I think a humane approach to using machine learning to solve business problems is to start with the people already working on the problem. Focus on front line staff asking what they feel are their most most valuable and meaningful contributions, what gets in their way, then build tools to help support them in these tasks. This could be called Human at the Heart - a play on the existing term Human in the Loop,
Human in the Loop describes an approach to applying machine learning algorithms that considers how human input should be included in the algorithm. This can apply in applications like self driving cars where how a person can influence and override the algorithm is a natural part part of the problem, but I am referring to business more generally. As an example, consider content moderation at an image hosting company. A standard approach would be to use a large number of previous examples to train an algorithm to detect certain classes of images. Once the accuracy is sufficient the algorithm would be phased in to replace some manual process, maybe at first with human verification of the results but eventually to replace staff. A Human in the Loop approach might be built to learn from the person checking images, and that person may be asked to describe their decisions in a way that makes sense to the algorithm. In both cases however the goal is to eventually replace most or all people from the process.
A Human at the Heart approach would start by asking the people involved in moderation when they feel most productive. They may say the most valuable work they do is where they need to understand the context and community where a particular image was posted, and focus on building tools to provide that information.
The problem with this idea is that it wouldn’t sell. The types of problems where use of machine learning is considered are generally ones that are well suited to automation. Technology is getting to the point where it can help individuals in complex interactions at scale, but business drivers tends toward reducing cost and headcount. To make this work it would be necessary to focus less on standardisation and improving existing business models and more on incentive design and defining acceptable behaviour of both people and algorithms. I believe that organisations that get this right could benefit greatly from the creativity of people, where I think they will exceed computers for a long time to come.
So what do you think so far? Any suggested reading material, who is thinking along these lines already? Any risks or pitfalls? One I see already from the example above many would prefer imperfect computer moderation to having individuals policing their content.