Software Engineer Job Summary: Roles and Responsibilities

An Amazon algorithm for evaluating software engineer job applicants ended up discriminating against women. The algorithm could not assess job skills so, instead, it looked for keywords in résumés and—since there were few women in Amazon’s technical-job résumé database—the algorithm assumed that applicants who went to women’s colleges or participated in female activities such as women’s tennis or women’s singing groups were not good software engineers.

The more general point is that computer algorithms will have a devil of a time predicting which jobs are most at risk for being replaced by computers, since they have no comprehension of the skills required to do a particular job successfully.

ASSISTING HUMANS IS EASIER THAN REPLACING THEM

In one study that was widely covered (including by The Washington Post, The Economist, Ars Technica, and The Verge), Oxford University researchers used the U.S. Department of Labor’s O*NET database, which assesses the importance of various skill competencies for hundreds of occupations. For example, using a scale of 0 to 100, O*NET gauges finger dexterity to be more important for dentists (81) than for locksmiths (72) or barbers (60).

The Oxford researchers then coded each of 70 occupations as either automatable or not and correlated these yes/no assessments with O*NET’s scores for nine skill categories. Using these statistical correlations, the researchers then estimated the probability of computerization for 702 occupations.

There are two glaring problems with this study. First, the Oxford group’s yes/no labeling of an occupation as being automatable is far too simplistic. For many (most?) occupations, computers can be invaluable assistants, but cannot replace humans fully. Lawyers can use computers to search for case precedents, but cannot rely on computers to make persuasive arguments. Meteorologists can use statistical programs to make weather forecasts, but cannot rely on computers to specify the variables that should be used in such models. Writers can use word processing programs to format their work and avoid spelling mistakes, but cannot rely on computers to write compelling novels.

Second, if the O*NET assessments in nine skill categories were sufficient, it would be relatively easy to predict the best job for every person and to predict how well any person would do in any job. It is not. Anyone who has ever worked for someone, worked with someone, or had someone work for them (did we leave anyone out?) understands how difficult it is to know in advance whether someone will be a good boss, coworker, or employee.

Some important skills are difficult to measure; others may be overlooked. For example, a robot with excellent finger dexterity won’t be a good dentist if its image-recognition software is bad at recognizing cavities. Radiology AI’s struggles are not comforting. Similarly, you may be in for a surprise if you trust a robot to cut your hair simply because it can open and close scissors.