News from the Collective

In this blog series I explore a burgeoning intimate surveillance culture in neighbourhoods across the world.  At the core of this research is a flourishing network of surveillance technologies produced by Silicon Valley and perfectly tailored to a vigilant and paranoid home-owner. This matters. Because being watched by the state is one thing, but being watched by your neighbours invites myriad more questions.

Where I live in the east of Amsterdam I’m surrounded by generous neighbours. We often exchange news in the building’s stairwell, borrow things from each other and generally care for our communal space. It’s a wonderful place to live and I am comforted by the feeling of togetherness and trust in our neighbourhood. Most of my immediate neighbours happen to also be in a WhatsApp group. When I first moved in I was completely unaware of the group. But, not long after I’d unpacked there was a letter in the post from the police, urging me to join. The letter stressed that by joining the group I could make our neighbourhood “safer and more peaceful”. So of course I did.

For the most part, the dynamics in our WhatsApp group simply mirror the dynamics I experience when I meet neighbours face-to-face. There’s a bit of information sharing, some friendly banter. Occasionally someone flags something suspicious. In one instance a neighbour with a Ring doorbell camera, which records footage of anyone who rings it, posted a video in the group. It showed a young man with well-coiffed hair wearing a black leather jacket. He was lingering outside the door waiting for the doorbell to be answered. It never was. The neighbour, clearly suspicious, urged others in the group to be on the lookout. Another neighbour zoomed in on the footage, screen-grabbed it and posted an enlarged and  pixelated image of the man’s face back into the WhatsApp group. I received the message on my phone mid-way through dinner. I was confused. What makes this person suspicious? And what exactly are we on the lookout for?

This anecdote goes to the heart of my research. For the past 7 years I have been exploring how technology shapes neighbourhoods and influences neighbours. I am particularly interested in what impact our domestic surveillance devices, like smart doorbell cameras and mobile messaging groups, have on social cohesion and our feeling of safety. Most urgently, this research zooms in on the role that technology plays in creating inclusive spaces. With my creative partner Klasien van de Zandschulp, we have researched neighbourhoods across the globe, from our hometown of Amsterdam, to York in England, Johannesburg, San Paulo, New York and the rural farmlands of the Netherlands, interviewing people about how they use domestic surveillance technology to watch their streets and homes. Amazingly, what we’ve found is that neighbourhood surveillance, enabled by technology, is not unique to wealthy cities with plenty of technology. Instead, it happens in all kinds of places across the world, albeit on different scales and with different implications. We see the rise of an intimate surveillance culture. What I mean by this is that neighbours are buying technology like Ring or Nest doorbell cameras and joining messaging platforms like WhatsApp, Nextdoor or Neighbourhoods (from Facebook) to police their space. Intimate surveillance is booming in streets, buildings, gated compounds and townships, enabled by affordable surveillance technology and social media platforms perfectly tailored to a vigilant consumer.

A great example of this is in the Netherlands where you will most likely spot a street sign with the words “Attention! WhatsApp Neighbourhood Watch”. The sign is often placed at the start of a street. It signals to newcomers that neighbours in that area are watching their suburb and circulating information about suspicious people in their local WhatsApp group. Amazingly, these signs, and the groups they represent, have been adopted and seemingly normalised as part of communal life in the Netherlands. The police encourage residents to join these groups, just like in my neighbourhood in Amsterdam. Ordinary folks become the eyes and ears of the police. It’s hard to decipher the true statistics here. One source claims there are approximately 9,000 registered WhatsApp groups dedicated to neighbourhood surveillance. This accounts for about 630,000 residents. Yet, there are thousands of other groups that aren’t registered but they do exactly the same surveillance work.

For a mere €75, you can order a WhatsApp neighbourhood watch sign for your street in the Netherlands. We had this one custom made for our live performance ‘Good Neighbours’ in Amsterdam in 2021. 

WhatsApp neighbourhood groups are however only the tip of the proverbial iceberg. They form a tiny part of a much larger ecosystem of surveillance technologies in neighbourhoods. Another popular device (the rising star of surveillance gadgets) is the Ring doorbell camera. Ring is the humble doorbell redesigned for a paranoid networked society, complete with its own Instagram account and 152,000 followers (as of 2021). In December 2020, when many people were locked down in their homes during the corona pandemic, global online sales of Ring cameras jumped by 180% compared to 2019. In December of 2020, Amazon.com sold nearly half a million Ring cameras to American consumers.

A recording from a Ring doorbell camera in the city of York (UK) where we did part of our research and presented a live performance. 

Besides its doorbell function, the Ring includes a built-in camera and motion sensor. If you press a Ring doorbell it films you. If the Ring owner isn’t home they receive a notification on their phone with footage of the visitor. If someone triggers the sensor by walking past, the Ring camera will record that person, regardless of whether they rang the bell or not. Ring owners can even speak through their phones (via the doorbell) to anyone standing outside their door. Sounds convenient right?

In the course of this blog series I will speak into this question, because there’s much more to Ring than convenience (or safety). I will examine WhatsApp, Ring and a few other platforms that are shaping the order of micro politics in neighbourhoods. Pointedly, these technologies raise urgent questions about surveillance, platforms and society. Most immediately, I want to draw attention to the dangerous practice in neighbourhoods when residents sue technology to mark others as strangers who don’t belong. Social and racial profiling are commonplace and becoming ever-more transparent through technology. As Marleen Stikker from Waag Society reminds us, “Technology is an expression of capital and power”. Said more plainly: it’s often about money. Look no further than Nexdoor, a Silicon Valley social network aimed at local neighbourhoods and infamous for facilitating instances of racial profiling,  recently valued at $2,1 billion (up 30% from its last valuation in 2017). This entire series is about keeping a critical public record of the relationship between neighbourhoods, neighbours and their technologies and to keep the focus on what is at stake.

Dr. Natalie Dixon is an INC research fellow and  founder & cultural insights director at affect lab, a women-led creative studio and research practice based in Amsterdam. Her work explores questions of gender, race and belonging through the lens of technology.

Thinking about the current state of platforms, I’m reminded of Mark Fisher’s articulation of capitalist realism, the idea that we don’t imagine or build alternatives to capitalism because we can no longer envision a world without it. Big tech, in a few short years, has managed to instill within the public a similar state of platform realism. So many are unable to imagine how global communication, media, search, etc. could ever function without the platforms. Despite a growing malaise, it’s become difficult for many to consider a world without big tech platforms as a viable conception of the future.

That means we undoubtedly need to focus some of our energy towards the building of such alternatives. Users of big tech will need to see, feel, and use new avant-garde technologies that center anti-capitalist pro-public values before they ever abandon big tech platforms en masse. But along the way, we also need to engage the world where they are. With 3 billion+ people already signed on, some of this work should happen inside big tech’s platforms themselves.

This isn’t as bad as it sounds, though, because platforms can be fruitful spaces for cultivating criticality. Whether covertly subversive or overtly confrontational, platform manipulations (such as those that can be enacted via browser extensions, online performance projects, etc.) can prompt users to reconsider the role of these systems currently considered as inevitable parts of the 21st century landscape. By exposing the hidden and hiding the visible, hacktivist art, tactical media, and other related practices can help users question the role of platforms in everyday life. Why are they built this way? Who benefits? Who is made most vulnerable? How could it be different?

Such manipulations can also transform big tech platforms into good spaces for modeling resistance to capitalism, as the platforms themselves embody capitalist values in their most distilled and potent forms. The platforms are encodings of tech bro entrepreneurial ideology, agential systems that enact and amplify their beliefs in the importance of scale, the imperative of growth, and the superiority of the quantitative. Examples include my own projects that alter existing platforms to hide their metrics, to confuse their algorithmic profiling, and to distill their central manipulation mechanisms down to their barest essentials in ways that make them visible and tangible.

Other examples include projects by artists such as Joana Moll or  Disnovation, as well as essential software/platform/code/media studies critiques by thinkers such as Wendy Chun, Safiya Noble, Matthew Fuller, Søren Pold, yourself, and others in our community. The more we can illuminate existing connections between wider societal ills and the ways that big tech models, reifies, and amplifies them, the easier any platform exodus will be.

Decentralization undoubtedly holds some promise for dreams of exodus from big tech platforms. But decentralization on its own is not a panacea. We only need look at speculative finance and libertarian crypto dreams to find clues about who feels most excited about decentralization and why (e.g., it’s not about rebuilding the public or strengthening democracy).

In other words, the problem with platforms isn’t, in all cases, centralization per se. Instead, the negatives we contend with on big tech’s platforms are rooted in their software’s alignment with and embedding of the ideologies of capitalism. Global-scale software-based platforms are reflective of the same profit-focused business values that drive big tech’s executives to build those companies in the first place: growth, scale, more at any cost. This foundation is doomed from the start, as it inevitably leads big tech to treat users as resources to be mined, manipulated, and transformed into profit. It makes expendable user-centric values around privacy and agency.

Instead we need a turn away from the private and a return to the public. Without a private profit motive, many of the problems with big tech platforms would fall away. I say this knowing full-well that making such systems public is by no means a solution by itself. We’ve seen unprecedented corruption of and new justified distrust in public institutions over the last many years. But big tech’s platforms are decidedly anti-public, and this positioning is part of what makes them so damaging to privacy, agency, and democracy.

I think we should also experiment with new platforms (public or non-profit private) that enact decidedly different values than what big tech promotes. For example, what would a platform look like if it actively worked to defuse compulsive use rather than to produce it? Or if it wants less from users rather than more? Or if it encourages conceptions of time that are slow rather than fast?

For me, these ideas point towards a shared set of values to consider as we work to dismantle the control of big tech. These include:

SLOW — We need media that actively and intentionally works against the platform capitalist idea that speed and efficiency is always desirable and productive.

LESS — We need alternatives that advance an anti-scale, anti-more agenda. Facebook’s answer to the negative effects of platform scale post-2016 was to foreground Groups to “give people the power to build community.” Four years later that platform-produced power propelled racism and authoritarianism to new heights, culminating (so far) in a violent insurrection at the US Capitol.

PUBLIC — Social media infrastructure for 3 billion+ users should never be driven by profit or controlled by single individuals. Ditto goods distribution (Amazon), information access (Google), etc.

DECOY — To help produce a culture of platform exodus we need new projects/works that get into the platforms and help users turn themselves away from them.

28 June, 2021, https://bengrosser.com/, Urbana, IL USA

Just a few days ago,  London Metro Police Officer Wayne Couzens pled guilty to the rape and murder of Sarah Everard by, a 33-year-old woman he abducted while she walked home from a friend’s house.  Since the news broke of her disappearance in March 2021, the UK has been going through a moment of national “soul-searching.” The national reckoning has included a range of discussions–about casual and spectacular misogynistic violence, about a victim-blaming criminal justice system that fails to address said violence–and responses, including a vigil in south London that was met with aggressive policing, that has itself entered into and furthered the UK’s soul-searching. There has also been a surge in the installation of personal safety apps on mobile phones; One Scream (OS), “voice activated personal safety,” is one of them.

Available for Android and iOS devices, OS claims to detect and be triggered by a woman’s (true) “panic scream,” and, after 20 seconds and unless the alarm is cancelled, it will send both a text message to the user’s chosen contacts and an automated call with the location to a nominated contact. The app is meant to help women in situations where dialing 999, (assumed to be the natural and preferred response to danger), is not viable for the user and, in the ideal embodiment, this nominated contact, “the helper,” is the police. OS did automatically contact police (and required a paid subscription) in 2016, but it did not work out well and by 2018, was declared a work in progress: “What we really want is for the app to dial 999 when it detects a panic scream, but first, we need to prove how accurate it is. That’s where you come in. . .” OS is currently in beta and free (while in beta). It is unclear whether the developers have given up with that utmost expression of OS. 

OS is based on the premise that men fight and women scream —“It is an innate response for females in danger to scream for help”—and its correct functioning requires its users to be ready to do so, even if such an innate and instinctive response doesn’t come naturally to them: “If you do not scream, the app will not be able to detect you.” However, there are two discriminations in terms of scream analysis, in how the app discriminates while listening for and to screams, and in failing to detect or respond to them. The first has to do with who can use the app (i.e., whose panicked screams are able to trigger it) in the first place. This is presented in terms of gender and age—for the moment, OS can listen to “girls aged 14+ and women under 60,” where cisgender, as in anything OS, is taken for granted.   It is, however, a matter of acoustic parameters set by the developers (notably, of reaching a certain high pitch and loudness threshold). Which is why the app was implemented to include a “screamometer” for potential users to scream, hard, figure out, and see whether they can reach “the intensity that is needed to set it off” (confetti means they do). The second one discriminates true panicked screams from other types of screams (e.g., happiness, untrue panic). As presented by the developers, both discriminations are problematic and misleading, and so is “the science behind screaming” One Scream‘s website boasts of. 

The app does not quite distinguish true from fake screams, nor joy from panic for that matter.  Instead, One Scream listens for “roughness,” which a team of scream researchers—it truly is a “tiny science lesson” —has identified as scream’s “privileged acoustic niche” for communicating alarm.  According to this 2015 study in Current Biology,  “roughness” is the distinctive quality of effective, compelling human screams (and of artificial alarms) in terms of their ability to trigger listeners and in terms of perceived urgency. Abrupt increases in loudness and pitch are not unique to screams. The rougher the scream, then, the greater its perceived “alarmess” and its alarming effect. That’s why developers say OS “hears real distress,” essentially “just as your own ear.” However, other studies suggest your own ears might not be so great at distinguishing happiness from fear and scream research, and particularly the specific “bit” OS builds on, by and large assumes, relies on, and furthers the irrelevance of “real” on the scream vocalizer end.  

In OS’s pledge to its users, the app’s fine-tuning to its scream niche—i.e., to rough temporal modulations between 30 and 150Hz—is as important, as is the developers (flawed) insistence on the irredeemably uniqueness of true panic’s scream vocalizations, which they posit are instinctive and can’t be plotted or counterfeit: “Experience has shown that it is difficult for women to fake their scream.” Yet, current scream analysis and research primarily and largely relies on screams delivered by human research subjects (often university students, ideally drama students) in response to prompts for the purposes of studying them as well as, especially, on screams extracted from commercial movies and sound effect libraries. The same applies to the other types of vocalizations (e.g., neutral and valenced speech, screamed sentences, laughter, etc.) produced or retrieved for the purposes of figuring out what it is that makes a scream a scream, and how to translate that into a set of quantifiable parameters to capitalize on that knowledge, regardless of the agenda. 

Because of their interest for audio surveillance applications, screams are currently a contested object and a hot commodity. Much as is the case with other scream distinction/detection enterprises, the initial training of OS most likely involved that vast and available bank of crafted scream renditions—by professional actors, machines, combinations of those, by and for an industry otherwise partial to female non-speech sounds—conveniently the exact type of “thick with body” female voicings OS is also invested in. For some readers, myself included, this might come across as creepy and, science-wise, flimsy. 

Scream research often relies on how human listeners recruited for the cause respond to audio samples. Apparently, whether the scream is “real,” acted, or post-produced is neither something study subjects necessarily distinguish nor a determining factor in how they rate and react. In terms of machines learning to scream-mine audio data, it is what it is: “natural corpora with extreme emotional manifestation and atypical sounds events for surveillance applications” are scarce, unreliable, and largely unavailable because of their private character. That is no longer the case for OS, which has been accruing, and machine-learning from, its beta-user screams as well as how users themselves monitor/rate their screams and the app’s sensibility. OS users’ screams might not be exactly ad lib, as users/vocalizers first practice with the “screamometer” to learn to scream for and as a means to interface with OS, but it’s as natural a corpora as it gets, and it’s free for the users of the screams. OS not only echoes “voice stress analysis” technologies invested in distinguishing true from fake or in ranking urgency, but, as part and parcel of a larger scream surveillance enterprise, also public surveillance technologies such as ShotSpotter, all of which Lawrence Abu Hamdan has brilliantly dissected in his essay on the recording of the police gunshots that killed Michael Brown in Ferguson, Missouri in 2014.

Chilla is a strikingly similar app developed and available in India—although there’s a nuanced difference in the developer’s rationale for Chilla, which in its pursuance of scream-activated personal safety also aims to compensate for the fact that many girls and women don’t call “parents or police” for help when harassed or in danger. As presented, Chilla responds both to assaults and to women’s ambivalence towards their guardians. The latter is, too, a manifestation of the breadth of gender-based violence as a socio-cultural problem, one that Chilla is trained to fail to listen to and one that, because of OS’s particular niche user market, is simply out of the purview of its UK counterpart.

That problem–and that failure–is neither exclusive to India nor to scream-activated personal safety apps. Calling 999 in the UK, 911 in the US, or 091 in Spain, where I am writing, doesn’t come naturally to many targets of sexual and gender-based violence because they don’t conceive police as a help or because, directly, they see it as a risk—to themselves and/or to others. As Angela Ritchie has copiously documented in Invisible No More: Police Violence Against Black Women and Women of Color, women of color and Black women in particular are at extremely high risk for rape and sexual abuse by police officers, as high as 1 in 5 women in New York City alone.

OS, then,  is framed as a pragmatic, partial answer to a problem it doesn’t solve: “We should never have to dress in a certain way…but we do.” The specifics of how OS would actually “save” or even has saved its users in particular scenarios go unexplained, because OS is meant to help with feeling safe; getting into the details, and the what ifs, compromises that service. This sense of safety has two components and is based on two promises: one, that OS will listen to your (panic) scream, and, two, as of now via the intermediacy of your contacts, the police will go save you. The second component and its assumed self-evidence speaks to the app’s whiteness and of its target market of white, securitized, cisgender female subjects. 

Over and above its acoustic profiling, the app is simply not designed with every woman in mind. OS’s branding is about a certain lifestyle—of going for early runs and dates with cis-men, of taking time for yourself because you’re super busy at your white-collar job and going for night runs, of taking inspiration from “world” women and skipping if running isn’t for you.  This lifestyle is also sold: sold as always under the threat of rape–despite its “rightfulness”–sold in a way that animates the feelings of insecurity and disempowerment that One Scream advertizes itself as capable of reversing.  Safety, then, is sold as retrievable with OS. 

Wearable or otherwise portable technologies to keep women “safe,” specifically from sexual assaults, are not new and are varied. These have been vigorously protested, particularly from feminist standpoints other than the white, securitized, capitalist brand OS professes—because, in (partly) delegating safety on technologies women then become personally responsible for, these technologies  further “blame” women.  For authorities and the patriarchy, this shift in blame is a relief. In discussing the racialized securitization of US university campuses, Kwame Holmes notes how despite “reactionary attacks” on campus feminism (e.g., so-called “snowflakes” complaining about bad sex) and authorities’ effective reluctance to acknowledge and challenge rape culture, anti-sexual assault technologies tend to be welcomed and accepted. As Holmes also notes, there’s no paradox in that. Those technologies flatten the discussion, deactivate more radical feminist critiques and potential strategies, and protect the status quo—not so much women and not those who, whenever an alarm sounds and especially when security forces respond, readily become insecure.  

It is not a stretch to think that OS could potentially amplify the insecurities of Black and brown people subject to white panic (screams) and to its violence, something other audio surveillance technologies are already contributing to, at least it’s not a greater stretch than to entertain situations in which police would show up and save an OS user before it’s too late. Even if it’s never triggered, as developers seem to assume will be the case for the majority of installed units—”Many people have never faced a situation where they have had to panic scream”—it’s trapped in a securitization logic that ultimately relies on masculine authority, one that calls for the expansion of CCTV cameras, wherein women are never quite secure (see Sarah Everard’s vigil). 

One Scream’s FAQs cover selected worries that users have or OS anticipates they might have. Among these, there are privacy concerns (i.e., does it listen to your conversations?) and the fear the alarm will activate “when it shouldn’t.” In the Apple Store user reviews, there’s a more popular type of concern: OS not responding to users’ screams. In other words, there’s simultaneously a worry about OS listening and detecting too much and about OS failing to listen “when it matters.” These anxieties around OS’s listening excesses and insufficiencies touch on (audio) surveillance paradoxical workings: does OS encroach on the everyday life of those within users’ cell phones’ earshot while not necessarily delivering on an otherwise modest promise of safety in highly specific scenarios? There’s a unified developer response to these concerns: OS “is trained to detect panic screams only.”

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Featured Image: By Flicker User Dirk Haun. Image appears to be a woman screaming on a street corner, but is actually an advertisement on the window of a T-Mobile cell phone shop (CC BY 2.0)

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María Edurne Zuazu works in music, sound, and media studies, and researches the intersections of material culture and sonic practices in relation to questions of cultural memory, social and environmental justice, and the production of knowledge (and of ignorance) in the West during the 20^th and 21^st centuries. María has presented on topics ranging from sound and multimedia art and obsolete musical instruments, to aircraft sound and popular music, and published articles on telenovela, weaponized uses of sound, music and historical memory, and music videos. She received her PhD in Music from The CUNY Graduate Center, and has been the recipient of Fulbright and Fundación La Caixa fellowships. She is a 2021-2022 Fellow at Cornell’s Society for the Humanities. 

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