In my last post, I lamented a bit the potential for advanced technologies to reinforce existing biases at a quicker and larger scale, without either our awareness nor consent.

The issue of algorithmically detecting gender that I briefly mentioned is one that I’ve been struggling with this semester. Inferring gender can be crucial in revealing lags at publications and harmful disparities in behavior where diversity data is not provided, yet the act of classification can be morally and socially harmful.

Nathan Matias is a Ph.D student at the neighboring Civic Media Lab who has been a great ally in helping soften the steep learning curve of grad school and also in tackling gender/identity related projects. His master’s thesis work in Open Gender Tracker has led to many great posts on the Guardian’s Datablog.

Like a truly great friend, he not only answered my questions with regard to ethically detecting gender, he wrote a comprehensive blogpost about it. He highlights the practical realities of keeping work both computationally effective and ethically responsible. A must-read for anyone considering work in this area. Thank you Nathan!

Tags: , ,

· · · ◊ ◊ ◊ · · ·

At the beginning of the seminar I’m taking in Race and Racism, we examined the historical origins of race– smirking at 19th century attempts to neatly classify and digest mankind into square boxes of color, things like Blumenbach‘s 5 categories (Caucasian, Mongolian, Ethiopian, American, Malay) and his offensive descriptions of the capabilities of each, how misguided they were, how clearly politically incorrect they are to us now.

One image stands out in particular– a chart, on the left axis, the races; on the right, a marking in each category of human competence, such that, summing up the checked boxes, one might literally rank different humans against another by racial category. The White man wins, in every grouping. It’s a glaring embodiment of early racist ideology, printed on paper, long since dismissed. It would be improbable to imagine a chart like that printed in a 21st century publication; the author would be fired, no doubt.

Yet the chart seemed eerily familiar to me. At first I couldn’t place it; I hadn’t seen it before. Then I realized: what it reminded me of was my own education, not in sociology or anthropology, but in Computer Science. As a graduate student, with a focus on Machine Learning and social applications, the very core of many of the algorithms I study and write is statistical classification, a major topic of research. What we are doing now– what Google does with its personalized search, what Facebook does with its unsettlingly accurate ads– is automating that same thought process of 19th century anthropologists. We look at a person, here, her technological imprint on the web, the traces she leaves, her purchase history, friends, and log-ins, then say: “what sort of human being are you”? And then, using our charts and tables– no longer printed ones, but weights on variables in our algorithms and databases of records– we first classify her, and after, yes, we rank her (sometimes we rank her first and then classify her, as well).

Users are commonly classified by gender, or what a machine can predict your gender to be, often quite accurately, and yes, different genders are ranked very differently. In the goods-driven world of online advertisement, a woman is worth differently than a man depending on what the product is, sometimes more, sometimes less. This entire pipeline is problematic to say the least; first, in the binary classification of gender; and then, in the ranking of different gendered individuals against one another; but our algorithms only reflect the constructs already driving these approaches.

Algorithms don’t quite yet classify users by race, or at least not outloud, because race is such a charged issue in most countries. But that doesn’t mean that when an algorithm doesn’t label a category outright, it doesn’t profile users. Algorithms, which learn, much like humans, based on history, only reinforce existing social constructs wherever they are used, because that is what they do: digest data, find a pattern, and make predictions according to that pattern. Harvard Professor Latanya Sweeney discovered that searches for racially-associated names were disproportionately causing targeted ads for criminal background checks and records to appear. These searches could go beyond offensive because targeted ads are reinforced by user behavior; if I click on that ad, I tell the machine that it was effective in targeting, reinforcing prejudiced thinking. Automated selection processes, which are beginning to gain popularity, such as automated admissions to schools and credit ratings, could cause real, harmful, physical ramifications.

This isn’t to say that there is anything inherently “evil” in Machine Learning itself; it’s a fascinating field of study, and could become a major tool in public health, disaster relief, and poverty alleviation. The machine is doing nothing new in its actions; it is merely a force multiplier of human behavior. I believe that classification is core to human cognition; and yes, we label others upon contact, always. Whether we like it or not, there is no way to escape being classified and classifying others. It’s impossible to meet someone without assigning some kind of underlying worth to them; it sounds ugly out loud, but our classification are essentially value-laden in order to be useful.

Ultimately, our machines only reflect our selves: it is vital to realize that computers are human, raised on human values, and there is no such thing as objective computation. The question that remains is: what kind of value systems will we feed our algorithms?

Tags: , , ,

· · · ◊ ◊ ◊ · · ·

Go here now, read and weep. To people who think that sexism in STEM is just hype, let me tell you that unfortunately, I have *NEVER* worked in a single lab or workplace of a technical nature where I have not experienced some sort of sexism– ranging from inappropriate comments to outright harassment. That is right– not a SINGLE lab or company. I would say my resume contains places people would consider inspiring, cutting edge, and liberal. Please be respectful and think about that.

Hattip @Mathbabe !

Tags: , ,

· · · ◊ ◊ ◊ · · ·

twitter X MIT

01 Oct 2014

we’re launched!

Tags: , , ,

· · · ◊ ◊ ◊ · · ·

twitter X MIT

01 Oct 2014

we’re launched!

Tags: , , ,

· · · ◊ ◊ ◊ · · ·

The slides from Democratizing Data Science, the vision paper that William, Ramesh, and I presented for KDD @Bloomberg on Sunday are now available online.

What a great first conference experience! Really interesting speakers and projects all around.

Take part in the conversation by tweeting at us (@mpetitchou, @tweetsbyramesh, @williampli) or putting your own opinions and experiences out there.

Tags: , ,

· · · ◊ ◊ ◊ · · ·

Guys! Guys! Guess what. Even though I’m practicing my April Ludgate glare in real life, today I’m going to be more like this. Why?

I co-wrote my first paper with two cool cats at MIT CSAIL, William Li and Ramesh Sridharan, and it got accepted to the KDD Conference as a highlight talk!

That means next Sunday, August 24th you can hear me taco ‘bout it in real life at 11am in the Bloomberg Building, 731 Lexington Avenue, NY, NY.

The theme of this year’s conference is “Data Mining for Social Good”, and our paper is a short vision statement on effecting positive social change with data science. We briefly define “Data Science”, ask 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.

The conference on Sunday at Bloomberg is free, and the line-up looks promising. 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!

For the full text of the paper, click here.

Tags: , ,

· · · ◊ ◊ ◊ · · ·

I’ve compiled a short list of resources on Sentiment Analysis, especially as applied to (political) debates. Check it out on the Govlab blog.

Tags: ,

· · · ◊ ◊ ◊ · · ·

I’ve compiled a short list of resources on Sentiment Analysis, especially as applied to (political) debates. Check it out on the Govlab blog.

Tags: ,

· · · ◊ ◊ ◊ · · ·

I’ve compiled a short list of resources on Sentiment Analysis, especially as applied to (political) debates. Check it out on the Govlab blog.

Tags: ,

· · · ◊ ◊ ◊ · · ·