addressed general-purpose and generative AI.
Environmental impact. The OECD’s updated principles now refer explicitly to environmental sustainability, and energy use is increasingly included in responsible AI discussions.
Frequently asked questions
What is responsible AI? It’s the practice of developing and using AI in ways that are safe, fair, transparent and accountable, supported by processes that put those principles into effect.
What are the principles of responsible AI? The list varies by framework. Most include fairness, transparency and explainability, privacy, safety and security, and accountability.
Is responsible AI a legal requirement? Not as a whole. Parts of it are required by law in some places, including the EU AI Act and privacy laws. Frameworks such as the NIST AI RMF are voluntary.
What’s the difference between responsible AI and AI ethics? AI ethics is the study of the moral questions AI raises. Responsible AI is the practical application: the policies, reviews and controls an organization uses.
What’s the difference between responsible AI and AI governance? Responsible AI is the goal and the set of principles. AI governance is the structure of roles, policies and oversight used to achieve it.
What is the NIST AI Risk Management Framework? A voluntary framework from the U.S. National Institute of Standards and Technology, released in January 2023. It describes seven characteristics of trustworthy AI and four functions: Govern, Map, Measure and Manage.
What is ISO/IEC 42001? An international standard that sets requirements for an AI management system. Organizations can be certified against it.
Who is responsible for responsible AI in a marketing team? Accountability usually sits with marketing leadership for the department’s use cases, working with legal, IT, data and privacy teams. Each AI use case should have a named owner.
Does responsible AI apply to tools bought from vendors? Yes. The organization deploying an AI tool is responsible for how it’s used, including tools it didn’t build.
How does a small team get started? List the AI tools in use, identify which ones affect customers or use personal data, write a short policy covering data and review, and assign an owner for each tool.
Related terms
- AI Governance Board (AIGB)
- Fairness, Accountability, and Transparency (FAT)
- Algorithmic Bias
- (4) Principles of Explainable AI
- Human-in-the-Loop (HITL)
- AI Development Lifecycle
- Environmental Implications of AI Usage
- General Data Protection Regulation (GDPR)
- AI Detection Software
- Filter Bubble
Sources
- OECD. “OECD AI Principles overview.” https://oecd.ai/principles/
- OECD. “AI principles.” https://oecd.org/en/topics/ai-principles
- American National Standards Institute. “OECD Updates AI Principles.” May 9, 2024.
Up to 3.8% invested Shop at Ansi.org and earn up to 3.8% of your purchase invested - National Institute of Standards and Technology. “AI Risk Management Framework.” https://www.nist.gov/itl/ai-risk-management-framework
- Wilson Sonsini. “NIST Releases Artificial Intelligence Risk Management Framework.” https://www.wsgrdataadvisor.com/?p=2761
- Fasken. “Artificial Intelligence Risk Management Framework Published by NIST.” February 2023. https://www.fasken.com/en/knowledge/2023/02/artificial-intelligence-risk-management-framework-published-by-nist
- arc42 Quality Model. “NIST AI RMF: Artificial Intelligence Risk Management Framework.” https://quality.arc42.org/standards/nist-ai-rmf