HBR published an opinion post by Andrew Burt, called “The AI Transparency Paradox.” This post talks about the problems that were created by tools that open up the “black box” of a machine learning model.
“Black box” refers to the situation where one can’t explain why a machine learning model predicted whatever it predicted. Predictability is not only important when one wants to improve the model or to pinpoint mistakes, but it is also an essential feature in many fields. For example, when I was developing a cancer detection model, every physician requested to know why we thought a particular patient had cancer. That is why I’m happy, so many people develop tools that allow peeking into the black box.
I was very surprised to read the “transparency paradox” post. Not because I couldn’t imagine that people will use the insights to hack the models. I was surprised because the post reads like a case for security through obscurity — an ancient practice that was mostly eradicated from the mainstream.
Yes, ML transparency opens opportunities for hacking and abuse. However, this is EXACTLY the reason why such openness is needed. Hacking attempts will not disappear with transparency removal; they will be harder to defend.
In my last post on data science career, I heavily promoted the idea that a data scientist needs to find his or her specialization. I back my opinion with my experience and by citing other people opinions. However, keep in mind that I am not a career advisor, I never surveyed the job market, and I might not know what I’m talking about. Moreover, despite the fact that I advocate for specialization, I think that I am more of a generalist.
If you had a chance to talk to me about data visualization, you know that I dislike the use of double Y-axis for anything except for presenting different units of the same measurement (for example inches and meters). Of course, I’m far from being a special case. Double axis ban is a standard stand among all the people in the field of data visualization education. Nevertheless, double-scale axes (mostly Y-axis) are commonly used both in popular and technical publications. One of my data visualization students in the Azrieli College of Engineering of Jerusalem told me that he continually uses double Y scales when he designs dashboards that are displayed on a tiny screen in a piece of sophisticated hardware. He claimed that it was impossible to split the data into separate graphs, due to space constraints, and that the engineers that consume those charts are professional enough to overcome the shortcomings of the double scales. I couldn’t find any counter-argument.
When I tried to clarify my position on that student’s problem, I found an interesting article by Financial Times commentator John Auther, called “Lies, Damned Lies and Statistics.” In this article, John Auther reviews the many problems a double scale can create. He also shows different alternatives (such as normalization). However, at the end of that article, John Auther also provides strong and valid arguments in favor of the moderate use of double scales. John Auther notices strange dynamics of two metrics
It is extraordinary that two measures with almost nothing in common with each other should move this closely together. In early 2007 I noticed how they were moving together, and ended up writing an entire book attempting to explain how this happened.
It is relatively easy to modify chart scales so that “two measures with almost nothing in common […] move […] closely together”. However, it is hard to argue with the fact that it was the double scale chart that triggered that spark in the commentator’s head. He acknowledges that normalizing (or rebasing, as he puts it) would have resulted in a similar picture
However, there is one problem with rebasing, which is that it does not take account of the fact that a stock market is naturally more variable than a foreign exchange market. Eye-balling this chart quickly, the main message might be that the S&P was falling faster than the euro against the yen. The more important point was that the two were as correlated as ever. Both stock markets and foreign exchange carry trades were suffering grievous losses, and they were moving together — even if the S&P was moving a little faster.
I am not a financial expert, so I can’t find an easy alternative that will provide the insight John Auther is looking for while satisfying my purist desire to refrain from using double Y axes. The real question, however, is whether such an alternative is something one should be looking for. In many fields, double scales are the accepted language. Thanks to the standard language, many domain experts are able to exchange ideas and discover novel insights. Reinventing the language might bring more harm than good. Thus, my current recommendations regarding double scales are:
Avoid double scales when possible, unless its a commonly accepted practice. In which case, be persistent and don’t lie.
In his blog post Big Data Or Big Hype? , Rick Ciesla is asking a question whether the “Big Data” phenomenon is “a real thing” or just a hype? I must admit that, until recently, I was sure that the term “Data Science” was a hype too — an overbroad term to describe various engineering and scientific activities. As time passes by, I become more and more confident that Data Science matures into a separate profession. I haven’t’ yet decided whether the word “science” is fully appropriate in this case is.
We have certainly heard a lot about Big Data in recent years, especially with regards to data science and machine learning. Just how large of a data set constitutes Big Data? What amount of data science and machine learning work involves truly stratospheric volumes of bits and bytes? There’s a survey for that, courtesy of […]
The first section in the original post is called “You’ll learn new things”. This is a universal truth. If you don’t “learn new things” every day, your professional career is stalling. Taken from the word of classification models, telling a universal truth has a very high sensitivity but very low specificity. In other words, it’s a useless waste of ink.
3. Not for developers only
The first section starts as follows: “When transitioning from a role as a developer to a position focused on data, …”. Most of the data scientists I know were never developers. I, for example, started as a pharmacist, computational chemist, and bioinformatician. I know several physicists, a historian and a math teacher who are now successful data scientists.
4. SQL skills are overrated
Another quote from the post: “Strong SQL skills are table stakes for data scientists and data engineers”. The thing is that in many cases, we use SQL mostly to retrieve data. Most of the “data scienc-y” work requires analytical tools and the flexibility that are not available in most of the SQL environments. Good familiarity with industry-standard tools and libraries are more important than knowing SQL. Statistics is way more important than knowing SQL. Julia Silge did indeed mention the tools (numpy/R) but didn’t emphasize them enough.
5. Communication importance is hard to overestimate
Again, quoting the post:
The ability to communicate effectively with people from diverse backgrounds is important.
Yes, Yes, and one thousand times Yes. Effective communication is a non-trivial task that is often overlooked by many professionals. Some people are born natural communicators. Some, like me, are not. If there’s one book that you can afford buying to improve your communication skills, I recommend buying “Trees, maps and theorems” by Jean-luc Doumont. This is a small, very expensive book that changed the way I communicate in my professional life.
6. It’s not that simple (part 2)
After giving some very general tips, Julia proceeds to suggest her readers checking out the data science jobs at StackOverflow Jobs site. The impression that’s made is that becoming a data scientist is a relatively simple task. It is not. At the bare minimum, I would mention several educational options that are designed for people trying to become data scientists. One such an option is Thinkful (I’m a mentor at Thinkful). Udacity and Coursera both have data science programs too. The point is that to become a data scientist, you have to study a lot. You might notice a potential contradiction between point 1 above and this paragraph. A short explanation is that becoming a data scientist takes a lot of time and effort. The post “Teach Yourself Programming in Ten Years” which was written in 2001 about programming is relevant in 2017 about data science.
Featured image is based on a photo by Jase Ess on Unsplash