Extending PrivacyCode to Address Responsible AI
By Eric Lybeck, Director of Privacy Engineering
AI is now ubiquitous and has already changed the way we live and work. We buy an increased amount of goods and services online, sometimes not even realizing AI is providing us recommendations. Social networks use advanced algorithms to keep us engaged. In the field of medicine, AI offers incredible progress in the early detection and treatment of disease.
As with any new technology, it’s important for organizations to prioritize their investments, address threats and risks posed by AI, and measure results in an effective manner.
PrivacyCode, working with input from our design partners and leveraging our AI/ML engine, created our Privacy Object Library that enables any organization to manage Responsible AI challenges by connecting them to business goals.
Link Responsible AI to Business Goals
The first step in building a Responsible AI program is identifying your desired outcomes. Whether the outcome is to improve customer retention, increase efficiency, or accelerate innovation, as long as you know the outcomes, you can start to track the AI initiatives and their impact.
For example, the goal may be to accelerate innovation in a specific market or vertical, so projects that involve incorporating AI-powered or defined capabilities in your products, may align to this corporate goal. Tracking, measuring, and proving that impact is what PrivacyCode.ai was built for.
Use a Common Enterprise-wide Framework
Despite popular headlines, AI is not an unregulated “Wild West.” Existing regulations already govern AI’s use cases and derivatives. Consider that all internal corporate policies apply as well, so you need to keep in mind all of these cross-disciplinary requirements related to security, privacy, ethics, and non-discrimination, to name a few.
The commonly-cited uncertainty of AI regulation often comes from new or emerging laws and frameworks that either add to or intersect with these existing requirements. For example, there are new frameworks, such as the NIST AI Risk Management Framework and proposed new laws such as the EU AI Act. This makes it increasingly important, yet difficult for organizations to stay up-to-date on the latest developments. PrivacyCode.ai was built for this too.
We use AI Machine Learning technology to quickly update our Privacy Objects library with new and emerging frameworks and requirements. Then we distill them into repeatable, reusable tasks that business teams can own and implement. Our Responsible AI library, Ethical and Responsible AI Essentials, provides the foundation of an enterprise-wide framework.
Design, Build, and Maintain Responsible AI Systems
Our customers use PrivacyCode.ai to manage Responsible AI projects, and solve problems such as validating the AI training dataset compliance, communicating how AI systems work, and proving fair and non-discriminatory results.
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If you are interested in more information about how you can improve your outcomes with Responsible AI, you can contact our team here.
