An Introduction to Responsible AI for the Enterprise

When AI first started to gain widespread adoption, it sparked a wave of fear. While much of that fear was overblown, we still need to remain cautious about any new technological innovation. Given AI’s potential to drive change on a massive scale, applying ethical principles to AI is not just important, but urgent.

Every company must prioritize responsible AI—not only as an ethical responsibility but as a practical, strategic choice. Regulators have stepped in, introducing new laws and frameworks that could lead to potential fines. And AI errors could lead to PR issues, which can also affect the bottom line. 

So that brings up the question: What is responsible AI? How do you make sure you’re taking the right steps to protect your data, your intellectual property, and your customers?

Responsible AI refers to the use of frameworks and guidelines to reduce or eliminate potential risks associated with artificial intelligence. It’s a broad approach that governs everything from reducing bias to model training to deploying models on use cases that minimize potential risk. In many ways, it’s both a philosophy and a practical approach that emphasizes minimizing potential AI harms and promoting transparency, fairness, and accountability among those who use and create AI models. 

The need for ethical AI doesn’t just apply to large, foundational models like OpenAI’s ChatGPT or Anthropic’s Claude; we all play a role. Every enterprise using AI needs their own use policies to make sure they don’t run afoul of regulators or introduce unnecessary risks. For example, the EU’s AI Act governs where you can deploy AI and where you can’t, with high risk systems having strict rules. For example, you cannot use AI to make hiring decisions under the AI Act, as this could lead to unintentional bias or discrimination.

Artificial intelligence is directly intertwined with data. Whether using a wide range of data to build an LLM model or leveraging an existing model to process information as part of a wider enterprise process, AI depends on data inputs and shapes data outputs. So, it’s critical to protect that data. Taking a private AI approach, where data remains protected and only seen by the models and people who should have access, is critical to responsible AI usage. 

For example, imagine you’re training an AI copilot on sensitive business documents or knowledge. Most AI copilots will answer nearly any question to be helpful, but some answers should remain restricted. For instance, a finance team member might need access to budget information, but details on individual salaries should remain restricted. Implementing strict user permissions around data access helps protect sensitive information. Equally important is ensuring robust security and compliance practices for AI systems to maintain data integrity and confidentiality. For instance, enterprise AI vendors who pass FedRAMP have taken serious steps to ensure compliance and strong security.

Concerns about AI bias drove the passage of a lot of new AI regulations. AI bias refers to the entrenchment of systemic inequalities in decision-making by using AI. Bias can stem from multiple sources, such as the data used to train a model, assumptions embedded within algorithms, or the way information is interpreted. 

Reducing potential biases requires a few steps. First, when training any model internally, ensure you have strong data that’s cleansed of any potential signifiers that could lead to systemic bias. For instance, before training a model, make sure to remove any potential identifiers such as protected classes like race, gender, or age. Also, make sure any models you train use diverse, representative datasets. 

Second, periodically audit your dataset and the results of decisions to ensure that bias doesn’t creep into your dataset. If you’re building the model yourself, you’ll want to regularly assess the data to see if you have to reweight or remove any factors, and you’ll want to continuously monitor the model for fairness. By implementing these audits, you can identify and address biases early, making it easier to retain algorithms for ethical and fair AI practices. 

Being responsible with AI means not only reducing elements like bias, but also being strategic about where you deploy AI within the enterprise. In fact, this was a driving force behind the EU’s AI Act, as certain use cases are prohibited, while others are heavily regulated. For example, candidate hiring is considered “high risk,” which means it’s subject to strict regulations and practices, including transparency, human oversight, and the use of a risk management system. 

The key here is to make sure you deploy AI only in areas where it can provide productive value without risking harm to users. For example, using AI to make housing loan application decisions could potentially lead to people being falsely denied mortgages. But, this doesn’t mean AI doesn’t have a place in limited circumstances—for example, ingesting mortgage loan documents and extracting key data to enter into a database or data fabric so you save time processing information for humans to review. You can also use AI models to remove personally identifiable information from specific documents, which can help demonstrate a commitment to remaining bias-free. 

In short, maintain a strong strategy around where you use AI. Choosing the right use cases will make a world of difference in ensuring you use it responsibly.

Often, how AI makes decisions can be murky. While this isn’t a major issue for consumer-based AI models or those used personally, when it comes to business uses, transparency into model decisions is paramount. Transparency enables administrators and even end users to ask questions and understand how an AI arrived at a decision. The processes must be understandable and accessible, and explain the data they use and the rationale behind their choices. 

For example, consider a situation where an automotive insurance company deploys a conversational AI for customer service that allows employees to ask questions on enterprise knowledge, such as internal policies or compliance regulations around specific states. While AI can give the answers, it should also provide a direct link to the source for the answer. This helps employees confirm accuracy, prevent bias and “AI hallucination,” and, ultimately, build trust in AI processes. 

With all the potential risks surrounding AI, it’s critical to take responsible steps to safeguard data and avoid taking action on poor decisions made by AI. While this kind of responsible AI use does make the world a better, more equitable, and safer place, it’s also practical for businesses to pursue. Being responsible with AI engenders trust in your customer base, retains and improves your organizational representation, and also helps you avoid compliance fines. So make sure to keep the four elements we discussed today in mind—they’re just good business.