Rachel Thomas wrote an article titled The problem with metrics is a big problem for AI.
« Metrics are typically just a proxy for what we really care about. »
« This an example of the common phenomenon of having to use proxies: You want to know what content users like, so you measure what they click on. You want to know which teachers are most effective, so you measure their students test scores. You want to know about crime, so you measure arrests. These things are not the same. Many things we do care about can not be measured. Metrics can be helpful, but we can’t forget that they are just proxies. »
« The state-owned media outlet Russia Today was an extreme outlier in how much YouTube’s algorithm had selected it to be recommended by a wide-variety of other YouTube channels. Such algorithmic selections, which begin autoplaying as soon as your current video is done, account for 70% of the time that users spend on YouTube. This chart strongly suggests that Russia Today has in some way gamed YouTube’s algorithm. (More evidence about issues with YouTube’s recommendation system is detailed here.) Platforms are rife with attempts to game their algorithms, to show up higher in search results or recommended content, through fake clicks, fake reviews, fake followers, and more. »
« It is much easier to measure short-term quantities: click through rates, month-over-month churn, quarterly earnings. Many long-term trends have a complex mix of factors and are tougher to quantify. »
« A recent Harvard Business Review article looked at Wells Fargo as a case study of how letting metrics replace strategy can harm a business. After identifying cross-selling as a measure of long-term customer relationships, Wells Fargo went overboard emphasizing the cross-selling metric: intense pressure on employees combined with an unethical sales culture led to 3.5 million fraudulent deposit and credit card accounts being opened without customers’ consent. The metric of cross-selling is a much more short-term concern compared to the loftier goal of nurturing long-term customer relationships. Overemphasizing metrics removes our focus from long-term concerns such as our values, trust and reputation, and our impact on society and the environment, and myopically focuses on the short-term. »
« All this is not to say that we should throw metrics out altogether. Data can be valuable in helping us understand the world, test hypotheses, and move beyond gut instincts or hunches. Metrics can be useful when they are in their proper context and place. One way to keep metrics in their place is to consider a slate of many metrics for a fuller picture… Even then, all this data should still be combined with listening to first-person experiences of those working at these companies. »
« Columbia professor and New York Times Chief Data Scientist Chris Wiggins wrote that quantitative measures should always be combined with qualitative information, “Since we can not know in advance every phenomenon users will experience, we can not know in advance what metrics will quantify these phenomena. To that end, data scientists and machine learning engineers must partner with or learn the skills of user experience research, giving users a voice.” »