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Monetizing Bad Models: Pay Per Correct Prediction

In many ML applications, especially ones where you need to train a model on customer data to get high levels of accuracy, the only models that ML SaaS companies can offer to a client out-of-the-box are bad. But many ML SaaS businesses hesitate to go to a client with a

The Nonscience of Machine Learning

In 2013, Girshick et al. released a paper that described a technique to solve an impossible-sounding problem—classifying each pixel of an image (or semantic segmentation). The technique that they proposed, R-CNN, combines deep learning, selective search, and SVM. It also has all sorts of ad hoc choices, from the

Explore Exclusive Exploitation: The Billboard Top 100 Method of Learning

The optimism in Internet browsing is palpable. Browse long enough, and you will have a ‘hit.’ Like gambling, which it mimics, lay browsing is a losing proposition. A better way to spend your time is to focus on known knowns—excellent teachers, communicators, etc.—and core ideas, insights, and big

Fairly Certain: Using Uncertainty in Predictions to Diagnose Roots of Unfairness

One conventional definition of group fairness is that the ML algorithms produce predictions where the FPR (or FNR or both) is the same across groups. Fixating on equating FPR etc. can harm the very groups we are trying to help. So it may be useful to rethink how to solve

Optimal Data Collection When Strata and Strata Variances Are Known

With Ken Cor. What’s the least amount of data you need to collect to estimate the population mean with a particular standard error? For the simplest case—estimating the mean of a binomial variable using simple random sampling, a conservative estimate of the variance (p=.5), and a ±3

Equilibrium Fairness: How Fair Algorithms Can Hurt Those They Purport to Help

One definition of a fair algorithm is an algorithm that yields the same FPR across groups (an example of classification parity). To achieve that, we often have to trade in some accuracy. The final model is thus less accurate but fair. There are two concerns with such models: 1. Net

No Stopping: Impact of the Stopping Rule on the Sex Ratio

For social scientists brought up to worry about bias stemming from stopping data collection when results look significant, the fact that a gender based stopping rule has no impact on the sex ratio seems suspect. So let’s dig deeper. Let there be n families and let the stopping rule

ML (O)Ops: What Data To Collect? (part 3)

The first part of the series, “Improving and Deploying On-Device Models With Confidence,” is posted here. The second part, “Keeping Track of Changes,” is posted here. With Atul Dhingra For a broad class of machine learning problems, nitpicking over the neural net architecture is over (see, for instance, here). Instead,