There are well-documented examples of AI systems making decisions that affect protected classes, such as housing assistance or unemployment benefits. AI can be used to screen resumes; banks apply AI models to grant individual consumers credit and set interest rates for them. Many small decisions, taken together, can have large effects, such as: AI-driven price discrimination could lead to certain groups in a society consistently paying more. But are there AI applications today that affect everyone, no matter their "class"? As I mentioned earlier, we are shifting our AI Ethics courses to more practical, useful techniques.

Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery. Existing data preparation and analysis systems rely on dictionary lookups and regular expression matching to detect semantic types. However, these matching-based approaches often are not robust to dirty data and only detect a limited number of types. We introduce Sherlock, a multi-input deep neural network for detecting semantic types. We train Sherlock on $686,765$ data columns retrieved from the VizNet corpus by matching $78$ semantic types from DBpedia to column headers. We characterize each matched column with $1,588$ features describing the statistical properties, character distributions, word embeddings, and paragraph vectors of column values. Sherlock achieves a support-weighted F$_1$ score of $0.89$, exceeding that of machine learning baselines, dictionary and regular expression benchmarks, and the consensus of crowdsourced annotations.

A critical phase of oil well completion involves positioning cement between the outer surface of a metal casing and the sides of the well. This task is done by injecting a specially formulated cement slurry down the center of the casing and up the sides of the bore hole. Designing these slurry systems is time consuming and expensive because of the variability of the conditions between wells and the variability of the raw materials and techniques used in geographically diverse locations. SlurryMinder is a design tool to aid field engineers in creating globally consistent cement slurry formulations and to rapidly disseminate current well-cementing techniques. We describe the implementation of this system and why AI technology was used; we also discuss corporate benefits of the system, both real and projected.