The ability of artificial intelligence to perform important business tasks has grown by leaps and bounds in recent years. As AI has progressed from a proof of concept to powering critical enterprise workflows, it has become increasingly apparent that this general-purpose technology must be assessed in precise context for Privacy, robustness, fairness, and explainability. These four assessments, along with transparency to stakeholders, constitute the five pillars of trustworthiness. If issues are discovered, they must be mitigated before serious harms occur. What are these pillars, how are they assessed, and how are they mitigated?
Let’s try to understand the pillars of trustworthy AI using a home mortgage approval application as an example.
Privacy is the idea that personal sensitive information should neither be disclosed inadvertently nor when a system is breached by a malicious actor. Data privacy has been studied and regulated for some time, but there are some nuances with AI in the mix. A historical dataset of home mortgage decisions might be protected against the disclosure of sensitive information such as the income of applicants. But using it to train an AI system may open up the sensitive information to inference by a user intelligently querying the AI.
Robustness is the ability of an AI system to remain accurate in different settings and conditions, including naturally occurring conditions and those set up by malicious actors to fool the AI. A robust AI mortgage model will not completely fall apart at the outset of a major change in the world, such as a global pandemic.
Fairness ensures that an AI system does not yield systematic advantages to certain privileged groups and individuals (defined by characteristics such as gender and national origin) and systematic disadvantages to certain unprivileged groups and individuals. The mortgage approval model should not systematically favor any race, ethnicity or gender.
Explainability allows people to understand how (typically opaque) AI systems make their decisions. Loan officers, applicants, and regulators can all make sense of an explainable AI system, each toward their own goals.
Transparency is achieved when the various assessments along with their justifications are documented and presented to stakeholders. Factsheets containing assessments of accuracy, privacy, robustness, fairness, and explainability of the mortgage approval model may be generated for model risk managers, regulators, and the general public.
Now that you know the pillars of trustworthy AI, how, when, and why do you assess them? Let’s start with the “why” using an analogy of inspecting the safety and functionality of a house along many dimensions (electrical, structural, plumbing, etc.). There are many reasons to inspect a house. The government inspects a house before issuing a certificate of occupancy. An owner inspects a house for peace of mind and to identify areas of improvement.
