What Is A Chair?

I’m currently reading a book called “Sorting Things Out” about how categorization and standards shape our world. I highly recommend it. One of the early examples in the book made me think about the way we code algorithms to categorize different things, specifically the difficulties in categorizing people and the consequences such categorizations have on them.

Imagine you have to write a program that recognizes whether an object is a chair (ignore the complexities of computer vision and such, just stay with me for a bit). You could code a simple set of binary checks like does the object have four legs? Does it have a flat surface at the end of those four legs? If it meets all your criteria, you could say the object was indeed a chair. But what a bench? It meets the criteria we’ve set up, but it’s technically not a chair, it’s a bench. Is a bench a subset of the chair category?  What about a tree stump in the woods? That is most certainly not a chair, but you can definitely sit on it. The tree stump then calls into question the whole purpose of making the categorization in the first place. Are you trying to sort items in a warehouse or are you just trying to find a place to sit down? The dilemma lies in whether you create a strict set of criteria that could exclude some items or you leave your rules lax and risk polluting your chair population with items such as tables.

Making the distinction between a chair and not a chair is very easy for humans to do, but it’s very difficult for software, especially if the purpose of the chair question is to determine whether you can comfortably sit down or not. Mistaking a table for a chair is benign enough, but algorithms often deal with people where mistakes can have life-altering consequences. Increasingly common are algorithms which decide whether a person gets approved for a loan, or a person’s prison sentence, their legal status as an immigrant, or whether they are a good match for a particular position at a company. Inevitably there will be people stuck in that fuzzy area. What happens then? Any person that deals with data about people needs to ask themselves what happens when their algorithm fails to make a correct determination.

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