Back in 2010, I did a post on I Write Like which reported the author most similar to a writing sample. I gave it several samples for which it gave me several different authors. The trend I noticed was the topic of the sample seemed to predict the result.
uses linguistic analytics to extract a spectrum of cognitive and social characteristics from the text data that a person generates through blogs, tweets, forum posts, and more.
It matches keywords in the writing to the Big 5 personality test and gives a summary based on it. So, it should be easy to skew too. I was able to find pieces of text from my blog that skewed the scores for all five measures. So, just like the other one, which samples I give it determines my “personality.” Something like FiveLabs’ Facebook Analyzer where it is looking at all or at least a huge sample of my writing probably would work better.
With a black box system a person working with it sees what goes in and what comes out. The machine’s decision making process is obfuscated. Theories are made based on incomplete evidence on the behavior. More data points on more situations confirming the behavior is my way of being more comfortable the theory is correct. Sometimes we lack the time or conscientiousness or even access to ensure the theory is correct. This leads to magical thinking like labeling the software in human-like terms, especially insane or stupid or seeking revenge.
With a white box system, a person working with it can see the machine’s logic used to make decisions. Theories can be made based on more complete evidence due to investigating the code to see what it is intended to do. The evidence is far more direct than testing more.
Systems today are so complex they tend to have many parts interacting with each other. Some will be of each type.
Then there are Application Programming Interfaces (APIs) which expose vendor supported methods to interact with a black box by disclosing how they works.
Proprietary systems tend towards a black box model from the perspective of clients. This black box philosophy depends on the experts, employees of the company, design the system so it works well and resolve the issues with it. So there is no need for clients to know what it is doing. Where the idea breaks down is clients who run the systems need to understand how it works to solve problems themselves. Sure the company helps. However, the client will want to achieve expertise to manage minor and moderate issues as much as possible. They want to involve the vendor as little as reasonably possible. Communities arise because peers have solved the client issues and getting an answer out of the vendor is either formulaic, inaccurate company line, or suspect. Peers become the best way to get answers.
Open source systems tend toward a white box model from the perspective of clients. This white box philosophy depends on clients to take initiative figuring out issues and solutions to resolve them. Clients become the experts who design the system so it works well. Where the idea breaks down is some clients just want something that works and not to have to solve the problems themselves. Sure the open source community helps. Companies have arisen to take the role of the vendor for proprietary systems to give CIOs “someone to yell at about the product”. Someone else is better to blame than myself.
Cases of both the black and the white box will be present in either model. That is actually okay. Anyone can manage both. Really it is about personal preference.
I prefer open source. But that is only because I love to research how things work, engage experts, and the feel of dopamine when I get close to solving an issue. My personality is geared towards it. My career is based around running web services in higher education. Running something is going to be my preference. (Bosses should take note that when I say not to run something, this means it is so bad I would risk being obsolete than run it.)
This post came about by discussing how to help our analysts better understand how to work with our systems. It is hard to figure out how to fix something when you cannot look at the problem, the data about the problem, or do anything to fix it. So a thought was to give our analysts more access to test systems so they get these experiences solving problems.