To provide the AI team with the best tools for success, the principles of DEIA should be at the forefront of any technology project. Responsibility for ensuring responsible design decisions that result in equitable outcomes falls on all team members, from the practitioners to managers.

The importance of this can be illustrated by examining all of the different decisions within the AI development lifecycle. These decisions, especially onesthat might be considered purely “technical,” require thorough consideration including an assessment of their impact on any outcomes. The following two examples illustrate the importance of DEIA considerations within AI system design and development:

One suggested action to encourage deep consideration of key technical questions during AI system design is to implement iterative review mechanisms, particularly to monitor for bias, and be transparent regarding tradeoffs made in decision making regarding model performance and bias. This process begins with the assumption that there are biases baked into the model, and the review’s purpose is to uncover and reduce these model and historical biases.

These may seem like technical questions that a leader, program, or project manager may not normally focus on. However, successfully managing an AI project means establishing structures that ensure responsible and trustworthy AI practices are the responsibility of the entire team, and not just left to the day-to-day developers. As demonstrated, seemingly simple day-to-day design decisions that AI teams make have implications for marginalized communities. Contributors from the entire team, which can include designers, developers, data scientists, data engineers, machine learning engineers, product owners, and project and program managers must work together to inform these decisions.