5 Responsible AI Principles Every Business Should Understand

The widespread adoption of artificial intelligence (AI) in the business world has come with new risks. Business leaders and IT departments are now facing a new set of concerns and challenges—from bias and hallucinations to social manipulation and data breaches—which they must learn to address.

If business leaders intend to reap the massive benefits of AI, then it is their responsibility to create an AI strategy that mitigates these risks to protect their employees, data, and brand. That is why the ethical deployment of AI systems and the conscientious use of AI are essential to companies trying to innovate quickly but also sustainably.

Enter responsible AI: creating and utilizing AI in a manner that is mindful, morally sound, and aligned with human values. Responsible AI goes beyond simply creating effective and compliant AI systems; it’s about ensuring these systems maximize fairness and reduce bias, promote safety and user agency, and align with human values and principles. 

Implementing a responsible AI practice is a strategic imperative to ensure the safety and effectiveness of this new technology within an organization. To help leaders proactively address AI’s risks and vulnerabilities, earn and foster user trust, and align their AI initiatives with broader organizational values and regulatory requirements, we’re sharing the five responsible AI principles that every business should adhere to.

A preface on Grammarly’s responsible AI principles

Every business should design its own responsible AI framework that centers on its users’ experience with the AI products approved for use at that company. The first objective of any responsible AI initiative should be to create ethical AI development principles that developers, data scientists, and vendors must follow for every AI product and user interaction. These responsible AI principles should align with your business’s core drivers and values. 

At Grammarly, our product is built around the goal of helping people work better, learn better, and connect better through improved communication. So when defining our guiding principles for responsible AI, we began with our commitment to safeguarding users’ thoughts and words. We then considered a range of industry guidelines and user feedback, consulting with experts to help us understand how people communicate and the language issues our users were potentially facing. This baseline assessment of industry standards and best practices helped us to determine the boundaries of our programs and establish the pillars of our responsible AI guiding principles. Since we’re in the business of words, we make sure to understand how words matter. 

Here are the five responsible AI principles that Grammarly uses as a North Star to guide everything we build: 

1. Transparency
2. Fairness
3. User agency
4. Accountability
5. Privacy and security

Transparency and explainability in AI usage and development are crucial for fostering trust among users, customers, and employees. According to Bloomberg Law, “transparency” refers to when companies are open about when people are interacting with AI, when content is AI-generated, or when a decision is made about the individual using AI. “Explainability” means that organizations should provide individuals with a plain-language explanation of the AI system’s logic and decision-making process so that they know how the AI generated the output or decision. 

When people understand how AI systems work and see the efforts to make them transparent, they are more likely to support and adopt these technologies. These are a few considerations to keep in mind when aiming to offer AI centered on transparency and explainability:

• User awareness: It should always be clear to users when they are interacting with AI. This includes being able to identify AI-generated content and distinguish it from human-generated content. In addition to knowing when an interaction is driven by AI, stakeholders should understand the AI system’s decision-making approach. When a system is transparent, users can better interpret the rationale behind its outputs and make appropriate decisions about how to apply them to their use cases, which is especially important in high-stakes areas like healthcare, finance, and law. 
• System development and limitations: Users should understand any risks associated with the model. This involves clearly identifying any conflicts of interest or business motivations to demonstrate whether the model’s output is objective and unbiased. Looking for AI vendors that build with this level of transparency can enhance public confidence in the technology. 
• Detailed documentation: Explainable AI, as well as detailed information articulating AI risks, is critical to achieving user awareness. For builders of AI tools, it’s essential to document the capabilities and limitations of the systems they create–similarly, organizations should offer the same level of visibility to their users, employees, and customers for the AI tools they deploy. 
• Data usage disclosures: Perhaps most essential, builders of AI (and the solutions that your company might procure) should disclose how user data is being used, stored, and protected. This is particularly important when AI uses personal data to make or influence decisions. 

AI systems should be designed to produce quality output and avoid bias, hallucination, or other unsafe outcomes. Organizations must make intentional efforts to identify and mitigate these biases to ensure consistent and equitable performance. By doing so, AI systems can better serve a wide range of users and avoid reinforcing existing prejudices or excluding certain groups from benefiting from the technology. 

Safety not only includes monitoring for content-based issues; it also involves ensuring proper deployment of AI within an organization and building guardrails to holistically defend against adverse impacts of using AI. Preventing these types of issues should be top of mind for businesses before releasing a product to its workforce. 

Here are a few things you should look for in an AI vendor to ensure fairness and safety in the solution before implementing it in your company: 

• Sensitivity guidelines: One way to build safety into a model is by defining guidelines that ensure the model stays aligned with human values. Make sure your AI vendor has a transparent set of sensitivity guidelines and a commitment to building AI products that are inclusive, safe, and free of bias by asking the right questions. 
• A risk assessment process: When launching new products involving AI, your AI vendor should assess all features for risks using a clear evaluation framework. This helps to prevent the feature from producing biased, offensive, or otherwise inappropriate content and evaluates for potential risks related to privacy, security, and other adverse impacts. 
• Tools that filter for harmful content: Investing in tools to detect harmful content is crucial for mitigating risks going forward, providing a positive user experience, and reducing the risks of brand reputation damage. Content should be reviewed both algorithmically and by humans to comprehensively detect offensive and sensitive language. 

Users should always be in control of their experience when interacting with AI. This is powerful technology, and when used responsibly, it should enhance a user’s skills while respecting personal autonomy and amplifying their intelligence, strengths, and impact. 

People are the ultimate decision-makers and experts in their own business contexts and with their intended audiences, and they should also understand the limitations of AI. They should be empowered to make an appropriate determination about whether the output of an AI system fits the context in which they are looking to apply it. 

An organization must decide whether AI or a given output is appropriate for their specific use case. For example, a team that is responsible for loan approvals may determine that they do not want to use AI to make the final call on who gets approved for a loan, given the potential risks of removing human review from that process. However, that same company may find AI to be impactful for improving internal communications, deploying code, or enhancing the customer service experience. 

These determinations may look different for every company, function, and user, which is why it’s critical that organizations build or deploy AI solutions that foster user agency, ensuring that the output can align with their organization’s own guidelines and policies.

Accountability does not mean zero fallibility. Rather, accountability is the commitment to a company’s core philosophies of ethical AI. It’s about more than just recognizing issues in a model. Developers need to anticipate potential abuse, assess its frequency, and pledge to take full ownership of and accountability for the model’s outcomes. This proactive approach helps ensure that AI aligns with human-centered values and positively impacts society.

Product and engineering teams should adhere to the following principles to embrace accountability and promote responsible and trustworthy AI usage: 

• Test for weak spots in the product: Perform offensive security techniques, bias and fairness evaluations, and other pressure tests to uncover vulnerabilities before they significantly impact customers.
• Identify industry-wide solutions: Locate solutions, such as open-source models, that make building responsible AI easier and more accessible. Developments in responsible approaches help us all improve the quality of our products and strengthen consumer trust in AI technology.
• Embed responsible AI teams across product development: This work can fall through the cracks if no one is explicitly responsible for ensuring models are safe. CISOs should prioritize hiring a responsible AI team and empower them to play a central role in building new features and maintaining existing ones.

Upholding accountability at all levels

Companies should establish clear lines of accountability for the outcomes of their AI systems. This includes mitigation and escalation procedures to handle any AI errors, misinformation, harm, or hallucinations. Systems should be tested to ensure that they are functioning correctly under a variety of conditions, including instances of user abuse/misuse, and should be continuously monitored, regularly reviewed, and systematically updated to ensure they remain fair, accurate, and reliable over time. Only then can a company claim to have a responsible approach toward the outputs and impact of its models. 

Our final, and perhaps most important, responsible AI principle is upholding privacy and security to protect all users, customers, and their companies’ reputations. In Grammarly’s 2024 State of Business Communication report, we found that over 60% of business leaders have concerns about protecting their employees’ and company’s security, privacy, personal data, and intellectual property.

When people interact with an AI model, they entrust it with some of their most sensitive personal or business information. It’s important that users understand how their data is being handled and whether it is being sold or used for advertising or training purposes.

• Training data development: AI developers must be given guidelines and training on how to make sure datasets are safe, fair, unbiased, and secure. Both human review and machine learning checks should be implemented to ensure the guidelines are being applied appropriately.  
• Working with user data: In order to uphold privacy, all teams interacting with models and training data should be thoroughly trained to ensure compliance with all legal, regulatory, and internal standards. All individuals working with user data must follow these strict protocols to ensure data is handled securely. Tight controls should be implemented to prevent private user data from being used in training data or being seen by employees working with models.
• Understanding data training: All users must be able to control whether their data is being used to train models and improve the product overall for everyone. No third parties should have access to user content to train their models.

Unlike other AI tools, Grammarly’s AI writing assistance is built specifically to optimize your communication. Our approach draws from our teams of expert linguists, deep knowledge of professional writing best practices, and over 15 years of experience in AI. With our vast expertise in developing best-in-class AI communication assistance, we always go to great lengths to ensure user data is private, safe, and secure. 

Our commitment to responsible and trustworthy AI is woven into the fabric of our development and deployment processes, ensuring that our AI not only enhances communication but also safeguards user data, promotes fairness, and maintains transparency. This approach permeates all aspects of our business, from how we implement third-party AI technologies to how we weave responsible AI reviews into every new feature we launch. We think critically about any in-house and third-party generative AI tools we use and are intentional in how our services are built, ensuring they are designed with the user in mind and in a way that supports their communication safely.

To learn more about Grammarly’s responsible AI principles, download The Responsible AI Advantage: Grammarly’s Guidelines to Ethical Innovation.