KDD2019 Social Impact Workshop にて一旦利用が停止したMLシステムを再稼動させるために、どのように住民とコミュニケーションを取ったかが発表された。
Communicating Machine Learning Results About the Flint Water Crisis to City Residents at Scale.
Jared Webb (Univ of Michigan); Eric Schwartz (University of Michigan); Jacob Abernethy (Georgia Tech); Stacy Woods (NRDC)
We developed a combined active and machine learning approach to produce a probability that each home in Flint, Michigan has lead pipes to help the city minimize recovery costs. Over the past several years, our work has all been “backend,” dealing with legal teams, the city council, and the recovery team. Now, we are developing a public facing website to communicate information and predictions to the citizenry. Our main outreach tool is an interactive map that a resident can use to observe the replacement efforts and our up-to-date predictions.
There are reasons for the slowdown. AECOM discarded the machine-learning model’s predictions, which had guided excavations. And facing political pressure from some residents, Weaver demanded that the firm dig across the city’s wards and in every house on selected blocks, rather than picking out the homes likely to have lead because of age, property type, or other characteristics that could be correlated with the pipes.