New: Privacy, Visibility, Anonymity: Dilemmas in Tech Use by Marginalised Communities

I started this Tactical Tech project two years ago and am thrilled to see it finally out. Research takes time! This is a synthesis report of two case studies we did in Kenya and South Africa on risks and barriers faced by marginalised communities in using technology (primarily in transparency and accountability work). You can download the report on the Open Docs IDS website here

The Tesla Crash

It’s happened. A person has died in a an accident involving a driverless car, raising difficult questions about what it means to regulate autonomous vehicles, to negotiate control and responsibility with software that may one day be very good, but currently is not.

In tracing an ethnography of error in driverless cars, I’m particularly interested in how error happens, is recorded, understood, regulated and then used as feedback in further development and learning. So the news of any and every crash or accident becomes a valuable moment to document.

What we know from various news reports is that 40 year old Joshua Brown was, supposedly, watching a Harry Potter DVD while test-driving his Tesla with the autopilot mode enabled, when the car slammed into the under-side of a very large trailer truck. Apparently the sensors on the car could not distinguish between the bright sky, and the white of the trailer truck. The top of the car was sheared off as it went fast under the carriage of the trailer and debris was scattered far.

Here’s an excerpt from a Reuters report of the crash from the perspective of a family whose property parts of the car landed in:

“Van Kavelaar said the car that came to rest in his yard next to a sycamore tree looked like a metal sardine can whose lid had been rolled back with a key. After the collision, he said, the car ran off the road, broke through a wire fence guarding a county pond and then through another fence onto Van Kavelaar’s land, threaded itself between two trees, hit and broke a wooden utility pole, crossed his driveway and stopped in his large front yard where his three daughters used to practice softball. They were at a game that day and now won’t go in the yard. His wife, Chrissy VanKavelaar, said they continue to find parts of the car in their yard eight weeks after the crash. “Every time it rains or we mow we find another piece of that car,” she said.”

People in the vicinity of a crash get drawn into without seeming to have a choice in the matter. Their perspective provides all kinds of interesting details and parallel narratives.

Joshua Brown was a Tesla enthusiast and had signed up to be a test driver. This meant he knew he was testing software; it wasn’t ready for the market yet. From Tesla’s perspective, what seems to count is how many millions of miles their car logged before an accident occurred, which may have not been the best way to lead with a report on Brown’s death.

Key for engineers is perhaps the functioning of the sensors that could not distinguish between a bright sky and a bright, white trailer. Possibly, the code analysing the sensor data hasn’t been trained well enough to make the distinction between the bright sky and a bright, white trailer. Interestingly, this is the sort of error a human being wouldn’t make; just as we know that humans can distinguish between a Labrador and a Dalmatian but computer programs are only just learning how to. Clearly, miles to go ….

A key detail in this case is about the nature of auto-pilot and what it means to engage this mode. Tesla clearly states that its auto pilot mode means that a driver is still in control and responsible for the vehicle:

“[Auto pilot] is an assist feature that requires you to keep your hands on the steering wheel at all times,” … you need to maintain control and responsibility for your vehicle while using it. Additionally, every time that auto pilot is engaged, the car reminds the driver to “Always keep your hands on the wheel. Be prepared to take over at any time.”

What Tesla is saying is that they’re not ready to hand over any responsibility or autonomy to machines. The human is still very much ‘in the loop’ with autopilot.

I suspect the law will need to wrangle over what auto pilot means in the context of road transport and cars as opposed to auto pilot in aviation; this history has been traced bt Madeleine Elish and Tim Hwang. They write that they “observe a counter intuitive focus on human responsibility even while human action is increasingly replaced by automation.”

There is a historical tendency to ‘praise the machine and punish the human’ for accidents and errors. Their recommendation is for a reframing of the question of accountability to increase the web of actors and their agencies rather than just vehicle and driver. They “propose that the debate around liability and autonomous systems be reframed more precisely to reflect the agentive role of designers and engineers and the new and unique kinds of human action attendant to autonomous systems. The advent of commercially available autonomous vehicles, like the driverless car, presents an opportunity to reconfigure regimes of liability that reflect realities of informational asymmetry between designers and consumers.”

This is so important and yet I find it difficult to see how, even as a speculative exercise, how you’d get Elon Musk to acknowledge his own role or that of his engineers and developers in accountability mechanisms. It will be interesting to watch how this plays out in the American legal system, because eventually there are going to have to be laws that acknowledge shared responsibility between humans and machines, just as robots needs to be regulated differently from humans.

The Problem with Trolleys at re:publica

I gave my first talk about ethics and driverless cars for a non-specialist audience at re:publica 2016. In this I look at the problem with the Trolley Problem, the thought experiment being used to train machine learning algorithms in driverless cars. Here, I focus on the problem that logic-based notions of ethics has transformed into an engineering problem; and suggest that this ethics-as-engineering approach is what will allow for American law and insurance companies to assign blame and responsibility in the inevitable case of accidents. There is also the tension that machines are assumed to be correct, except when they aren’t, and that this sits in a difficult history of ‘praising machines’ and ‘punishing humans’ for accidents and errors. I end by talking about questions of accountability that look beyond algorithms and software themselves to the sites of production of algorithms themselves.

Here’s the full talk.

Works cited in this talk:

1. Judith Jarvis Thompson’s 1985 paper in the Yale Law Journal,The Trolley Problem
2. Patrick Lin’s work on ethics and driverless cars. Also relevant is the work of his doctoral students at UPenn looking at applications of Blaise Pascal’s work to the “Lin Problem”
3. Madeleine Elish and Tim Hwang’s paper ‘Praise the machine! Punish the human!’ as part of the Intelligence & Autonomy group at Data & Society
4. Madeleine Elish’s paper on ‘moral crumple zones’; there’s a good talk and discussion with her on the website of the proceedings of the WeRobot 2016 event at Miami Law School.
5. Langdon Winner’s ‘Do Artifacts Have Politics’
6. Bruno Latour’s Actor Network Theory.

Experience E

How science represents the real world can be cute to the point of frustrating. In 7th grade mathematics you have problems like:

“If six men do a piece of work in 19 days, how many days will it take for 4 men to do the same work when two men are off on paternity leave for four months?”

Well, of course there was no such thing then of men taking paternity leave. But you can’t help but think about the universe of such a problem. What was the work? Were all the men the same, did they do the work in the same way, wasn’t one of them better than the rest and therefore was the leader of the pack and got to decided what they would do on their day off?

Here is the definition of machine learning according to one of the pioneers of machine learning, Tom M. Mitchell[1]:

“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E”

This can be difficult for a social scientist to parse because you’re curious about the experience E and the experience-r of experience E. What is this E? Who or what is experiencing E? What are the conditions that make E possible? How can we study E? And who set the standard of Performance P? For a scientist, Experience E itself is not that important, rather, how E is achieved, sustained and improved on is the important part. How science develops these problem-stories becomes an indicator of its narrativising of the world; a world that needs to be fixed.

This definition is the beginning of the framing of ethics in an autonomous vehicle. Ethics becomes an engineering problem to be solved by logical-probabilities executed and analysed by machine learning algorithms. (TBC)

[1] http://www.innoarchitech.com/machine-learning-an-in-depth-non-technical-guide/?utm_source=medium&utm_medium=post&utm_content=chapterlink&utm_campaign=republish

Past present: Revisiting the past through documentary

When you’re curating a program for yourself at an event or conference you’re often doing so consciously and conscientiously: there are things you need to see or attend for work, or for something new you need to wrap your head around. Then there are those times when it seems like you have no agenda except for entertainment and pleasure, which doesn’t mean, however, that your curated program is serendipitious or magical. This is what this week’s Berlinale is for me. I found myself curating one part of my program with some expected resonances: three films involving female protagonists reconstructing or re-discovering the past, and in doing so visit the unstable ground between, and in the creation of, fiction and non-fiction:

1. Kate plays Christine. Robert Greene. 2016.
In 1974 in Sarasota, Florida, a 29 year old newscaster, Christine Chubbuck, shot herself, fatally, on live TV. In 2015, an actress, Kate Lyn Sheil, prepares to recreate that moment and the film follows her journey.

2. A Magical Substance Flows Into Me. Jumana Manna. 2016
In the 1930s, Robert Lachmann, a German, had a radio show featuring “Oriental” i.e Palestinian music. In 2014, Jumana Manna, a Palestinian artist, travels around Israel and Palestine playing recordings from the old shows and recording contemporary versions. What do these songs sound like now when performed by Moroccan, Kurdish, or Yemenite Jews, by Samaritans, members of the urban and rural Palestinian communities, Bedouins and Coptic Christians?

3. The Watermelon Woman. Cheryl Dunye. 1996
Cheryl is a young black woman working at a video store. She becomes curious about black women playing stereotypical ‘mammys’ in films from the 1930s and 1940s. She sets out to discover one who is known only as the Watermelon Woman, a black lesbian actress who had an affair with a white woman director….

I’m excited to see them all and will be writing about them here..

Things at 3am, like Rohith Vemula

A name from an email, a name that I recognise as distinctly East African, Kenyan to be exact, buzzes in your head. It goes round and round because it is unusual and melodic. The name belongs to someone who attended a talk I gave at an event some years before. I gave her my email ID and said ‘write to me if you have any questions, or if you want to talk more, I’m great on email and terrible on Facebook’. However, she has only ever reached out to me to accept her LinkedIn request. [I have an inbox filter set to identify the word LinkedIN in a subject line and push them to the trash folder.] It occurs to me that the two most plaintive cries of our times are: “you’re breaking up!” and “Teresa Wambui sent you a Linked In Request.” I imagine a long line of LinkedIN requests waiting patiently to be accepted, long-suffering and hopeful like not attractive people on a dating site. I ignore all of those requests, because they aren’t really requests; they are intrusions generated en masse by someone else not reading the fine print, or for that matter, what’s on the box itself. I curse her and everyone who doesn’t know what the default means, that there is default setting on things. Perhaps even on the world as you encounter it. Like the world that seemed too much for Rohith Vemula to struggle on with any further. The stardust of his dreams catch in your throat and you think about every single way that caste privilege and power is casually and not-casually implicated in your ideas of the world, your self.

My mother, after being called North-Indian-Lower-Caste by the maamis in madsaars to the point where her name was changed on her own wedding invitation card to sound more South Indian and Brahmin, has become a naturalised Tam Brahm. I can hear it in her English and Tamil. For years she was judged and teased for not being able to produce the perfectly set curd or sambar. Personally I applaud her for this, though I know it has been the source of much self doubt for her. Of course every last tyrannical Brahminical madsaar-wearing Maami wanted her to be their doctor, and she gently and respectfully helped them reach the end.

There’s the way the Brahminical self is asserted, usually jokingly, about our gradual lapse into modernity. From eating beef in restaurants, to bringing cooked beef into the house, and the granddaughter of the no-beef-in-my-house grandmother producing the finest erchi oliyathatu ever. From rank alcoholism and domestic violence to genteel wine tasting tours of Napa. Marrying lower castes, Christians. No Muslims yet, but who knows. Some never marrying at all.

Then there are the smart “Paapan” genes, shorthand for a combination of privilege, access, pressure and expectation to become doctors and/or engineers who will eventually live The Good Life in America, far away from the heat and dust of Chennai, visiting only to look in on old parents and expose American-born children to their roots. It’s a little perverse, like spitting on your grandmother’s diamond earrings, to choose something else, something outlandish like Cultural Studies, Gender Studies, Activism.

Do young people have to die in India to make a point? First there was Jyoti Pandey and now Rohith Vemula. It seems that they do. The work of politics however is harder and more personal, and it’s something that I think you do in private, in the small gestures that no one sees. It is in questioning origin stories, speech, in what you’ve come to believe in as personal choices as really being about giving in to conditioning and pressure. The work on the self doesn’t stop if you want to live a considered, sensitive life.

Character assassination I, II, III

Character Assassination

n
the act of deliberately attempting to destroy a person’s reputation by defamatory remarks

I
I could write about the auburn-haired woman who I sit across from at work, the one with the tics and the lazy eye. She is an only child with that peculiar sense of phantom wholeness only children have. People think she is a bureaucrat, and it may be that I am the only one who can sense the evil lurking in her. She doesn’t take risks, which isn’t necessarily a bad thing; the world needs people who can look at things rationally and calmly for a long time before acting. She is someone out of a book written by Lionel Shriver about a family full of broken people. She would be the dark horse – or the roan maybe – quietly spinning lies and deceit in the corner and all the while seeming to be the most gentle. What is it like to get into the head of a character that you dislike and yet feel empathy for? I think I would write this character falling in love with a boy much below her class – these things are very important to the English, did I mention she was English – and did madcap things with him, like walk naked down the high street and almost get arrested for it.

II

I could write the heartbreaking story of my best friend who fell apart from anger following the tragic death of her roommate from breast cancer. The roommate was one of those unlucky young women – 31 when she was diagnosed – who unknowingly harboured a lump like a dark grudge. She was diagnosed and dead within six months. It was six months of my friend visiting her in the hospital, accompanying her to chemotherapy, comforting the boyfriend and the girl’s family. My friend couldn’t bring herself to attend the funeral or the memorial. She was so wrapped up in her own grief, it seemed at the time, that she couldn’t reach out to the roommate’s husband (the boyfriend married her while she was dying in hospital), sister or family. She mourned for weeks and the decision to stay in their shared apartment took an additional toll on her. Months later, when I couldn’t keep quiet about it any longer,I asked her what she needed to do to get over it and move on from the roommate’s death. A lot of Old Monk later, sprawled across the divan staring at the ceiling fan, a tear rolled out of the corner of her eye and she said that she fucking hated them all, her dead roommate’s family that is. They didn’t really thank her enough for all that she did and she has never forgiven them for it. She was furious that she had been “passed over” without enough praise and thanks. She felt used. She wasn’t going to get over it until they thanked her properly for everything she had done.

III
There is the woman with the watery grey eyes and a gaze so steady that I believe it gives her the power of endurance, as if she could stand in a light blizzard in her mustard yellow coat and not move for hours. She arrives at her studio-office every morning, which is on the ground floor of my apartment building. She is an illustrator for school text books. Every morning she has müsli and a cafe latte at the Swiss bakery and then goes to her studio after checking for mail, sometimes pausing to look at the junk mail. She makes herself a second cup of coffee, usually black because the milk has gone bad. She sniffs at the milk every morning. She is at her desk by five minutes to 9 o’clock. She spends a few minutes rearranging her papers, checking on her pencils, scanner, computer. She then gets down to work and does not move for three hours. She refills her coffee cup in a sort of daze and then returns to her desk. She is fixed, but fluid, for those three hours, sitting in one place but appearing to be very far away somewhere inside herself, or her work. At twelve o’clock she goes for a walk, and to eat lunch if she hasn’t brought a sandwich with her. She comes back looking alert, bright-eyed, and flushed as if she has been exerting herself by walking up a hill; the approach to our building is flat however. Her afternoon routine is in complete contrast to her morning one in that there is no routine. It’s difficult to know how she will spend her afternoons. Some days she just reads, other days she types furiously at her computers, and some days she browses through what must be clickbait – there’s a sort of glazed look in her eyes as her index finger clicks through at a regular beat. The days she reads she revisits some of the morning’s deep torpor, unmoving, lost in what she is doing. And then there are those days when she lies on the chaise lounge and cries. This is preceded by a lazy pacing of the studio, staring at the floor and then collapsing into the chair with deep sobs that seem to come from very deep within and wrack her narrow frame. She seems to be able to cry for hours on end, sustaining herself through a particular rhythm. Each long, slow wave of tears building up to a crescendo as if the memories or feelings come faster and harder like contractions, they take hold of her and she seems to be as if possessed for she can seem to go on crying for a while at a loud, fevered pace. Then it ebbs and you can see her gasping for breath, realising her own tiredness, eventually stopping with a series of whimpers and falling back till the next fresh wave crashes over her. Hours later, exhausted, she falls into a deep sleep. She leaves the studio every evening at five o’clock.