Part three of a three-part blog series on responsible AI focuses on “how.” Read part one to explore the “why” and part two to discover the “who.

In the rush to capitalize on AI's potential, many enterprises are charging ahead at full throttle. But how do you implement AI swiftly and responsibly?

To achieve this balance, it's crucial to understand what we're dealing with. While AI can be used to tackle many complex enterprise tasks, at its core, it’s sophisticated software with unique capabilities and challenges. Like any critical technology, how you implement and use AI matters greatly.

Responsible AI involves aligning with the principles of fairness, security, privacy, accountability, inclusivity, sustainability and transparency. This approach helps you manage risk in the face of innovation. Pay careful attention to everything that goes into AI models (the inputs) and everything that comes out (the outputs) and take a proactive approach with every step in between. Knowing how to implement responsible AI can boost your confidence and reduce problems.

Build a solid foundation

You probably already have security and privacy policies in place. But AI is a relatively new enterprise tool, so it’s understandable if you aren’t sure where to start implementing responsible AI practices. You can integrate responsible AI practices into your existing compliance framework to ensure alignment with any additional required government regulations.

Referencing AI use in your ethics guidelines is a fantastic start, something more organizations are doing. Stipulating responsible AI measures can lower your vulnerability to security and privacy breaches, exposure of proprietary information and other detrimental issues for the business. But go one step further and ensure that your vendors and partners follow responsible AI principles. This will demonstrate your commitment to the concept and act as an additional safeguard for any data accessed by your vendors and partners.

Consider following the example of numerous other organizations and letting customers know your ethics principles include a promise of responsible AI. This can cement the trust of customers, partners and prospects and reassure them that you will protect their data and promise to use it only for ethical purposes. And some governments may require such transparency in the form of specific regulations.

Ensure the human element is part of the process

Human-AI collaboration is a powerful ingredient of responsible AI and the key to charting a course from tactical to transformative with AI. As the architects of AI technology, humans possess the intuition and self-awareness that AI lacks. Human involvement is critical during planning, implementation, and operation, especially when AI output assists in making critical decisions. Humans oversee final decision-making, bias detection, explainability, observability and other responsible AI components in the output.

Approximately 43% of business and technology leaders said “regular human review of AI models and results” was prioritized as an approach to manage ethical concerns, according to Unisys’ “From Barriers to Breakthroughs: Unlocking Growth Opportunities with Cloud-Enabled Innovation” research report.

Promote user adoption

Ensuring your entire company understands what responsible AI is and why it matters is crucial. Just as organizational change management can support user adoption of AI tools, it can also encourage the adoption of technology use guidelines like responsible AI.

Include training on responsible AI practices as part of your change management efforts for AI. This can increase awareness and acceptance among employees. The vast majority of employees don’t intend to do harm when they use AI. However, unintentional mistakes can happen, whether that’s copyright infringement, intellectual property sharing or the consequences of using the “wrong” data.

Take action to encourage responsible AI

Implementing responsible AI takes a solid foundation of guidelines, practices and policies. People are integral to this process, and establishing appropriate human oversight is a major value-add. You also rely on people to embrace responsible AI practices; organizational change management efforts make that smoother.

For details on what organizations are experiencing with AI, download the “Operationalizing Generative AI for Better Business Outcomes” report and reach out if you’re ready to explore AI solutions from Unisys.