For a while now I’ve been thinking I should build a decision tree for deciding which algorithm to use on a given data project. And yes, I think it’s kind of cool that “decision tree” would be an outcome on my decision tree. Kind of like a nerd pun.
I’m happy to say that I finally started work on my algorithm decision tree, thanks to this website called gliffy.com which allows me to build flowcharts with an easy online tool. It was one of those moments when I said to myself, this morning at 6am, “there should be a start-up that allows me to build a flowchart online! Let me google for that” and it totally worked. I almost feel like I willed gliffy.com into existence.
So here’s how far I’ve gotten this morning:
I looked around the web to see if I’m doing something that’s already been done and I came up with this:
I appreciate the effort but this is way more focused on the size of the data than I intend to be, at least for now. And here’s another one that’s even less like the one I want to build but is still impressive.
Because here’s what I want to focus on: what kind of question are you answering with which algorithm? For example, with clustering algorithms you are, you know, grouping similar things together. That one’s easy, kind of, although plenty of projects have ended up being clustering or classifying algorithms whose motivating questions did not originally take on the form “how would we group these things together?”.
In other words, the process of getting at algorithms from questions is somewhat orthogonal to the normal way algorithms are introduced, and for that reason taking me some time to decide what the questions are that I need to ask in my decision tree. Right about now I’m wishing I had taken notes when my Lede Program students asked me to help them with their projects, because embedded in those questions were some great examples of data questions in search of an algorithm.
Please give me advice!
I’ve was away over the weekend (apologies to Aunt Pythia fans!) and super busy yesterday but this morning I finally had a chance to read Ethan Zuckerman’s Atlantic piece entitled The Internet’s Original Sin, which was sent to me by my friend Ernest Davis.
Here’s the thing, Zuckerman gets lots of things right in the article. Most importantly, the inherent conflict between privacy and the advertisement-based economy of the internet:
Demonstrating that you’re going to target more and better than Facebook requires moving deeper into the world of surveillance—tracking users’ mobile devices as they move through the physical world, assembling more complex user profiles by trading information between data brokers.
Once we’ve assumed that advertising is the default model to support the Internet, the next step is obvious: We need more data so we can make our targeted ads appear to be more effective.
This is well said, and important to understand.
Here’s where Zuckerman goes a little too far in my opinion:
Outrage over experimental manipulation of these profiles by social networks and dating companies has led to heated debates amongst the technologically savvy, but hasn’t shrunk the user bases of these services, as users now accept that this sort of manipulation is an integral part of the online experience.
It is a mistake to assume that “users accept this sort of manipulation” because not everyone has stopped using Facebook. Facebook is, after all, an hours-long daily habit for an enormous number of people, and it’s therefore sticky. People don’t give up addictive habits overnight. But it doesn’t mean they are feeling the same way about Facebook that they did 4 years ago. People are adjusting their opinion of the user experience as that user experience is increasingly manipulated and creepy.
An analogy should be drawn to something like smoking, where the rates have gone way down since we all found out it is bad for you. People stopped smoking even though it is really hard for most people (and impossible for some).
We should instead be thinking longer term about what people will be willing to leave Facebook for. What is the social networking model of the future? What kind of minimum privacy protections will convince people they are safe (enough)?
And, most importantly, will we even have reasonable minimum protections, or will privacy be entirely commoditized, whereby only premium pay members will be protected, while the rest of us will be thrown to the dogs?
“Data Science” is one of my least favorite tech buzzwords, second to probably “Big Data”, which in my opinion should be always printed followed by a winky face (after all, my data is bigger than yours). It’s mostly a marketing ploy used by companies to attract talented scientists, statisticians, and mathematicians, who, at the end of the day, will probably be working on some sort of advertising problem or the other.
Still, you have to admit, it does have a nice ring to it. Thus the title Democratizing Data Science, a vision paper which I co-authored with two cool Ph.D students at MIT CSAIL, William Li and Ramesh Sridharan.
The paper focuses on the latter part of the situation mentioned above. Namely, how can we direct these data scientists, aka scientists who interact with the data pipeline throughout the problem-solving process (whether they be computer scientists or programmers or statisticians or mathematicians in practice) towards problems focused on societal issues?
In the paper, we briefly define Data Science (asking ourselves what the heck it even means), then question what it means to democratize the field, and to what end that may be achieved. In other words, the current applications of Data Science, a new but growing field, in both research and industry, has the potential for great social impact, but in reality, resources are rarely distributed in a way to optimize the social good.
We’ll be presenting the paper at the KDD Conference next Sunday, August 24th at 11am as a highlight talk in the Bloomberg Building, 731 Lexington Avenue, NY, NY. It will be more like an open conversation than a lecture and audience participation and opinion is very welcome.
The conference on Sunday at Bloomberg is free, although you do need to register. There are three “tracks” going on that morning, “Data Science & Policy”, “Urban Computing”, and “Data Frameworks”. Ours is in the 3rd track. Sign up here!
If you don’t have time to make it, give the paper a skim anyway, because if you’re on Mathbabe’s blog you probably care about some of these things we talk about.
Everyone I know who codes uses stackoverflow.com for absolutely everything.
Just yesterday I met a cool coding chick who was learning python and pandas (of course!) with the assistance of stackoverflow. It is exactly what you need to get stuff working, and it’s better than having a friend to ask, even a highly knowledgable friend, because your friend might be busy or might not know the answer, or even if your friend knew the answer her answer isn’t cut-and-paste-able.
If you are someone who has never used stackoverflow for help, then let me explain how it works. Say you want to know how to load a JSON file into python but you don’t want to write a script for that because you’re pretty sure someone already has. You just search for “import json into python” and you get results with vote counts:
Also, every math nerd I know uses and contributes to mathoverflow.net. It’s not just for math facts and questions, either, there are interesting discussions going on there all the time. Here’s an example of a comment in response to understanding the philosophy behind the claimed proof of the ABC Conjecture:
OK well hold on tight because now there’s a new online forum, but not about coding and not about math. It’s about all the other STEM subjects, which since we’ve removed math might need to be called STE subjects, which is not catchy.
So far only statistics is open, but other stuff is coming very soon. Specifically it covers, or soon will cover, the following fields:
- Cognitive Sciences
- Computer Sciences
- Earth and Planetary Sciences
- Science & Math Education
- History of Science and Mathematics
- Applied Mathematics, and
I’m super excited for this site, it has serious potential to make peoples’ lives better. I wish it had a category for Data Sciences, and for Data Journalism, because I’d probably be more involved in those categories than most of the above, but then again most data science-y questions could be inserted into one of the above. I’ll try to be patient on this one.
Here’s a screen shot of an existing Stats question on the site:
There’s an interesting and horrible New York Time story by Jessica Silver-Greenberg about a PayDay loan syndicate being run out of New York State. The syndicate consists of twelve companies owned by a single dude, Carey Vaughn Brown, with help from a corrupt lawyer and another corrupt COO. Manhattan District Attorneys are charging him and his helpers with usury under New York law.
The complexity of the operation was deliberate and intended to obscure the chain of events that would start with a New Yorker online looking for quick cash online and end with a predatory loan. They’d interface with a company called MyCashNow.com, which would immediately pass their application on to a bunch of other companies in different states or overseas.
Important context: in New York, the usury law caps interest rates at 25 percent annually, and these PayDay operations were charging between 350 and 650 percent annually. Also key, the usury laws apply to where the borrower is, not where the lender is, so even though some of the companies were located (at least on paper) in the West Indies, they were still breaking the law.
They don’t know exactly how big the operation was in New York, but one clue is that in 2012, one of the twelve companies had $50 million in proceeds from New York.
Here’s my question: how did MyCashNow.com advertise? Did it use Google ads, or Facebook ads, or something else, and if so, what were the attributes of the desperate New Yorkers that it looked for to do its predatory work?
One side of this is that vulnerable people were somehow targeted. The other side is that well-off people were not, which meant they didn’t see ads like this, which makes it harder for people like the Manhattan District Attorney to even know about shady operations like this.
There was a recent New York Times op-ed by Sonja Starr entitled Sentencing, by the Numbers (hat tip Jordan Ellenberg and Linda Brown) which described the widespread use – in 20 states so far and growing – of predictive models in sentencing.
The idea is to use a risk score to help inform sentencing of offenders. The risk is, I guess, supposed to tell us how likely the person is to commit another act in the future, although that’s not specified. From the article:
The basic problem is that the risk scores are not based on the defendant’s crime. They are primarily or wholly based on prior characteristics: criminal history (a legitimate criterion), but also factors unrelated to conduct. Specifics vary across states, but common factors include unemployment, marital status, age, education, finances, neighborhood, and family background, including family members’ criminal history.
I knew about the existence of such models, at least in the context of prisoners with mental disorders in England, but I didn’t know how widespread it had become here. This is a great example of a weapon of math destruction and I will be using this in my book.
A few comments:
- I’ll start with the good news. It is unconstitutional to use information such as family member’s criminal history against someone. Eric Holder is fighting against the use of such models.
- It is also presumably unconstitutional to jail someone longer for being poor, which is what this effectively does. The article has good examples of this.
- The modelers defend this crap as “scientific,” which is the worst abuse of science and mathematics imaginable.
- The people using this claim they only use it for as a way to mitigate sentencing, but letting a bunch of rich white people off easier because they are not considered “high risk” is tantamount to sentencing poor minorities more.
- It is a great example of confused causality. We could easily imagine a certain group that gets arrested more often for a given crime (poor black men, marijuana possession) just because the police have that practice for whatever reason (Stop & Frisk). Then model would then consider any such man at a higher risk of repeat offending, but that’s not because any particular person is actually more likely to do it, but because the police are more likely to arrest that person for it.
- It also creates a negative feedback loop on the most vulnerable population: the model will impose longer sentencing on the population it considers most risky, which will in turn make them even riskier in the future, if “length of time in prison previously” is used as an attribute in the model, which is surely is.
- Not to be cynical, but considering my post yesterday, I’m not sure how much momentum will be created to stop the use of such models, considering how discriminatory it is.
- Here’s an extreme example of preferential sentencing which already happens: rich dude Robert H Richards IV raped his 3-year-old daughter and didn’t go to jail because the judge ruled he “wouldn’t fare well in prison.”
- How great would it be if we used data and models to make sure rich people went to jail just as often and for just as long as poor people for the same crime, instead of the other way around?
There’s a CNN video news story explaining how the NYC Mayor’s Office of Data Analytics is working with private start-up Placemeter to count and categorize New Yorkers, often with the help of private citizens who install cameras in their windows. Here’s a screenshot from the Placemeter website:
You should watch the video and decide for yourself whether this is a good idea.
Personally, it disturbs me, but perhaps because of my priors on how much we can trust other people with our data, especially when it’s in private hands.
To be more precise, there is, in my opinion, a contradiction coming from the Placemeter representatives. On the one hand they try to make us feel safe by saying that, after gleaning a body count with their video tapes, they dump the data. But then they turn around and say that, in addition to counting people, they will also categorize people: gender, age, whether they are carrying a shopping bag or pushing strollers.
That’s what they are talking about anyway, but who knows what else? Race? Weight? Will they use face recognition software? Who will they sell such information to? At some point, after mining videos enough, it might not matter if they delete the footage afterwards.
Since they are a private company I don’t think such information on their data methodologies will be accessible to us via Freedom of Information Laws either. Or, let me put that another way. I hope that MODA sets up their contract so that such information is accessible via FOIL requests.