OWNW Talk on Affective Infrastructure and #MeToo: Script

Here is the full text of the talk I gave at the first salon organised by the new Otherwise Network in Berlin on April 6, 2019. You can watch the talk here:

(Thank yous – Otherwise Network. Audience)

Humbling to have the space to articulate ideas and think with people.

And especially on such a beautiful Spring day when you could be outdoors.

This talk will have three parts and I hope to lead you all through it gently and smoothly. There is theory and if theory is not your thing, then just hold tight and breathe, just like when there is turbulence and you’re sitting at the back of the plane.

One. Giving Data to Life

in the beginning was data, but it was small
the personal story, the case study, the story told through sobs after the third drink. or not at all.

Quote. You taught me my first workplace lesson. I was 23, you were 43. I grew up reading your smart opinions and dreamt of being as erudite as you. You were one of my professional heroes. Turns out you were as talented a predator as you were a writer. It was more date, less interview. … I escaped that night, you hired me, I worked for you for many months even though I swore I would never be in a room alone with you again. Endquote. (By Priya Ramani calling out abuse by MJ Akbar)

Women who work at microsoft on the Xbox team say that they have been called ‘bitch’ at work at least once. Another refused to sleep with a colleague and was threatened with death. Yet another was asked to sit on a colleague’s lap at a meeting with HR and executives present and no one said anything.

About 18 months ago, these small data became a hashtag.
the data became a flood
the details didn’t matter
you just had to say two words.
the data had volume velocity variety
so then we could call it big data.

But like big data, it was not raw, but it was made of things that felt raw.
but, data is always cooked. it always comes from someone making choices about what to put in and what to keep out.

it is no surprise that the most popular and dominant forms for organising information – the spreadsheet, the dataset, the hashtag – have converted what was always my story and your story, into a data story. the body had to become data so that it could be seen.

so we had the shitty men in media list
the shitty men in the arts list
the list of sexual harassers in academia
the hollywood list
the bollywood list

let me talk about just one list, because all data are different and have their own histories.

in 2017, two Dalit feminist students of Indian origin, Raya sarkar and inji pennu, both in California, put up a spreadsheet called LoSha List of Sexual Harassers in Academia. The include 50-odd names of Indian men academics living in India, Europe, the UK and the US and included some illustrious names. Before our eyes the spreadsheet expanded to 70+.

(Disclosure: I have a friend whose name is on the list. Also an acquaintance who lives here in Berlin)

The list had just a few columns
Who
What
Where
When

data, which offers all kinds of analytical possibilities instead was referred to as empty, desperate, chaotic, and anarchic. it made no sense to just make a list online.

and the thing is, people have been making accusations for a long time. Like in the whisper network. but like really great infrastructure, it is mostly invisible. that’s its point. but it was not enough to just talk among ourselves. something is significantly broken.

The first thing that happened in response to the LoSHA list was that a group of senior Indian feminist academics wrote an open letter in defence of one person on the list, one of their friends, The old feminists were suspicious of social media, of accusations that could be made on google spreadsheets, without “due process”. It is important to note that most of these older academics have been closely involved in social justice movements and activism on rape, sexual harassment, dowry deaths, honor killings and so on. Some of them have been dear mentors and inspirations.

The younger women responded: you oldies are obsolete. your stupid blog is obsolete. your networks are obsolete. the women responded that they had taken to social media and the internet because nothing else had made good on its promise of due process.

So the immediate outcome of the list of sexual harassers in academia was actually a conflict about whose feminism was purer.

This is not unlike the moment three years ago when a group of people set up a website detailing Jacob Appelbaum’s sexually abusive behaviour and sexual bullying. The first thing that happened was that a group of six women wrote a response quite similar to the one the Indian feminist academics did. The website, Ourresponse.org is now not available and has anime characters dancing across it instead.

But what I really want to get to here is what happens the day after the revolution, when the maidan, the square, has to be cleared of torn posters and coffee cups? It is not actually as simple as making allegations online. People who make allegations of sexual abuse know this already.

Women are having to quit jobs, and find new ones, to organise evidence in cases where things do go to court, or having to go through internal complaints committees, or having to withstand online and offline attacks for making allegations, find emotional resilience to go through the aftermath of putting your story out there. They are sharing information, preparing with and supporting each other, encouraging each other in WhatsApp groups, email lists, offline. This is the work of care and support that comes after.

The other big shift taking place is the flowering of uncertainty and unhappiness, a discomfort about how broken sociality is. Consider this.

The actor Henry Cavill says in an interview to Australian GQ.

quote It’s very difficult to do that if there are certain rules in place. Because then it’s like: ‘Well, I don’t want to go up and talk to her, because I’m going to be called a rapist or something’. So you’re like, ‘Forget it, I’m going to call an ex-girlfriend instead, and then just go back to a relationship, which never really worked’. But it’s way safer than casting myself into the fires of hell, because I’m someone in the public eye, and if I go and flirt with someone, then who knows what’s going to happen? End quote

So are having to deal with hesitation, awkwardness, self consciousness, nervousness, uncertainty, fear. This emotional management calls for a coordination of mind and feeling, watchfulness, an awareness of body, a re-negotiation of social scripts, a struggle to find the right words.
How might the difficult, uncertain, awkward aspects of intimacy might offer new territories for thinking about the emotional management we have to be doing to create new ways of relating.

I do not want to valorise some old fashioned gendered notion of romance that existed in the past and say that was a kinder gentler time and that things have become complicated now. It was not better back then, it never was, it was just a time when women were either silent or muttered to each other under their breaths.

In moments of uncertainty, we rush to establish things that reassure us. So consider the rise of sexual consent apps like Good 2 Go and SaSie. Or my favourite, Legalfling that puts consent on the blockchain.

And I quote from their website:

“Communication and understanding is key. Talking about rules and boundaries before sex can be awkward and uncomfortable for some. Having an app that clearly shows the rules of engagement as well as personal preferences, can remove misunderstandings and prevent unintentional bad situations.”

or Netflix passes a five second rule saying their employees cannot make eye contact for longer than 5 minutes or stay in a lingering hug.

Two.

slide :the world is in bad shape and I am overwhelmed: – agnes varda

I want to consider MeToo and its aftermath as a kind of affective infrastructure. And this is the part where I draw on Lauren Berlant’s concept of the same name.

But first we have to work really hard to dismantle our idea that infrastructure is material. That infrastructure is roads, water systems, nuclear power plants, airlines, museums. Brian Larkin, Susan Leigh Star and others reminds us that infrastructure is as much things as the relationships between things. It is values and symbols, beliefs that sustain organisations, it is a multiplicity of cultural factors.

Even things like roads and highways are about cultural values of freedom, autonomy, and individuality. The cultural history of US highway infrastructure and engineering during the Cold War is a particularly interesting little rabbit hole.

Infrastructure is things, it is people says Abdoumaliq Simone. If you look at how roads are built, in India, it often starts with a thin man in a thin cotton shirt, hacking at the hot tar road with a spade, under the blazing sun.

So let us be wary of materiality as infrastructure and infrastructure as materiality. And let us be wary of the metaphors that come to mind: networks and grids. We are so deep in network thinking that it is difficult to consider an ontology without networks.

“Just because a space on a grid is shared intends nothing about the affective and material substance or even the fact of membership, “ slide

Lauren Berlant invites us to try.

‘Affective infrastructure’ pushes up against the notion that ‘infrastructures’ are physical and material relationships; I like to think of affective infrastructures as more like synaptic relationships, in which neurotransmitters convey messages across the gaps between neurons by osmosis, rather than how muscles are attached to bones by tendons.

Affect includes the cognitive, proprioceptive, behavioural and psychological; Facial expressions, respiration, tone of voice, posture, all transmit affect, which means “we are not self-contained in terms of our energies. … Because affect is unformed and unstructured (unlike feelings and emotions) it can be transmitted between bodies.” So a starting point for thinking about ‘affective infrastructure’ are things that are ephemeral, unstructured, things that are more in the air.

What does it mean to be connected to others not only by emotions that are shaped and shared by language, but desires that are visceral, that may be pre-linguistic and bodily, and affect that overlaps both these?

I am not saying that our shared emotions, means that we have an affective infrastructure.

quote: suddenly we dilute what we call structural by shifting the force of the normative infrastructures from the state and commodity capitalism into the ordinary that also includes the local plural intimacies and associations that make like sticky and interesting for it. but this multiplication of forms in movement, not a denial of colonial/racial/patriarchal/class inheritance ( p 408) endquote

the other signficant aspect of affective infrastructure is the idea of movement rather than something fixed and established.

Movement is what distinguishes infrastructures from institutions, although the relation between these concepts and materialities is often a matter of perspective. Institutions enclose and congeal power and interest and represent their legitimacy in the way they represent something reliable in the social, a predictability on which the social relies. Institutions norm reciprocity. What constitutes infrastructure in contrast are the patterns, habits, norms, and scenes of assemblage and use. Collective affect gets attached to it too, to the sense of its inventiveness and promise of dynamic reciprocity

Affective infrastructure trouble the tightness of sociality, of what we think of as infrastructure and shows us how things are broken. It is infrastructure of that which is destablising and shifting, and yet holds together.

It proposes that the commons concept is a powerful vehicle for troubling troubled times. For the very scenes in which the concept attains power mark the desire for living with some loss of assurance as to one’s or one’s community’s place in the world, at least while better forms of life are invented and tried out. The better power of the commons is to point to a way to view what’s broken in sociality, the difficulty of convening a world conjointly, although it is inconvenient and hard, and to offer incitements to imagining a livable provisional life.

The list, and the challenges in different approaches in feminism itself, and how to deal with generational divides, the impact of me too
our different thoughts on what aziz ansari did.
The disagreements about what Joe Biden did, smelling people’s hair
It would be easy if all violations were about rape or non consensual masturbating in front of a date or colleague, but it is not..

and there are those cases that we maybe do not agree with, where as women we look at another generation, or another women’s discomfort and feel frustrated or we disagree.

I think that affective infrastructure and what binds us as we move are not made by our movement, they are not pre-existing, they are not material, the connections are in our uncertainty. So when we resist that which is difficult or awkward I would like to think that is possibly when we are actually moving towards each other.

3.. The Life of Emotional Data
Affective computing

So I want to shift a little now and talk about affect in another context, and the ways in which affect is more important than ever and how we are being told what affect is. Because affect is already the next big thing in AI. And very soon, I gather that the kinds of affect we have just been talking about, these uncertainties and awkwardnesses and exhaustions will appear as glitches, bugs or be classified as not affect. So there will clearly be a structuring of affect to mean something very specific.

There are two aspects to affective computing. There are things like Consider the following examples. Woebot, a therapy bot in an app, helps track your mood, and talks you through the reality of living with depression. And Lovot, – “powered by love” – is a silly, adorable little robot that just wants to make you happy.

There is also Paro, a soft, cuddly, baby harp seal robot that does not move and makes soft, animal noises. It is effective in therapy with Autistic children, and older people in homes, including those with dementia. Paro does not make the first move, remembers how it was held and does not feel bad if it is rejected or ignored.

Then there is the other kind of affective computing, how our faces, voices and body language are mined to feed the growing industry of ‘Emotion AI’. Emotional artificial intelligence is “the process of giving machines the ability to recognise and react to human emotion.”

Companies like Affectiva, an early pioneer in the field, are developing datasets of human affect and emotion to train future bots and digital interfaces to “understand all things human” as their tag line goes.

At its foundation, affective computing is about breaking down human facial expressions into a map of emotional states. Here is what Affectiva, one of the most important companies developing this technology says about how it works:

Humans use a lot of non-verbal cues, such as facial expressions, gesture, body language and tone of voice, to communicate their emotions. Our vision is to develop Emotion AI that can detect emotion just the way humans do.. Our Emotion AI unobtrusively measures unfiltered and unbiased facial expressions of emotion, using any optical sensor or just a standard webcam. Our technology first identifies a human face in real time or in an image or video. Computer vision algorithms identify key landmarks on the face – for example, the corners of your eyebrows, the tip of your nose, the corners of your mouth. Deep learning algorithms then analyze pixels in those regions to classify facial expressions. Combinations of these facial expressions are then mapped to emotions.

Affectiva is using this technology in the automotive context to understand “drivers’ and passengers’ states and moods, in order to address critical safety concerns” like road rage and driver fatigue. So a future car might issue a calming and authoritative order to an angry driver to pull over; or may increase the air conditioning to make a sleepy driver more uncomfortable, and therefore alert. Our faces and voices betray a lot about us, like it we are lying or angry. In the UK, one in five people lie to insurers or attempt to defraud them, so affective computing is being used to identify possible lying and cheating through facial features and voice.

But affective computing is also for humans to have a better experience relating to humanoid robots, digital assistants and chat bots. These are perceived to be awkward to interact with because they lack affect. When Google launched Duplex, its voice-based digital assistant, they emphasized how natural-sounding it was:

The system also sounds more natural thanks to the incorporation of speech disfluencies (e.g. “hmm”s and “uh”s)….adding synthetic waits, which allows the system to signal in a natural way that it is still processing. (This is what people often do when they are gathering their thoughts.) In user studies, we found that conversations using these disfluencies sound more familiar and natural.

In this, the not-human-ness of the voice assistant is trained out by real-time supervision of the software by a human. The system then learns how to negotiate around that mistake the next time. Making a -bot sound more human in this way is to reduce ‘friction’, that is, to not alert the human on the other end that they are talking to a -bot. Or to suspend disbelief for longer that we are talking to a human

So we can very quickly see how these systems of capture will create a map – this is what happiness looks like, this is what surprise looks like and so on.

But here is the thing with affect, affect is also awkward, unique, personal, bodily, physical, may be easy to capture but is difficult to analyse and process. As humans we have problems recognising and understanding what people are saying, but with the capture of enough human emotional data, we can possibly establish some clarity about what things mean.

Part 4.

So I want to close by acknowledging two very different kinds of moves here. And we need to struggle with the work of theorising these together, or at least in light of each other.

There is what Berlant urges us to acknowledge, that sociality is difficult and broken and often unbearable, and socially necessary proximity is unbearable. We are constantly dealing with the pain and privilege of others and this is hard.

But we are also seeing simultaneously a growth in the industry of the capture of emotions and affect in an attempt to make human feeling legible, and to control people and manage our errors and deviance. And through acts of prediction as well. Capturing and analysing our emotional data to predict what we might do, or establish paths of expected behaviour based on patterns emerging from prior situations.

And I leave you with that conundrum, now.

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