High-Stakes AI Decisions Need to Be Automatically Audited

Oren Etzioni and Michael Li in Wired: “…To achieve increased transparency, we advocate for auditable AI, an AI system that is queried externally with hypothetical cases. Those hypothetical cases can be either synthetic or real—allowing automated, instantaneous, fine-grained interrogation of the model. It’s a straightforward way to monitor AI systems for signs of bias or brittleness: What happens if we change the gender of a defendant? What happens if the loan applicants reside in a historically minority neighborhood?

Auditable AI has several advantages over explainable AI. Having a neutral third-party investigate these questions is a far better check on bias than explanations controlled by the algorithm’s creator. Second, this means the producers of the software do not have to expose trade secrets of their proprietary systems and data sets. Thus, AI audits will likely face less resistance.

Auditing is complementary to explanations. In fact, auditing can help to investigate and validate (or invalidate) AI explanations. Say Netflix recommends The Twilight Zone because I watched Stranger Things. Will it also recommend other science fiction horror shows? Does it recommend The Twilight Zone to everyone who’s watched Stranger Things?

Early examples of auditable AI are already having a positive impact. The ACLU recently revealed that Amazon’s auditable facial-recognition algorithms were nearly twice as likely to misidentify people of color. There is growing evidence that public audits can improve model accuracy for under-represented groups.

In the future, we can envision a robust ecosystem of auditing systems that provide insights into AI. We can even imagine “AI guardians” that build external models of AI systems based on audits. Instead of requiring AI systems to provide low-fidelity explanations, regulators can insist that AI systems used for high-stakes decisions provide auditing interfaces.

Auditable AI is not a panacea. If an AI system is performing a cancer diagnostic, the patient will still want an accurate and understandable explanation, not just an audit. Such explanations are the subject of ongoing research and will hopefully be ready for commercial use in the near future. But in the meantime, auditable AI can increase transparency and combat bias….(More)”.