Clive Thompson a very prominent technical author came to Rochester on November fifteenth to talk about the “Problems of Efficient Coding”. Going into this talk, I expected it to go along the lines of how making super “efficient” code often results in code that nobody understands and is hard to maintain. To my pleasant surprise, Clive Thompson provided a nuanced discussion around the cultural problems created when we try to optimize every problem using technology.
To understand Clive’s point, he used Facebook as a prime example of this problem. Before the Facebook feed system, the web largely acted like a blog where people had to actively reach out to everyone’s page to get content. Right after Facebook implemented the feed system there was a big debacle where nearly 20% of Facebook users entered a Facebook group opposed the new feed system. For nearly a week there were student protesters outside of the Facebook office. People initially found the feed system creepy because it gave everyone ambient awareness of everything happening in their network; this in some regards decreased “anonymity”. You no longer had to go out to every one’s page, Facebook created a tailored newspaper for you to consume. As a result of the new feed system, people started producing a lot more content to put on social media sites since people consumed it immediately. To filter content and only provide people with “important” posts, Facebook employed machine learning algorithms which favored posts that get more clicks. It turns out that people are very likely to click on things that are highly emotional or controversial–machine learning algorithms were quick to learn this and favor controversial content. People started to play the algorithm and turn Facebook into a hot take tire fire as it get littered with absurd conspiracy theories like #Pizzagate. Facebook’s motto used to be “move fast and break things”, however, after Zuckerburg was lambasted in front of congress, that motto is slowly changing.
Facebook like many tech companies credits its major success to optimizing a sometimes niche problem – this is something that programmers love to do and computers are perfect at. Facebook optimized how people consume media, but they did it to the detriment of quality content. Youtube tremendously optimized how we view videos by suggesting us recommended videos to watch, but, it often suggests repulsive content. Uber optimized how people found rides, but it resulted in an influx of part time drivers that are slowly pushing out full time drivers. This is not to say that optimization is a bad thing. As a result of optimizing tasks we can save a tremendous amount of time and be more productive members of society. Thompson suggests that there are certain cases where we should slow down and add friction to cases that we initially see the need to optimize. Reflection and deliberation are important things that are often thrown to the wind when we optimize things.
This now begs the question: how do we do we solve these issues? This is something that Thompson didn’t discuss in depth nor had a great answer for. We could point our fingers at governments, companies, or consumers and tell them to solve the problem. Surely having the government enact some well-constructed public policy based on the current policy environment would solve the issues… right? The problem in the age of big data is that things are changing at a rapid pace and by the time we realize the dangers of a particular issue, it may have already caused grave damages or morphed into another form. Look at gambling for example, we have had decades of laws and regulations surrounding underage gambling, however, online gambling issues have been consistently creeping their way into policy discussion over the last five years. It is fascinating that most public policy generated in the technology field is actually created in the court systems. This is good in the sense that the court system is often faster than passing a new law, but, it is also very problematic. Old laws when used to interpret a nuanced technological problem often yield outcomes that the original authors of the law would possibly disagree with.
Although Thompson’s talk raises more questions and problems than tangible easy-to-implement solutions, we must start having discussions like this so we can enact a cultural change around how we approach optimization tasks. Adding back careful reflection and deliberation back to currently optimized tasks on the internet could give us more freedom over how we consume content and interact with the world.