IBM’s Francesca Rossi on AI Ethics: Insights for Engineers

As a computer scientist who has been immersed in AI ethics for about a decade, I’ve witnessed firsthand how the field has evolved. Today, a growing number of engineers find themselves developing AI solutions while navigating complex ethical considerations. Beyond technical expertise, responsible AI deployment requires a nuanced understanding of ethical implications.

In my role as IBM’s AI ethics global leader, I’ve observed a significant shift in how AI engineers must operate. They are no longer just talking to other AI engineers about how to build the technology. Now they need to engage with those who understand how their creations will affect the communities using these services. Several years ago at IBM, we recognized that AI engineers needed to incorporate additional steps into their development process, both technical and administrative. We created a playbook providing the right tools for testing issues like bias and privacy. But understanding how to use these tools properly is crucial. For instance, there are many different definitions of fairness in AI. Determining which definition applies requires consultation with the affected community, clients, and end users.

In her role at IBM, Francesca Rossi cochairs the company’s AI ethics board to help determine its core principles and internal processes. Francesca Rossi

Education plays a vital role in this process. When piloting our AI ethics playbook with AI engineering teams, one team believed their project was free from bias concerns because it didn’t include protected variables like race or gender. They didn’t realize that other features, such as zip code, could serve as proxies correlated to protected variables. Engineers sometimes believe that technological problems can be solved with technological solutions. While software tools are useful, they’re just the beginning. The greater challenge lies in learning to communicate and collaborate effectively with diverse stakeholders.

The pressure to rapidly release new AI products and tools may create tension with thorough ethical evaluation. This is why we established centralized AI ethics governance through an AI ethics board at IBM. Often, individual project teams face deadlines and quarterly results, making it difficult for them to fully consider broader impacts on reputation or client trust. Principles and internal processes should be centralized. Our clients—other companies—increasingly demand solutions that respect certain values. Additionally, regulations in some regions now mandate ethical considerations. Even major AI conferences require papers to discuss ethical implications of the research, pushing AI researchers to consider the impact of their work.

At IBM, we began by developing tools focused on key issues like privacy, explainability, fairness, and transparency. For each concern, we created an open-source tool kit with code guidelines and tutorials to help engineers implement them effectively. But as technology…