Tag Archives: Astrid Countee

An Engineering Anthropologist: Why tech companies need to hire software developers with ethnographic skills


1955a58Note from the Editor, Tricia Wang: I’m very please to announce that the next contributor in the Co-designing with machines edition is Astrid Countee (@ianthro), an anthropologist, software developer, data analyst and writer all-in-one. In this article, Astrid illustrates how being an anthropologist makes her a better developer, and argues that the gap between the social science and tech must be bridged to reach new innovations. Her article echoes themes brought up in edition contributors Stephen Gustafson’s and Che-Wei Wang’s post – where both authors discuss the importance of the human side of AI innovations. As a long time fan of Astrid’s work, I’m also excited that we get to hear her recount her journey of starting out as neurosurgeon student to becoming an anthropologist and then a software programmer. She is an organizer for Rails Girls, a workshop that teaches girls and women how to code. Her newly available book, Family Talk and Chronic Disease, a practical guide for black families to manage diabetes and hypertension. She is currently pursuing a masters in computer science and math. Read more of Astrid’s writing at Ianthro.

photo by Martin QuirozI did not always have dreams of being a software engineer. For a very long time I dreamed only of being a surgeon. I was fascinated with medicine, and longed to be able to help people from the inside out. It was with this singular focus that I entered college as a forensic science pre-med major and started down a path that I thought for sure would end with me in the operating room.

But my fate was changed, at first very slowly and then with a quickness. The first couple of years in school had been rough on me. It made me question if I was doing the right thing since I wasn’t enjoying my major as much as I thought I would. I didn’t like the way that the natural sciences taught through memorization. I was interested in discovery, and wanted the challenge of making something new, rather than learning how things already worked. All of these things were small nuisances at first, but they let to me deciding  to drop my pre-med designation. I was now free to take classes that I found interesting. I found a better fit studying psychology, neuroscience and linguistics. Then I took my first anthropology class. This ushered in the quick change. I found the discipline that I would continue to study in graduate school, and a worldview that gave me the chance to discover. I loved the integration of natural science, philosophy with art and history. It allowed my mind to see the world from a new angle.

While working on my graduate degree, I also worked full time at a data company. It was at this company that I learned about technology and my love and affinity for it. I learned how to run queries, how to build databases, and how to manipulate data in ways I had never thought to before. It was a great compliment to my graduate studies as a medical anthropologist. It was also at this company that the seeds were planted that lead me to become a software engineer. It was that same sense of discovery combined with tools to build what I wanted into existence.

I found ways to apply anthropology to everything that I did, including software. Anthropology and software are not exactly peanut butter and jelly, but they do maintain a delicate balance to innovation.

Digging into Anthropology

Anthropology is a broad discipline concerned with techniques like ethnography, often using grounded theory, where you go out into the field and allow a culture to tell you who they are and how they do things. It is a science unlike any other in that what you can study nearly knows no bounds.  The vastness of the discipline trains you to see universal patterns. Everything is understood as belonging to a system. It is through understanding the system that you can find your footing in something unfamiliar, and find your way through it. It is no wonder that when I started working as a software engineer, I was drawn to DevOps and systems engineering. My anthropological training lead me straight to the framework for how technology works.

I know the value of holism, of seeing how one piece affects another. It is an obvious thing that often gets ignored when building technical systems. People often think of technology as machines talking to machines. And while that is true at some level in the technology stack, building software is more about people than anything else. There are people who are using the systems, there are people who are architecting the systems. There are people who are writing the software. The human footprint can be found everywhere you turn. So, it makes sense that humanistic thinking in software is revolutionary. It is the reason why Apple can change the world by taking their iPod and attaching it to a cell phone. 10 years ago smart phones were an extremely small part of the market. Now, in the western world, it is likely that there are more smart phones and tablets in a home than there are personal computers. It isn’t by accident, or only by great marketing. It is by using technology to tap into a holistic system. These systems exists around us all the time, and an anthropologist is trained to root them out, understand them, and predict how they will change.

Gearing up with Engineering

But like any balanced equation, being a software developer has changed my view as an anthropologist as well. My training, even as an applied practitioner was not nearly as project driven as my work as a software engineer. In order to break down the problems I am looking at, it is helpful to start doing something, in order to understand it. Even if that means sketching out the chain of events that I am trying to fix, action is a virtue. You are a software engineer because you write working programs. That’s it. No peer-reviewed work, no list of accolades to prove your value. That intentional execution has influenced the way that I think about problem solving. It forces me to get deep into the dirty work much sooner. It also means becoming expert at shrinking big problems down to size. The only way to eat the elephant is one bite at a time. No one knows that better than a software engineer. It is a huge part of the job to dissect what you are doing down to small chunks of solvable problems.  Being in the thick of it is what I loved about being an anthropologist. Being a software engineer takes that to a whole new level.Read More… An Engineering Anthropologist: Why tech companies need to hire software developers with ethnographic skills

Co-designing with machines: moving beyond the human/machine binary



web-7525squareLetter from the Editor: I am happy to announce the The Co-Designing with Machines edition. As someone with one foot in industry redesigning organizations to flourish in a data-rich world and another foot in research, I’m constantly trying to take an aerial view on technical achievements. Lately, I’ve been obsessed with the future of design in a data-rich world increasingly powered by of artificial intelligence and its algorithms. What started out over a kitchen conversation with my colleague, Che-Wei Wang (contributor to this edition) about generative design and genetic algorithms turned into a big chunk of my talk at Interaction Design 2016 in Helsinki, Finland. That chunk then took up more of a my brain space and expanded into this edition of Ethnography Matters, Co-designing with machines. In this edition’s introductory post, I share a more productive way to frame human and machine collaboration: as a networked system. Then I chased down nine people who are at the forefront of this transformation to share their perspectives with us. Alicia Dudek from Deloitte will kick off the next post with a speculative fiction on whether AI robots can perform any parts of qualitative fieldwork. Janet Vertesi will close this edition giving us a sneak peak from her upcoming book with an article on human and machine collaboration in NASA Mars Rover expeditions. And in between Alicia and Janet are seven contributors coming from marketing to machine learning with super thoughtful articles. Thanks for joining the ride! And if you find this to be engaging, we have a Slack where we can continue the conversations and meet other human-centric folks. Join our twitter @ethnomatters for updates. Thanks. @triciawang

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Who is winning the battle between humans and computers? If you read the headlines about Google’s Artificial Intelligence (AI), DeepMind, beating the world-champion Go player, you might think the machines are winning. CNN’s piece on DeepMind proclaims, “In the ultimate battle of man versus machine, humans are running a close second.” If, on the other hand, you read the headlines about Facebook’s Trending News Section and Personal Assistant, M, you might be convinced that the machines are less pure and perfect than we’ve been led to believe. As the Verge headline puts it, “Facebook admits its trending news algorithm needs a lot of human help.”

The headlines on both sides are based in a false, outdated trope: The binary of humans versus computers. We’re surrounded by similar arguments in popular movies, science fiction, and news. Sometimes computers are intellectually superior to humans, sometimes they are morally superior and free from human bias. Google’s DeepMind is winning a zero-sum game. Facebook’s algorithms are somehow failing by relying on human help, as if collaboration between humans and computers in this epic battle is somehow shameful.

The fact is that humans and computers have always been collaborators. The binary human/computer view is harmful. It’s restricting us from approaching AI innovations more thoughtfully. It’s masking how much we are biased to believe that machines don’t produce biased results. It’s allowing companies to avoid taking responsibility for their discriminatory practices by saying, “it was surfaced by an algorithm.” Furthermore, it’s preventing us from inventing new and meaningful ways to integrate human intelligence and machine intelligence to produce better systems.

giphyAs computers become more human, we need to work even harder to resist the binary of computers versus humans. We have to recognize that humans and machines have always interacted as a symbiotic system. Since the dawn of our species, we’ve changed tools as much as tools have changed us. Up until recently, the ways our brains and our tools changed were limited to the amount of data input, storage, and processing both could handle. But now, we have broken Moore’s Law and we’re sitting on more data than we’re able to process. To make the next leap in getting the full social value out of the data we’ve collected, we need to make a leap in how we conceive of our relationships to machines. We need to see ourselves as one network, not as two separate camps. We can no longer afford to view ourselves in an adversarial position with computers.

To leverage the massive amount of data we’ve collected in a way that’s meaningful for humans, we need to embrace human and machine intelligence as a holistic system. Despite the snazzy zero-sum game headlines, this is the truth behind how DeepMind mastered Go. While the press portrayed DeepMind’s success as a feat independent of human judgement, that wasn’t the case at all. Read More… Co-designing with machines: moving beyond the human/machine binary