I Came Out as Trans like a Data Scientist
By: Melissa Ingle
Tags: LGBTQ, Data Science

I am both a data scientist and a transgender woman. The two of course are completely unrelated and I only mention them together because I used the principles of one to help me deal with the messy realities of the other. I realize this description may be extremely eye roll-inducing to some, but please bear with me here.

What is data science?

At its core, data science is a software engineering field. It combines computer science with statistics to produce machine learning. The statistics predict the most likely outcome which is then checked against the real world. Did the algorithm get it right? Which subtle difference explained most of the data? The winning output can then be fed back into the algorithm and the process started all over again. Consider then that this can be repeated millions of times, across arbitrarily small distinctions all processed at the speed of modern supercomputers and you begin to see some of the power of this growing field.

Neural network with one hidden layer

What data science is NOT

As your resident transgender data scientist, I feel it is my duty to speak up and point out that — like most buzzed about technologies — data science is ripe for abuse. It’s great at certain things, like identifying patterns or maximizing trends. Is that image a cat or a dog? What promo should I offer my customers to maximize my profit?

A neural network is designed to learn, just like the human brain

But an algorithm can only work in probabilities. Which outcome is most likely? Around the time of the 2016 election we saw dubious claims from firms such as Cambridge Analytica that they could divine your entire personality based upon a few survey questions and more recently we’ve seen the deployment of crime-predicting AI (artificial intelligence) used by police forces across the country to identify likely criminals and arrest them before they’ve even committed a crime in a scenario straight out of Minority Report. And, as numerous observers have pointed out, algorithms reflect the biases of those who write them. If you think, even subconsciously, that minorities are more likely to commit a crime, or that women are unlikely to hold certain professions, that can show up in the likelihood you assign to certain parameters. And so, for the record allow me to state: If anyone claims a formula can tell you what is going to happen with certainty, that person is either ignorant or lying and you should immediately view them with suspicion.

How data science helped me

The deployment of a good data science model consists of a number of steps which help insure the success of the project. The diagram below illustrates one of several interpretations of those steps.

Steps in a data science project

Define the Goal

I’d kept any notion that I might be transgender hidden so deep for so long, I couldn’t be sure what was true. Of course, I’d had feelings since I was quite young, but the only signal I got from my community was that those feelings were wrong. So, in a reaction familiar to most in the LGBT community, I learned to tamp down all feelings and mannerisms. However, in an equally familiar outcome, this did not magically cause these feelings to go away. Neither did joining the Army where I jumped out of airplanes and fixed helicopters, getting married, earning my MBA, or fathering two children.

But here’s the complicated legacy: although these actions were born partially or completely out of my repressed emotions, I regard many of them as positive. I LOVE my children, they’re the center of my world. While I view my time in the Army with more mixed emotions there were many positive things to come out of that as well. I found myself having lived my life under constant shadow of these feelings and I could not easily separate where my personality began and my desperation to prove my masculinity ended. And so, five years ago, after my marriage had crashed to the ground, I started following the next step in the data science life cycle.

Get the Data

I had to confront these feelings directly. As much as they terrified me, I needed to see what they were. Almost a year after my divorce, I worked up the courage to call a therapist who specialized in gender. Gradually, over time and in this safe environment, I began to explore these emotions. She gave me permission to feel all the things I’d already been feeling my entire life. I began coming to therapy dressed as my true self. I purchased a wig and breast forms. I began to go to the local transgender support group, where I met all sorts of amazing people from various walks of life and all along the gender spectrum. I even marched in the local pride parade. I now had a pretty robust set of experiences indicating I enjoyed interacting with the world as a woman, but they were scattered and discrete. To put it in data science terms — my data were messy and I couldn’t draw any conclusions.

Clean the Data

In a typical data project, cleaning and exploring the data takes up 75% of the entire process. Data are often from disparate sources, with incomplete or extraneous information, and in a completely unusable format. This was true for my coming out process as well. I found myself paralyzed with indecision. I was never sure if I had enough experiences. I could never convince myself I was actually transgender. So, no matter how many support groups or therapy sessions I went to, no matter how many pride parades I marched in or how much I spent on female clothing, makeup, or accessories, I could never convince myself it was all real. I was stuck in this loop for over two years, trying to work up the courage to accept this part of myself.

Enrich the Data

In the world of data analytics, enriching the data allows you to get the most out of it. Joining columns, analyzing for bias, etc. Paradoxically, enriching can also mean reducing: throwing out unneeded or repetitive data that doesn’t help you draw insights or just muddies the water. You want to be left with just the data you need to tell the story you want to tell.

I had all of these experiences as a woman, but my data was what is known as “sparse”, a term used to describe a data set where a relatively high percentage of data cells are empty. I needed to get from sparse to dense. To illustrate, the table on the left tells you very little. However, in the table on the right, all extraneous data have been removed. With THIS table, we can finally start to get somewhere.

Sparse versus Dense Matrix

I’d never lived an entire, uninterrupted chunk of my life as a woman. If, in my life, the rows were the days and the columns the everyday activities (getting coffee in the morning, grocery shopping, laundry, dining out, etc), my matrix of time as a woman was very sparse indeed. I needed to make it dense and that meant spending extended periods of time as a woman. And so, in June of 2019 I flew to Houston to spend almost a full week as a woman with my sister. None of her friends there had ever met me before. They had no preconceived notions of who I was or was supposed to be. I could start over and see how people reacted to me as a woman. And I have to tell you — it was amazing! I’d never felt so at home, so calm, and so sure of myself. My sister and I went to the Houston Museum of Natural Sciences, stopped for a snack at a restaurant, met friends for brunch, and marched in a parade. If these weren’t exactly the run-of-the-mill day-to-day activities I’d be pursuing as a full-time woman, they were close enough. I had my answer: I was transgender and I had to transition, there was no other choice.

Find Insights and Visualize

When I got home, I needed a plan to come out, who, what, when, where, how, and I already knew the why. I researched a gender clinic to begin my medical transition. Though I’d been occasionally wearing women’s clothes and makeup, I kicked it into overdrive: every available moment I spent as my female self. And most importantly, I started formulating plans on how and to whom I needed to come out. Once I started taking female hormones I knew it was a matter of when not if.

Deploy Machine Learning and Iterate

Machine learning (ML) is where the rubber hits the road for data science. Where the theoretical becomes actual. It combines powerful statistical models with distributed cloud computers capable of performing billions of calculations per second. Furthermore, as the table below illustrates, different use cases require different algorithms. How is your data structured? What exactly do you want to find out?

I’m combining ML and iteration here, because in a data science project, iteration occurs after each model is run. Data scientists optimize for variables such as bias, variance, statistical power, and p-value, among others in a process known as model tuning. In short, you want your model to explain the greatest amount of data with the least amount of bias. If a process can be changed or a variable tweaked to give you better results, those changes should be made and the model run again. This is iteration.

Now, I’m not a computer. Not even close. But what I took from this is that I would need different approaches based on my best guesses of how people would react to my news. Along the way, I would tweak my statements to produce better results. Of course, I’d already come out to a handful of people: a trusted female friend (tearfully confessing over cocktails I might be transgender), a coworker who had lesbian parents, and my older sister who I knew would always support me and who lives her life out loud. They had given me valuable insight into what to say and when to say it. But now I needed to go wide. In data science parlance, I needed to stand up my model. I divided the remaining people into sections: parents, children, everyone else, in that order. Each presented their own unique challenges and each needed a unique strategy. They also needed to happen in quick succession. Once my parents knew I didn’t want them to have to keep it from my kids, and as anyone with experience with children can tell you, once kids know, there’s no secret at all.

Before telling my kids, I first needed to tell my ex-wife and co-parent so that we could develop a strategy to tell the kids together. She was… none too happy to hear the news. We have a fairly cooperative relationship at this point, focused on caring for our two children whose custody we split 50/50 but this would throw an entirely new element into our dynamic. At first she couldn’t look at me. I was asked to sit away from her at our children’s sporting events. But to her credit she’s a progressive, modern, educated woman living in San Francisco. She came around and just needed time. And so, in short order I sent emails to my parents and religious fundamentalist younger brother. Dreading the worst, I was incredibly relieved when they immediately responded with love and support. The literal next day I told my kids with my ex-wife present. They were shocked, and my son cried, but it’s amazing how accepting and judgment-free children can be. Within a few hours they had accepted it and we were back to our usual dynamic. They stayed at my place that night by prior agreement, I read them stories, and they even slept in my bed, which is typical for us on a weekend. With these experiences under my belt, the final reveal was simple: I wrote a heartfelt post on Facebook, hit send, and logged off all social media for the day.

Conclusion

I’d been terrified of this entire process, and not without good reasons. LGBT people face discrimination both systemic and personal. Until June of this year people could be fired from their job for the crime of being homosexual or transgender. In my life, I’ve been incredibly lucky. I have supportive parents, friends, and children. I have an ex-partner who is somehow willing to deal with me on even terms when it comes to our children, and believe me, I’ve heard horror stories of things not going so well in that department.

One way I dealt with this existential terror was to structure my plan like a data science model. I did a ton of research, planned for years, and followed the model. This approach will not work for everyone. LGBT people are as diverse as the nation as a whole, and each person must and will find their own path forward.

Sources

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