The Six Principles of Responsible AI
- Fairness
- Reliability and Safey
- Privacy and Security
- Inclusiveness
- Transparency
- Accountability
Responsible AI grounds system design within a framework of principles that define desired behavior and outcomes. These principles can align to a moral code of ethics, provide business risk mitigation, or a combination of the two. While the framework may consistent across organizations, each organization may have unique definitions for each principle. For example, RAI promotes fairness, but it does not specify which definition of fairness should apply.
Building digital systems that align with our values has been in practice since the beginning of software development. One might ask, “why do we need an explicit definition of responsibility for AI-based solutions?” Traditional software follows human developed rules within a constrained space. AI systems create complex patterns from data that are not always easy to interpret. AI systems also have the capacity to interact across a vast array of inputs and domains, making it difficult to test every possible scenario.
The practice of responsible AI defines guidelines to constrain AI to its intended purpose. These constraints are often referred to as guardrails. Guardrails on the highway keep wayward vehicles from running far off the road. But there are various ways that cars may avoid the protection guardrails offer. Sometimes on purpose, but often unintentionally. Even well-designed AI guardrails may not prevent every possible harmful outcome. We will need to decide what systems can be guided and which require hard and fast rules that cannot be broken.
If you want to know more, the latest episode of Talk To Me Petey D deep dives into Responsible AI.
