Manually Creating a Rudimentary Searchable Image Tagging System

This post is the third in a series of three posts about some changes I have been making in my personal life with respect to how I interact with online social media platforms. When I published the second post in this series, I was on the lookout for secure privacy-respecting cloud storage services. As of this post, I still haven't committed to a specific service. One of my requirements has been that the service should allow me to share certain files or folders securely with others. Unfortunately, unless I use a service like Google Drive which has just as little respect for data privacy as Facebook does, it isn't clear how I can easily tag images with details about people, location, and other comments in a way that I or others can easily search. My proposed solution, involving BASH scripts, is far from perfect, it is very much a work in progress, and it is arguably somewhat specific to my particular situation. Follow the jump to see more details.

Book Review: "Weapons of Math Destruction" by Cathy O'Neil

I've recently read Weapons of Math Destruction by Cathy O'Neil. It is a short but dense exposition into the various ways that computer algorithms can determine the courses of people's lives and exacerbate existing societal inequities and biases/prejudices, in areas like education, civic engagement, education, health at the workplace, and many others. It argues that while many algorithms used in big data can be used for good, many instead widen inequalities and reinforce various cycles of poverty because of their opacity, lack of accountability, poor use of statistics, and lack of critical examination by those in charge (who instead use results and predictions generated by such algorithms to fire workers, deny opportunities to potential employees, financially prey on poor people, and so on, making such predictions self-fulfilling prophecies as only confirmatory data is fed back in); in particular, many of these algorithms and models use questionable proxies to predict certain attributes or behaviors (especially when the desired attributes are hard to quantify but the proxies are easy), these models are rarely transparent in what inputs are collected and how they are manipulated to produce outputs, and further research and fine-tuning are rarely performed to correct models that most humans would recognize produce incorrect results (but which computers would miss). It concludes that extensions of existing regulations on use of health and financial data are needed to curtail the misuse of such algorithms, and that simultaneously data scientists need to be scrupulous about the ways that their work is used and developed.

I rather enjoyed reading this book: it's pretty fast-paced, yet gives many detailed examples of the abuse of these algorithms to form a compelling narrative. Additionally, it in many ways follows the book The Attention Merchants by Tim Wu (which I have previously reviewed), because as that book shows the various ways that companies collect and sell customer data, this book shows the various ways that data can be used for the benefit of those companies (even if that works against some of those customers). There are only two issues that I have with this book. One is that the few times that politics comes up, the author's political bias (in favor of liberals in the US) is obvious; perhaps this is just due to the nature of the author's passionate crusade against abuse of algorithms and for institutional action uplifting poor and marginalized people, as that would necessitate regulation of such mathematical instruments, which would be (and has been) loudly opposed by large corporations maintaining their short-term profits and long-term status quo through these algorithms as well as the conservative politicians that they support. The other is that there aren't too many examples of big data and related algorithms truly working toward greater socioeconomic equity, especially when such algorithms are finding patterns that wouldn't be found by humans; while I get that the author is trying to build a brief but dense narrative warning against the excesses and abuses of such algorithms (as she professes herself to not be a big data evangelist), I would have liked to see more nuanced examples of proper uses of big data, because as this book stands, it seems just as one-sided/polemical as uncritical big data evangelism. Overall, I certainly feel like I got a better sense of the potential dangers of unchecked and uncritical use of algorithms to shape the economy and society. Plus, now that I'm about halfway through my PhD, I've started to think more about the sorts of jobs that I'd like to take after I finish. I've decided that I don't want to go into finance because (as mentioned in this book too) I'm not comfortable with playing with other people's money, as it is too easy to be seduced by mathematical simplicity and elegance into doing questionable things. That said, one thing (among the many) that has caught my fancy has been studies of policy problems (especially as related to STEM fields, but as they affect ordinary people); however, the story in this book about the role of the Mathematica Policy Research company in developing the arbitrary and statistically unsound metrics for evaluating teachers in DC public schools has made me realize that I'll need to make sure if I end up joining a policy research organization/consultancy/think tank that the organization that I join is responsible and transparent about the data that it collects and processes as much as possible.

Apple to Third-Party iOS Developers: You Can Return Now

I got this from an OSNews submission on this. In a previous post on the subject, I lamented how Apple exercises near-total control over third-party developers' abilities to write applications, such as what programming language and tools they use.
Well folks, this just in from Apple itself: the requirements have been relaxed! Now, developers can use any tools and languages they want (as long as no other code is downloaded); in addition to this, Apple is being more transparent (or so it says, but we'll see about that) about its review process.
These are both huge pieces of good news for iOS developers who have been shut out of the process for reasons never fully made clear (until now). Hooray, and bravo Apple!

Programming is Creative

I was reading FSDaily (or something like that) a few days ago when I came across this submitted article.
The blog author posits in this article that the reason software shouldn't be patentable is because it is, at its core, a very large binary number. The author elaborates by saying that if one was to create a program that wrote a random series of binary digits to a file in an infinite loop and this program was repeated ad infinitum, eventually every program ever devised in the past, present, and future would be (re)created.
Upon reading this article at first, I was taken by the argument and by its simplicity. However, [this information is strictly private], I realized that this particular argument against software patents falls flat. This is because of the Infinite Monkey Theorem. (Wikipedia is an excellent resource with regard to this. For the readers who choose not to trouble themselves with clicking on another link, the Infinite Monkey Theorem states that if a monkey typed at a typewriter continuously for all of eternity, eventually the monkey would type out every work mankind has ever written. Traditionally, this is stated as "the monkey would eventually type out all the works of Shakespeare", and I will go with this statement for argument's sake.)
One could similarly posit that all the works of Shakespeare are merely very long strings of characters from a set of 27, and that they could similarly be taken at random. Yet this seems to make Shakespeare less of a genius.
Similarly, calling a computer program "just a number" demeans the work a programmer must put in to actually arrive at that "number".
Of course, this doesn't mean that software should be patentable. All this means is that programming is a creative work and programs aren't "just numbers" any more than books are just very long strings of letters, spaces, and punctuation; hence, the argument against software patents should not rest on calling software "just a number".