Superhuman: Can ML Beat Human-Level Performance in Supervised Models?

20 Dec

A supervised model cannot do better than its labels. (I revisit this point later.) So the trick is to make labels as good as you can. The errors in labels stem from three sources: 

  1. Lack of Effort: More effort people spend labeling something, presumably the more accurate it will be.
  2. Unclear Directions: Unclear directions can result from a. poorly written directions, b. conceptual issues, c. poor understanding. Let’s tackle conceptual issues first. Say you are labeling the topic of news articles. Say you come across an article about how Hillary Clinton’s hairstyle has evolved over the years. Should it be labeled as politics, or should it labeled as entertainment (or my preferred label: worthless)? It depends on taste and the use case. Whatever the decision, it needs to be codified (and clarified) in the directions given to labelers. Poor writing is generally a result of inadequate effort.  
  3. Hardness: Is that a 4 or a 7? We have all suffered at the hands of CAPTCHA to know that some tasks are harder than others.   

The fix for the first problem is obvious. To increase effort, incentivize. Incentivize by paying for correctness—measured over known-knowns—or by penalizing mistakes. And by providing feedback to people on the money they lost or how much more others with a better record made.

Solutions for unclear directions vary by the underlying problem. To address conceptual issues, incentivize people to flag (and comment on) cases where the directions are unclear and build a system to collect and review prediction errors. To figure out if the directions are unclear, quiz people on comprehension and archetypal cases. 

Can ML Performance Be Better Than Humans?

If humans label the dataset, can ML be better than humans? The first sentence of the article suggests not. Of course, we have yet to define what humans are doing. If the benchmark is labels provided by a poorly motivated and trained workforce and the model is trained on labels provided by motivated and trained people, ML can do better. The consensus label provided by a group of people will also generally be less noisy than one provided by a single person.    

Andrew Ng brings up another funny way ML can beat humans—by not learning from human labels very well. 

When training examples are labeled inconsistently, an A.I. that beats HLP on the test set might not actually perform better than humans in practice. Take speech recognition. If humans transcribing an audio clip were to label the same speech disfluency “um” (a U.S. version) 70 percent of the time and “erm” (a U.K. variation) 30 percent of the time, then HLP would be low. Two randomly chosen labelers would agree only 58 percent of the time (0.72 + 0.33). An A.I. model could gain a statistical advantage by picking “um” all of the time, which would be consistent with 70 percent of the time with the human-supplied label. Thus, the A.I. would beat HLP without being more accurate in a way that matters.

The scenario that Andrew draws out doesn’t seem very plausible. But the broader point about thinking hard about cases which humans are not able to label consistently is an important one and worth building systems around.

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