Tag Archives: Madeleine Clare Elish

The future of designing autonomous systems will involve ethnographers


elish_photoNote from the Editor, Tricia Wang: Next up in our Co-designing with machines edition is Madeleine Clare Elish, (@mcette), is an anthropologist and researcher at Data & Society, presents a case for why current cultural perceptions of the role of humans in automated systems need to be updated in order to protect against new forms of bias and worker harms. Read more about her research on military drones and machine intelligence at Slate. Madeleine also works as a researcher with the Intelligence & Autonomy Initiative at Data & Society which develops empirical and historical research in order to ground policy debates around the rise of machine intelligence.

“Why would an anthropologist study unmanned systems?” This is a question I am often asked by engineers and product managers at conferences. The presumption is that unmanned systems (a reigning term in the field, albeit unreflexively gendered) are just that, free of humans; why would someone who studies humans take this as their object of study? Of course, we, as ethnographers, know there are always humans to be found.  Moreover, few if any current systems are truly “unmanned” or “autonomous.” [1] All require human planning, design and maintenance. Most involve the collaboration between human and machine, although the role of the human is often obscured. When we examine autonomous systems (or any of the other terms invoked in the related word cloud: unmanned, artificially intelligent, smart, robotic, etc) we must look not to the erasures of the human, but to the ways in which we, as humans, are newly implicated.

My dissertation research, as well as research conducted with the Intelligence and Autonomy Initiative at Data & Society, has examined precisely what gets obscured when we call something, “unmanned” or “autonomous.” I’ve been increasingly interested in the conditions and consequences for how human work and skill become differently valued in these kinds of highly automated and autonomous systems. In this post, Tricia has asked me to share some of the research I’ve been working on around the role of humans in autonomous systems and to work through some of the consequences for how we think about cooperation, responsibility and accountability.

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Modern Times, 1936 [giphy]

The Driver or the System?

Let me start with a story: I was returning to New York from a robot law conference in Miami. I ordered a Lyft to take me to the Miami airport, selecting the address that first populated the destination field when I typed the phrase “airport Miami” into the Lyft app. The car arrived. I put my suitcase in the trunk. I think the driver and I exchanged hellos–or at the very least, a nod and a smile. We drove off, and I promptly fell asleep. (It had been a long week of conferencing!) I woke up as we were circling an exit off the highway, in a location that looked distinctly not like the entrance to a major airport. I asked if this was the right way to the airport. He shrugged, and I soon put together that he did not speak any English. I speak passable Spanish, and again asked if we were going to the right place. He responded that he thought so. Maybe it was a back way? We were indeed at the airport, but not on the commercial side. As he drove on, I looked nervously at the map on my phone.

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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