June 13, 2016 | 1 Comment

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

Letter 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.

giphy As 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

February 13, 2014 | 6 Comments

A case study on inclusive design: ethnography and energy use

Dr. Dan Lockton (@danlockton) is a senior associate at the Helen Hamlyn Centre for Design, at the Royal College of Art in London. Originally a design engineer, he became interested in including people better in design research while working on mobility products. For his PhD at Brunel University, he developed the Design with Intent toolkit, a multidisciplinary collection of design patterns around human behaviour which Tricia blogged about in 2011. Since then, he has worked on a number of domestic and workplace energy-related behaviour change projects, including CarbonCulture and currently SusLab, a large pan-European project. There is a ‘SusLab at the RCA’ blog; this article is based on the paper Dan presented at EPIC 2013.

Editors note: Energy usage and conservation can be a seemingly mundane part of an individual’s daily life on one hand, but a politically, ecologically, and economically critical issue on the other. Despite its importance, there is a startling lack of insight into what guides and influences behaviors surrounding energy.

With conventional quantitative analyses of properties and income explaining less than 40% of variations in households’ consumption, Dr Dan Lockton (@danlockton) and Flora Bowden set out to unpack some of the behavioral nuances and contextual insights around energy use within the daily lives of British households, from the perspective of design researchers. Their interviews had them meeting everyone from “quantified self” enthusiasts to low-income residents of public housing, and involving them in the design process. What they discovered bears significant implications for design which seeks to influence behaviors around energy, for example, where policy makers and utility companies see households as “using energy”, household members see their own behavior as solving problems and making their homes more comfortable, such as by running a bath to unwind after a trying day, or preparing a meal for their family.

Read on to see what else Dan and Flora learned in their ethnographic research, and how understanding “folk models” of energy – what energy “looks like” – may hold the key to curtailing energy usage.

For more posts from this EPIC edition curated by contributing editor Tricia Wang (who gave the opening keynoted talk at EPIC this year), follow this link.

A householder in Bethnal Green, East London, shows us her gas prepayment card.

It’s rare a day goes by without some exhortation to ‘reduce our energy use’: it’s a major societal and geo-political challenge, encompassing security, social issues and economics as well as environmental considerations. There is a vast array of projects and initiatives, from government, industry and academia all aiming to tackle different aspects of the problem, both technological and behavioural.

However, many approaches, including the UK’s smart metering rollout, largely treat ‘energy demand’ as something fungible—homogeneous even—to be addressed primarily through giving householders pricing-based feedback, with an assumption that they will somehow automatically reduce how much energy they use, in response to seeing the price. There is much less emphasis on understanding why people use energy in the first place—what are they actually doing?Read More… A case study on inclusive design: ethnography and energy use

January 15, 2014 | 15 Comments

I’m Coming Out: Four Awkward Conversations for Commercial Ethnographers

With an approach built on ethnography and design methodologies, Drew Smith (@drewpasmith) delights in bringing consumer and client to the conference table. In the process, he works with them to co-create game-changing products, services and businesses for some of the world’s biggest companies. Drew shapes culture and strategy at Seren Partners. He blogs occasionally at DownsideUpDesign and posts pictures of cars, mostly side-on, here.

Editor’s Note: I asked Drew Smith (@drewpasmith) to kick off our January EPIC theme because of his background as a designer and a tweet that he had sent. Until Drew attended EPIC 2103, he was hesitant to say that he was an ethnographer in certain professional contexts. But after listening to my opening keynote for EPIC 2013, he tweeted, “Today, I’m coming out. I’m an @ethnographer!” We had an interesting chat afterwards where Drew explained to me why he would even need to “come out of the closet.” It was a fascinating conversation and one that many readers will relate to, especially if you work in a design or strategy agency where you may be the one person with very proficient ethnographic skills.

So I thought it would be interesting to hear how someone with a strong design background experienced EPIC 2013. In Drew’s first guest post on Ethnography Matters, he urges designers and strategists with ethnographic skills be brave: commercial ethnography needs to come out of the closet. Drew provides some conversations that will help us get there.

For more posts from this January EPIC edition curated by contributing editor Tricia Wang, follow this link.

Over the course of my career I’ve developed an unwavering belief in the transformative power of ethnography. I’ve used its tools and techniques to bring about positive change for my clients, shaping products, services, businesses and brands with the rich, people-centred insight it can bring to bear.

Yet until recently, I’d never called myself an ethnographer; I’ve always been an automotive designer-turned-strategist. This is the story of how that came to change.

Ethnography by Another Name

During my student years, I’d come to know a London co-creation agency called Sense Worldwide. They had a mission to “make things better, by making better things”, a concept that was deeply appealing to an idealistic young designer.

We built trust and I allowed them to explore how I was using social networks (the early days of Facebook, the mid-life crisis of Gaydar) and why I was dreaming of upgrading my Sony Ericsson K750 to a Nokia N95. Together, we came up with ideas to make my world of mobile technology better. I loved the experience so much that I wanted to work for them.

Desperate, keen and with none of the ethnography or anthropology qualifications that usually accompanied their recruits, Sense Worldwide nevertheless took a chance. Without realising it, I became an ethnographer by the back door.

During my time there, I witnessed the profound impact that ethnographic research could have. The stories and insight pulled back from the field transformed not only the way new products and services were developed, but also how companies were led and run.

I noticed, however, that getting ethnography on the table with prospective clients was a challenge. It was often perceived as expensive and more than a little quirky. To ease the sales process, we adopted a series of jazz-handed 1-liners that got ethnography sold, perhaps overly so. Yes, we conducted ethnographic research, but sometimes our practice failed to live up to the over-the-top expectations set by language designed to hide our commercial awkwardness.Read More… I’m Coming Out: Four Awkward Conversations for Commercial Ethnographers

May 13, 2013 | 43 Comments

Big Data Needs Thick Data

Tricia Wang

Editor’s Note: Tricia provides an excellent segue between last month’s “Ethnomining” Special Edition and this month’s on “Talking to Companies about Ethnography.” She offers further thoughts building on our collective discussion (perhaps bordering on obsession?) with the big data trend. With nuance she tackles and reinvents some of the terminology circulating in the various industries that wish to make use of social research. In the wake of big data, ethnographers, she suggests, can offer thick data. In the face of derisive mention of “anecdotes” we ought to stand up to defend the value of stories.

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image from Mark Smiciklas at Intersection Consulting

Big Data can have enormous appeal. Who wants to be thought of as a small thinker when there is an opportunity to go BIG?

The positivistic bias in favor of Big Data (a term often used to describe the quantitative data that is produced through analysis of enormous datasets) as an objective way to understand our world presents challenges for ethnographers. What are ethnographers to do when our research is seen as insignificant or invaluable? Can we simply ignore Big Data as too muddled in hype to be useful?

No. Ethnographers must engage with Big Data. Otherwise our work can be all too easily shoved into another department, minimized as a small line item on a budget, and relegated to the small data corner. But how can our kind of research be seen as an equally important to algorithmically processed data? What is the ethnographer’s 10 second elevator pitch to a room of data scientists?

…and GO!

Big Data produces so much information that it needs something more to bridge and/or reveal knowledge gaps. That’s why ethnographic work holds such enormous value in the era of Big Data.

Lacking the conceptual words to quickly position the value of ethnographic work in the context of Big Data, I have begun, over the last year, to employ the term Thick Data (with a nod to Clifford Geertz!) to advocate for integrative approaches to research. Thick Data uncovers the meaning behind Big Data visualization and analysis.

Thick Data: ethnographic approaches that uncover the meaning behind Big Data visualization and analysis.

Thick Data analysis primarily relies on human brain power to process a small “N” while big data analysis requires computational power (of course with humans writing the algorithms) to process a large “N”. Big Data reveals insights with a particular range of data points, while Thick Data reveals the social context of and connections between data points. Big Data delivers numbers; thick data delivers stories. Big data relies on machine learning; thick data relies on human learning.