AI-enabled automation is often portrayed as a binary on-or-off: A process is either automated or not. But in practice, automation is a spectrum, and AI teams have to choose where on this spectrum to operate. It’s important to weigh the social impact of our work, and we must ameliorate automation’s impact on jobs. In addition to this important consideration, the best choice often depends on the application and what AI can and cannot do.
Take the problem of diagnosing medical patients from X-rays. The deployment options include:
- Human only: No AI involved.
- Shadow mode: A human doctor reads an X-ray and decides on a diagnosis, but an AI system shadows the doctor with its own attempt. The system’s output doesn’t create value for doctors or patients directly, but it is saved for analysis to help a machine learning team evaluate the AI’s performance before dialing it up to the next level of automation.
- AI assistance: A human doctor is responsible for the diagnosis, but the AI system may supply suggestions. For example, it can highlight areas of an X-ray for the doctor to focus on.
- Partial automation: An AI system looks at an X-ray image and, if it has high confidence in its decision, renders a diagnosis. In cases where it’s not confident, it asks a human to make the decision.
- Full automation: AI makes the diagnosis.
These options can apply to medical diagnosis, visual inspection, autonomous navigation, media content moderation, and many other tasks. In many cases, I’ve found that picking the right one is critical for a successful deployment, and that using either too much or too little automation can have a significant negative impact.
When you’re choosing a point along the automation spectrum, it’s worth considering what degree of automation is possible given the AI system’s accuracy, availability of humans to assist with the task, and desired rate of decision making (for example, human-in-the-loop options won’t work if you need to select an ad to place on a webpage within 100 milliseconds). Today’s algorithms are good enough only for certain points on the spectrum in a given application. As an AI team gains experience and collects data, it might gradually move to higher levels of automation within ethical and legal boundaries.
Some people say that we should focus on IA (intelligence augmentation) rather than AI — that AI should be used to help humans perform tasks rather than automate those tasks. I believe we should try to create value for society overall. Automation can transform and create jobs (as when taxi cabs created new opportunities for cab drivers) as well as destroy them. Even as we pick a point on this spectrum, let’s take others’ livelihoods into account and create value that is widely and fairly shared.