Thinking loudly about networked beings. Commonist. Projektionsfläche. License: CC-BY
2177 stories
·
85 followers

Standard Evasions

1 Comment

Amid accelerating environmental breakdown, widespread socioeconomic instability, and emergent forms of fascism, if there’s one consistency, it’s the sense of a complete lack of consistency: They all could be linked to a supposed “trust deficit” that a range of researchers, journalists and organizations have argued is currently worsening, what sociologist Gil Eyal has described as a “crisis of expertise.” On this view, more and more of the public regards the authority of both formal experts and institutions with suspicion, refusing to “believe the science” or the supposedly good intentions of those entities. This suspicion is bound up with a deeper sense of instability and anxiety, as both a contributing cause and a mounting effect.

Feeding both the mistrust and the anxiety is the idea of accelerating technological change, with artificial intelligence and machine learning being particularly concerning. Many fear, for instance, that algorithms will eliminate jobs through automation and exacerbate existing forms of injustice. And these fears are well-founded: Specific uses of these technologies — from crime prediction to employment verification to controlling the issuing of medications — and their associated modes of thought (i.e. a purely quantifiable approach to human existence and relations) will at best destabilize and at worse further dehumanize people, just as previous cycles of industrial and societal automation have done. While AI developers and their boosters may insist that “this time, it’s different,” it’s not clear what, if anything, has changed: As Judy Wajcman has argued, those making such promises often have little to say about what is different about their approach, and even less when those promises are broken.

This agency’s work on trust will be the work on trust in the U.S. as far as these technologies — for crime prediction, employment verification, controlling the issuing of medications — are concerned

It would seem like good news, then, that there is a plan to try to soothe these anxieties so that people can feel as though they can work with AI technologies rather than against them: a plan to develop trust in AI. But if, in such planning, the idea of trust itself is misconceived, this becomes more bad news. Such is the case with this U.S. Executive Order — signed by Donald Trump and carried over to the Biden administration — that is meant to ensure “reliable, robust, and trustworthy systems that use AI technologies.” It designates the National Institute of Standards and Technology (NIST) — a branch of the Department of Commerce that establishes measurement standards — as the lead organization in this project, which is tasked with developing rubrics for what such “trustworthy systems” might look like and how they might be assayed.

Its work on trust will therefore be, to a certain degree, the work on trust in the U.S. as far as these technologies are concerned: It will directly inform public standards around AI and how AI systems are subsequently developed. The U.S. government’s status as a primary purchaser of new technologies, combined with NIST’s role in setting technical expectations for said government — around system security, biometric data sharing, and a host of other areas — means that NIST guidance, however non-binding it might be in theory, often serves as the rules of the game in practice. The “secure hash algorithms” developed by NIST are an illustrative example: These have been adopted so broadly that they serve as the linchpin of what “secure” internet browsing means today. Its standards for what make an AI system “trustworthy” could have a similar normalizing effect.

The first fruits of NIST’s “trust” project, a recently released paper called “Trust and Artificial Intelligence,” focuses on how the institution defines “trust in AI” and how that trust might be modeled. Its definition, however, makes plain its priorities. Rather than define trustworthy systems with respect to their consequences or differential impacts, NIST focuses strictly on how they are perceived by “users,” who are defined in a restrictive and reductive way. Moreover, in assessing whether AI systems are trusted by these users, it does not bother to ask whether the systems themselves are even worth trusting.


Any kind of infrastructure, including algorithmic systems, have impacts that are not evenly distributed. As algorithms are introduced for everything from facial recognition to hiring to recidivism prediction, they are invariably met with valid concerns about the effects they will have on already marginalized communities. The history of concern, complaint, and critique about these algorithmic systems is as long as the history of the systems themselves.

So it is disconcerting that the NIST report, in a section called “Trust Is a Human Trait,” does not acknowledge or cite from that history of concern but instead quotes approvingly from a evolutionary psychology paper that basically argues that we are destined by natural selection to be bigots.

This sets the stage for how NIST will go on to conceptualize “trust”: not as a complex, historically conditioned social phenomenon that takes many different forms in different and often conflicting contexts but as something very simple. So simple, in fact, that it can be resolved into an equation:

 

For this model to make sense, you need — among other things — for trust to be a matter of two fixed, contextless parties and their more or less isolated encounters with each other. Interactions with AI, that is, must live in a little two-party bubble, just as it does in this figure from the report:

After ruling out any situation that doesn’t involve a single person electing to use a single AI system, the equation then defines trust as calculable in that scenario in terms of an array of the AI system’s design attributes and an array of user attributes. To evaluate how much the user trusts the system, you take the system’s security, resiliency, objectivity, and so on — all quantifiable attributes — and match them with, well, such attributes as the user’s gender, age, and personality type, which are also conceived as fully quantifiable and capable of being plugged into the equation above. In other words, NIST’s first step in handling people’s unease with systems that reduce the world to what is quantifiable is to reduce people’s unease itself to something quantifiable.

Tellingly, the paper illustrates a “user” with two hypothetical examples, which could be summed as: middle-aged lady who doesn’t use computers;

and the hip young programmer bro.

Their attributes, after being converted to quantities, are then evaluated with respect to the attributes of a particular AI system to determine the likelihood that the user will “trust” that system — understood here as their willingness to continue using it. So while the second user above might be young enough and male enough to have a smooth and trusting relationship with AI, the first user might be seen as lacking the necessary attributes.

In this setup, mistrust is not conceived as a matter of shortcomings or inconsistencies in algorithms’ broader designs or their impacts themselves but as a matter of a user perceptions. To fix “trust issues,” the users’ perceptions would need to change; changes to the algorithm are necessary only to alter those perceptions

None of this is how AI works in practice. In NIST’s model, as the diagram helpfully captures, there are two parties to an algorithmic interaction. In reality, there are many, many more. Algorithms are embedded in broader infrastructures of data collection, processing, refining and deployment, and as Daniel Neyland has documented, each of these stages involves many actors (programmers, marketers, corporate executives, and, infrequently, regulators) with many different concerns that end up reflected in the resulting software. What an algorithmic system that, say, matches users to possible purchases “does,” after all, is not simply match users to “all possible purchases” but those use cases that the system’s marketers have chosen to emphasize (what products have the highest profit margin?), what that system’s programmers had the time and budget to implement (what products were fastest to categorize and import?), and what sorts of user experience its researchers saw as worth testing.

But more pressingly, even if you consider a particular algorithm only in the context of its deployment, there are still more parties involved: One must consider the algorithm’s developers and deployers and their interests in a particular interaction. Can the user trust them? For example, the purpose of Amazon’s Alexa, we are told on its app store page, is to help users “listen to music, create shopping lists, get news updates, and much more.” But it is also to passively collect a user’s data, shipping it back to Amazon, who can then use it to produce anything from personalized advertisements to a reconstruction of users’ identity to enable further data mining.

Defining “trust” as the degree to which a user believes the algorithm can do its job evades the question of whether the user wants it to

As Nanna Bonde Thylstrup insightfully observes, the “big data hype” is fundamentally based on “the waste-related epiphany that seemingly useless data can be extracted, recycled, and resold for large amounts of money.” Reuse and dual-use, in other words, are part and parcel of how even AI developers see their work. Thus algorithmic interactions are not, as NIST portrays, about a single user trusting a single algorithm for a single purpose, but instead they are multifarious exchanges for multifarious purposes among multiple parties, some of which are obscured from any individual user.

Examples like Alexa, however, are not fully representative, because they represent situations where — to some degree, at least — the user can be said to have opted into a service or an interaction with an algorithm. But in more and more cases, users have algorithmic systems imposed on them, as with, for instance, work-tracking software that allows employers to fire people for not keeping pace. (This is in line with the long history of workplace automation, which is often deployed specifically to eliminate jobs, as detailed in this paper.) In such situations, defining trust as the degree to which a user believes the algorithm can do its job completely evades the question of whether the user (let alone society at large) even wants it to. That a particular user “uses” an algorithm is hardly proof that they trust it or its logic or the other actors behind it. Rather, it may reflect the fact that they don’t have a choice. They may not be not trusting but trapped.

Users are often fully aware of the sometimes treacherous nature of algorithmic systems and the multiple motivations of the organizations behind them but feel they have no alternative but to use them. When Cami Rincón, Corinne Cath-Speth, and I interviewed trans people about their concerns with voice-activated AI like Alexa and Siri for this paper, they weren’t focused on usability (e.g. “can I trust Siri to provide an answer to a question”); they were focused on the agendas of Apple and Amazon. That is, they “trusted” that the algorithms could do what they were expected to do but not that what they were doing wasn’t ultimately very, very bad for the user, extracting their personal information and desires in order to generate ever more precise efforts to extract their money as well. In other words, these systems are untrustworthy because they work. It’s precisely because they meet the ideals of reliability and resiliency that NIST highlights as components of trust that certain groups of users find them harmful and unacceptable.

This highlights the harm that NIST’s definition of trust allows for and in some ways enables. If trust is modeled as strictly between the user and the algorithm, oriented around a single, discrete task, then user distrust of companies like Apple and Amazon can be dismissed as irrelevant, either because the users’ reliance on the system is taken as sign of trust or because qualms about the algorithm’s developers or their other potential purposes is outside the definition’s scope. The definition further implies that “trust” requires users themselves be categorizable and categorized in ways that fit simplistic and hegemonic ideas of humanity.

Developers can brag about how “trusted” their algorithms are by regulators — and trusted by the regulators’ own standards, to boot! — while the systems’ parallel impacts or indirect harms and those experiencing them are ignored.


This inadequate definition of trust isn’t just NIST’s problem or failure. The idea of algorithmic trust as focused on the user and algorithm alone is prevalent in a lot of the work in the fields of computer science and human-computer interaction. In a meta-analysis that looked at 65 studies on the topic of trust, the closest the authors came to identifying broader structural and organizational concerns as a factor was in their discussion of “reputation” — specifically, the algorithm’s reputation, not the wider agenda of developers.

In some respects, NIST’s limited view is not surprising: The researchers’ task for this report was to explicitly model trust, and since broader contexts are hard to model, they have simply been sidelined from the start. This is precisely the problem. It’s not just that NIST gets trust wrong; it’s that it can’t possibly get it right if trust is treated as merely a technical problem. Trust isn’t not technical, but it isn’t just technical, either. It is ultimately political. Giving the task of defining trust to a technical standards organization is ultimately a way of eliding those political ramifications. Trust is not just about how reliably an algorithm does its work but what that work is, who that work is for, and what responsibilities come with its development and deployment. NIST is not equipped to answer these questions. It is capable only of burying them.

Rather than models aimed at instrumentalizing trust, what we desperately need is to take a step back and confront algorithmic systems as a political phenomenon: as systems that demand public and democratic accountability. This is not an easy task: It raises questions not just about the design of algorithmic systems but about the structure of work, the vast ecosystem of relations between companies, and what futures we collectively want to build toward. But these concerns do not go away if we mask them with a technocratic view of algorithmic systems and their regulation. They simply lurk and rot, returning to haunt us in a more malevolent form. We may find ourselves with AI systems that have perfect scores on measures of algorithmic trust, even as the world in which they operate has become more distrustful and destabilized than ever.

Read the whole story
tante
21 days ago
reply
"Trust is not just about how reliably an algorithm does its work but what that work is, who that work is for, and what responsibilities come with its development and deployment." Os Keyes with a great essay on Trust in "AI"
Berlin/Germany
Share this story
Delete

Reconnected

1 Comment

The conventional wisdom about networks suggests that their politics can be reduced to how centralized they are: A centralized network is designed for control, while a decentralized or distributed network is democratic. Early champions of the internet assumed both that its structure made it decentralized and that its decentralization would protect it from monopolization. In 1999, Tim Berners-Lee, the inventor of the World Wide Web, wrote that the internet “is so huge that there’s no way any one company can dominate it.”

From the beginning, then, there was clearly a misunderstanding of what the internet was: Its biggest advocates believed it would be immune from corporate or state capture while empowering individuals with new tools for information sharing and production. But that view was always clouded by libertarian idealism. As Joanne McNeil writes in Lurking, “the internet I felt momentarily nostalgic for” — one where people came together in chat rooms and on forums to have discussions untainted by politics, social divides, or economic pressures — “is an internet that never actually existed.”

While it’s technically true that no one company dominates the internet today, the cloud services, undersea cables, and other infrastructures that power it are increasingly concentrated in a small group of telecommunications conglomerates and the owners of the web’s dominant platforms: Amazon, Google, Microsoft, and Facebook. But looking back at the internet of the 1990s, the power of private companies was already apparent. Even though the internet’s infrastructure wasn’t fully privatized until 1995, online interactions were already being shaped by commercial pressures. In 1994, Carmen Hermosillo published an influential essay on the nature of community online, arguing that “many cyber-communities are businesses that rely upon the commodification of human interaction.” Hermosillo explained that even though “some people write about cyberspace as though it were a ’60s utopia,” early networked services — America Online, Prodigy, CompuServe, and even the Whole Earth ‘Lectronic Link (the WELL) — were businesses that turned the actions of their users into products, shaped users’ interactions to serve corporate ends through censorship and editorial discretion, and maintained a permanent record that made cyberspace “an increasingly efficient tool of surveillance.” Counter to the boosterism of Wired, the electronic community, Hermosillo argued, benefited from a “trend towards dehumanization in our society: It wants to commodify human interaction, enjoy the spectacle regardless of the human cost.”

But looking back at the internet of the 1990s, the power of private companies was already apparent

Hermosillo was not the only voice pushing back against the libertarian framing of cyberspace. In 1996, technology historian Jennifer S. Light compared the talk of “cyberoptimists” about virtual communities to city planners’ earlier optimistic predictions about shopping malls. As the automobile colonized U.S. cities in the 1950s, planners promised that malls would be enclosed public spaces to replace Main Streets. But as Light pointed out, the transition to suburban malls brought new inequities of access and limited the space’s functions to those that served commercial interests. The same went for virtual communities where, under private ownership, “these agora function only in their commercial sense; the sense of the market space as site for civic life is subject to strict controls.” Commercial “communities” prioritized business interests over facilitating the participation of marginalized voices, promoting education and productive exchanges, and facilitating the democratic governance of digital spaces.

Light provided the example of Prodigy, which she described as an “electronic panopticon” that monitored public posts, censored those that were not family-friendly, and subjected users to “constant advertising” tailored to information collected about them. These same problems, of course, still characterize commercial communication platforms. If anything, the post dot-com-bubble approach to monetizing the web made it worse. “Web 2.0,” which Tim O’Reilly aptly described in 2005 as the effective platformization of the web, began a concerted effort to enclose all forms of online interaction so that everything users did could be captured and catalogued for corporate gain.

In less than 20 years, a small number of companies came to oversee many of our online interactions and dictate their shape to serve their interests, just as Light warned. As Twitter user @tveastman joked in 2018, “I’m old enough to remember when the Internet wasn’t a group of five websites, each consisting of screenshots of text from the other four.”


Yet in recent years, we have begun to see cracks in the model that allowed these companies to dominate: Digital advertising, which has been key to subsidizing the sites, services, and platforms that brought users on, is under threat. Part of this is because of increased efforts to impede the industry’s surveillance of users: Apple recently rolled out features to block ad targeting on iOS devices, which Facebook in particular saw as a huge threat. But digital advertising’s effectiveness in general is also being questioned. “Ad fraud,” or the misrepresentation of attention metrics (often through the use of bots), is believed to be rampant, and companies like Procter & Gamble and Uber cut their ad spend by $100 million each with little apparent impact on their revenue growth.

The vulnerability of the digital advertising model offers an opportunity to imagine a different kind of network, rooted in an alternative political agenda; one that elevates social benefit over corporate profit. But that won’t happen automatically. Without coordinated action for a better internet, the move away from digital advertising may simply lead to a further monetization of our networked interactions. The so-called creator economy is helping normalize a renewed emphasis on micropayments and subscription models, and the dominant social media platforms have followed suit with new monetization features, such as Twitter’s Super Follows and Facebook’s Bulletin. Beyond new apps and services, there’s a growing effort to graft an infrastructure of monetization onto the internet itself: Web3, a vision that Drew Austin describes as “a blockchain-based internet that works less like an open network circulating ‘free information’ and more like an expansive matrix of built-in ownership and payment infrastructure.”

On the creator side, these technologies offer what amounts to false promises to people who don’t already have a large audience. The creator economy is even more unequal for artists and performers because of how platforms drive a superstar economy that hollows out the “middle class” of professions. A small number of people with huge followings can leverage the new tools to generate more revenue, while a vast pool is left playing the virality lottery. Meanwhile, monetization features render the internet more unequal generally, linking access to an individual’s ability to pay and universalizing an invasive form of personal commodification where we are all incentivized to turn ourselves into products.

The vulnerability of the digital advertising model offers an opportunity to imagine a different kind of network that elevates social benefit over corporate profit. But that won’t happen automatically

But in some circles, there is hope that Web3 will renew the lost promise of decentralization of the early internet. Crypto enthusiasts and some activists in the digital rights space assert that cryptocurrencies, blockchains, smart contracts, and related technologies will evade the regulatory power of the state and allow people to avoid predatory intermediaries like major banks. Yet they downplay how centralized these technologies have already become and fail to explain how renewed libertarian appeals to decentralization will avoid succumbing to a similar corporate capture as the internet.

Web3 is a technological solution that does not contend with how power is distributed in the real world. It does not aim to produce a more equitable means of networking society; rather, it seeks to forestall the political struggles that pursuing that aim would actually require. Like other “decentralized” concepts, it is readily available for co-optation. Silicon Valley billionaires are already openly hailing cryptocurrency as a right-wing technology, while Amazon recently launched its own “distributed” network consisting of its own products. Bitcoin’s infrastructure is controlled by just a few major companies, and in the same way that Google financialized digital ad markets, Web3 seeks to extend the logic of financialization to even more of our digital interactions.

Appeals to decentralization too often fail to contend with the power structures that can take hold of supposedly liberatory projects. In an essay in Your Computer Is on Fire, Benjamin Peters argues that “networks do not resemble their designs so much as they take after the organizational collaborations and vices that tried to build them.” In other words, focusing solely on network design misses the political ideals and institutional practices that gave birth to them and that have been baked into them. For example, while anyone can still hook up their own server to the internet, the infrastructure is increasingly controlled by the same tech giants that have enclosed our activity on the network and whose devices we use to access it. Not to mention that purported decentralization did not stop mass surveillance by the National Security Agency or companies like Google and Facebook tracking virtually everything we do online.

Decentralization is not a politics in and of itself. Without a politics that explicitly seeks to serve the public while challenging corporate power, decentralization isn’t an actual strategy to decommodify our online interactions and reorient our networks toward alternative purposes.


The libertarian boosters of the early web held up the individual hacker as key to challenging state power and bringing about the liberatory potential of the internet, even as corporate giants took it over. Today, we hear about the individual creator, taking advantage of the opportunities offered by the consolidated internet, even as such narratives serve to keep us all creating content for platform companies to monetize for themselves. But individual actions will never generate emancipatory online spaces. That will require state action to fund and build the alternatives, pushed by an organized public demanding technology for the people.

There is an alternative to fetishizing decentralization. The history of state-driven communications projects in the 20th century offers examples of how to approach networks in different ways and with different politics.

When the history of the internet is usually told, it begins in 1969 with the first computers connected to ARPANET — an early packet-switching network funded by the U.S. Department of Defense that linked university research centers. But as Peters describes in How Not to Network a Nation: The Uneasy History of the Soviet Internet, the first proposal for a national civilian computer network originated in the Soviet Union in 1959, when Soviet cybernetics pioneer Anatoly Kitov proposed the Economic Automated Management System to help coordinate the planned economy. The network would have had a hierarchical design, but Kitov also built in the ability for workers to provide feedback and criticism, thus giving them greater influence over the planning process. The project was stifled by Soviet bureaucracy, but had Kitov’s vision been realized, worker participation would have made for a very different beginning to the civilian network era, orienting it around economic planning rather than knowledge transfer among academics.

Chile’s Project Cybersyn, developed under socialist president Salvador Allende, had a similar economic orientation, but took a different approach. As Eden Medina explains in Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile, it was an explicit attempt to break away from the “technological colonialism” of powerful countries like the U.S. “that forced Chileans to use technologies that suited the needs and resources of the wealthy nations while preventing alternative, local forms of knowledge and material life to flourish.” Its developers sought to imbue it with the politics of the left-wing government by seeking worker feedback, maintaining factory autonomy, and building a command center accessible to people without technical skills. The government hoped to use the network to coordinate production as it nationalized key industries, but we’ll never know how it would have operated in practice because Allende was overthrown in a CIA-backed coup in 1973. Medina asserts that the project demonstrated how rather than networks having an intrinsic politics, the political process itself can direct the design of networks, and innovation can occur outside the purview of market competition.

Another alternative was the Minitel system, developed in France after French President Valéry Giscard d’Estaing declared in 1974 that “for France, the American domination of telecommunications and computers is a threat to its independence.” As Julien Mailland and Kevin Driscoll detail in Minitel: Welcome to the Internet, the system, which was rolled out in the 1980s, had a “hybrid network design, part centralized and closed, part decentralized and open.” While the state telephone company controlled the center of the network, the edges — the servers on which private services were stored — were managed by private companies. It also had monetization built into its design. When a user connected to a service — users could access news, games, sports updates, topical message boards, and much more — they were charged for every minute of access, creating a revenue stream for the service provider and the telephone company, which took a cut. This business model incentivized companies to keep people on their services as long as possible without having to turn to advertising or tracking. In fact, Minitel had a certain privacy built in; when a user’s bill arrived, it would not identify which services had been used.

Crypto enthusiasts downplay how centralized these technologies have already become. Decentralization isn’t a strategy to decommodify our online interactions and reorient our networks

Mailland and Driscoll compare Minitel to Apple’s App Store, where a centralized authority manages the “platform” and maintains standards. But unlike on Minitel, where decisions “were subject to due process and could be appealed in a court of law, Apple exercises absolute control over the communication that takes place on its platform. The public has no interest, no representation, and no recourse to settle disputes.” In other words, because Minitel was run by the state telephone company, there was public accountability both through legislation that granted citizens those rights, as well as through the representative democratic system.

These earlier network projects — especially Project Cybersyn and Minitel — were explicit efforts to block technological imperialism. The globalization of the internet in the years since has hampered such aspirations. Most governments can only speculate about networks with a different politics because the global networks their citizens already use appear to be beyond the regulation of any nation outside the United States.

The most notable exception is China, where the “Great Firewall” is at work. This set of technologies and legislation is often framed solely as an attempt to limit the Chinese populace’s access to sensitive information or communication about controversial topics — which is undoubtedly true and a problem — but its economic implications are arguably more important. China’s protectionist measures, paired with generous state funding, have allowed it to develop a domestic tech industry that has grown to rival that of the U.S. — something other countries are seeking to emulate.

Researcher Juan Ortiz Freuler argues that if countries in the Global South continue to have the value they create extracted by U.S. companies, they will become more open to the Chinese model. This fuels concern about the internet breaking up along national borders — what’s called the “splinternet.” But Ortiz Freuler argues that the trend toward fragmentation is not only incipient but has already occurred — just along platform and not necessarily national lines. In enclosing online activity, companies like Google and Facebook limit the open transfer of data and interoperability of services to cement their dominance. The response to a “splinternet” is not to assure the internet’s supremacy but to explore alternatives — and the alternative politics that would underpin them.


Technology for the public good will most likely not emerge from either side of a technological cold war between the U.S. and China. With a nod to the 120 countries that rejected the choice between U.S. capitalism or Soviet communism during the 20th century’s Cold War, Ortiz Freuler calls for a digital non-aligned movement to build information systems “geared toward solving the big challenges we face as humans on this planet,” including the climate crisis and global inequality. Our social, environmental, and technological salvation, he argues, is not to be found in the Global North, whose cultures are “so intertwined with the rationalities that bred capitalism itself.” The Global South, however, has the imagination to create different systems, as it has “seen its own cultures shattered by colonialism, only to see the pieces of it repurposed into a new narrative, favorable to its new rulers.” That is not to place the responsibility for solving the problems created by the Global North on those it’s oppressed, but they will likely have a unique approach to ending the dominance of U.S. technology — and rejecting its replacement by Chinese tech. We might expand Ortiz Freuler’s call to include oppressed peoples in the Global North as well — those who are subject to the surveillance and control of technical systems that are constantly expanding to cover more areas of life.

As the Chinese example shows, allowing alternatives to thrive will likely require existing platforms to be neutered, whether by blocking them or pursuing policies to tear down their “walled gardens.” We should also recognize the importance of state control of infrastructure and how that allows public entities to shape network outcomes. There are many forms this experimentation could take, but one example could be found in Dan Hind’s proposal for a British Digital Cooperative, consisting of a communication platform free from advertising pressures and designed to promote socially beneficial forms of interaction, and community technology centers that educate locals and develop technologies to address their needs. But could we also imagine Chile, as it undertakes to rewrite its dictatorship-era constitution, rekindling the anti-imperial technological spirit of Project Cybersyn? Or Cuba learning from its biopharma success to reimagine how digital networks should serve a socialist society instead of succumbing to the hegemony of US tech giants? Or even Brazil, once again under the leadership of Lula da Silva, leading a coalition to build technologies that serve the Global South?

After commenting on how we’ve idealized the early web, McNeil writes that “when I think I feel nostalgic for the internet before social media consolidation, what I am actually experiencing is a longing for an internet that is better, for internet communities that haven’t come into being yet.” Clearly, this is not just about decentralization; it’s about thinking through the outcomes we want to see and building institutions — and only later technologies — in service of those political goals. Instead of hoping a particular network design will be immune from corporate control, we can build a better internet by first building the political power necessary to make it a reality.

Read the whole story
tante
51 days ago
reply
"Without a politics that explicitly seeks to serve the public while challenging corporate power, decentralization isn’t an actual strategy to decommodify our online interactions and reorient our networks toward alternative purposes."
Berlin/Germany
Share this story
Delete

Blockchain-Strategie der Bundesregierung liefert bislang wenig Ergebnisse

1 Comment

2019 präsentierte die Bundesregierung die nationale Blockchainstrategie. Bislang haben nur wenige der Vorhaben daraus auch Resultate hervorgebracht.

Read the whole story
tante
91 days ago
reply
Die Tatsache, dass die "nationale Blockchainstrategie" bisher keine signifikanten Ergebnisse hat, ist eine gute Nachricht.
Berlin/Germany
Share this story
Delete

Name of the Game

1 Comment

In March, the UK Parliament announced the launch of a public inquiry into “influencer culture,” calling for experts who could inform confused MPs and civil servants what it is, exactly, that influencers do. As a media studies academic who has studied influencers in the UK for the past five years, I met with other academics in law, business, and computer science to help assemble a response. The first question presented to us was “How would you define influencers?” Each discipline’s answer reflected a different approach: Law scholars started with baseline contractual questions; business academics wanted to refer to the quality and effectiveness of their influence; computer scientists looked to influencers’ relationships with algorithms.

These are all worthwhile lines of inquiry, but for me, it’s pointless to identify influencer as a fixed occupational category. The use of this term and other related terms like content creator, is worth examining because these terms do certain things that are inseparable from the context of their use: They evoke certain feelings and associations, and they convey political motivations. Rather than give influencer or creator a static definition, we should look at who uses it and consider what they are trying to do. My approach here is informed by Sara Ahmed’s call in The Promise of Happiness to “follow language around,” seeing what terminology “can and does do.” Why do certain terms appeal? Why do they “stick to certain bodies” and not to others?

In terms of tasks or specifications, “influencer” and “creator” aren’t really different jobs: They both involve the independent, serial production of content for social media platforms. Both influencers and creators are renumerated in similar ways, through a mix of platform revenue-sharing schemes, sponsorships, and fan-funded models like Patreon. Why, then, are they made to sound like different things?


Concerns about who has “influence” and what they do with it are longstanding. The word influencer has held a dark and sorcerous connotation since at least the 17th century, when, as Laurence Scott points out, Shakespeare used it in All’s Well That End’s Well. In contemporary media, before its association with Instagrammers and TikTokers, the term had been used to describe news anchors (as in this 1984 New York Times piece about Dan Rather “helping direct the national agenda”) and ultra-rich noncelebrities, as in this 2005 Sunday Styles article called “Influencers Are Forever.” Even into the social media era, influencer could be used to describe business leaders, as in this 2014 celebration of Spotify CEO Daniel Ek in Wired.

Influencers are seen as trading in the calculated depiction of an “authentic lifestyle,” while “creators” are held to a different standard of realness in representations

More recently, the term influencer has often stood in contrast with creator to differentiate among those who independently produce content professionally for new media platforms. Within digital culture, the term influencer typically connotes someone specializing in advertorial, with an ability to persuade audiences to buy things. Creator, by contrast, evokes someone making art, motivated by their vocation rather than the likelihood that their content will attract sponsorship.

This somewhat porous distinction has also taken on a gendered cast: A 2019 Wired piece by Emma Grey Ellis argued that women are more likely to be called influencers and men are more likely to be called creators, noting that men working in lifestyle and fashion verticals (like James Charles) are often referred to as “male beauty influencers,” as though “female” was automatically implied by “influencer.” In his history of the consumer and consumption, Don Slater points out that historically, women have been seen as more commercially aligned — less rational and taken less seriously as proper economic actors. This may be where the gendered distinction between influencers and creators first comes from.

These gendered labels, Ellis argues, shape the distribution of power in these media ecologies. Influencers are often derided in popular culture and dismissed as frivolous because they use self-representations in part to sell products; detractors thus accuse them of being vain and narcissistic. They sit below creators within the value hierarchy of online culture, yet creators too feature themselves in their work and make money through advertorial content. Influencers are regarded as fundamentally commercialized, with any creativity and agency drained from their practice, while creators appear as the inverse, only incidentally commercialized because of the appeal of their creative agency. Influencers are seen as trading in the calculated depiction of an “authentic lifestyle,” while “creators” are held to a different standard of realness in representations, affording them flexibility and more opportunities.

In an article for the Atlantic, Taylor Lorenz, a reporter who covers influencers, rejected Ellis’s gendered typology — “that men are more likely to self-identify as creators, while women more often call themselves influencers” — as an oversimplification. For Lorenz, the influencer/creator divide is not political but a matter of corporate branding. As YouTube grew its partner program in the early 2010s, it poured its marketing budget into branding its top content producers — both women and men — as “creators.” The then ambiguous and flexible term helped the platform sell a new kind of star — ostensibly ordinary teens who could attract millions of viewers with amateur content made from suburban bedrooms — to (nervous) legacy industry stakeholders in Hollywood and ad land.

“Creators” distinguish themselves from their emerging competitors (“influencers”) even as they are tied to platforms and separated from celebrities in their field

Following the success of this venture, the term creator was co-opted by other platforms like Tumblr, to describe the burgeoning group of bloggers who were gathering scores of followers on the sites. In Lorenz’s view influencer stands against creator as a “platform agnostic” term that is applied to newer, up-and-coming content producers with less experience, less early-adopter cache, and thus less legitimacy. That is, it helps “creators” distinguish themselves from their emerging competitors even as it ties them to platforms and separates them from celebrities in film, music, and television.

When discussing the terms influencer and creator, it’s important to differentiate between how content producers define themselves and how they are defined by other people. Lorenz notes that describing oneself as an influencer is kind of cringe; she claims that those who do this tend to be younger and inexperienced, less likely to be earning a living by making content. Indeed, when I scoured the profiles and websites of scores of content producers in an unscientific effort to test this theory, I couldn’t find any UK-based producers who publicly self-identified as influencers. (A friend later pointed me to ex-Love Island contestant Zara McDermott, a lonely “influencer” island in a sea of Instagram’s “beauty and fashion content creators.”)

Social media platforms also avoid the term influencer and are heavily invested in the creator label — not only YouTube (which has a Creator Support Team, as well as a Creator Monthly newsletter that spotlights Creators on the Rise) but also Instagram (which features a Creators tab on its support page and has promised to launch “creator shops”) and TikTok (which runs a “creator marketplace” where brands can “shop” the platform’s “most popular creators”). Even Twitter wants a piece of the “creator” action, with a “Twitter pro tips for creators” page. On none of these platforms is there any official mention of “influencers.” It’s as if they don’t exist.

But influencers are undoubtedly a culturally important part of these platforms’ business models. The sorts of content producers who some would deride as “influencers” are also among those who perpetually produce and post high-quality content that impels their audiences to scroll. Influencers attract and keep audiences. Why are the platforms afraid to speak their name? To return to Ahmed’s call to ask what words do, what work does their preference for creator perform?


In many ways, the distinction between influencer and creator is the product of longstanding critical divisions between art (seen as organically created) and mass culture (seen as manufactured and dangerous). Influencer suggests a mode of distracting and sedating the public, creating generations of docile consumers. Creator reaches into a different tradition.

Creator, of course, dovetails with creativity, a word softly humming with warm, positive connotations. Urban studies theorist Richard Florida, famously identified “creativity” as the “key factor in our economy and society” that “distinguishes us as humans from other species.” Perhaps most important, Florida argues that creativity is a “highly prized commodity in our economy, yet it is not a ‘commodity.’” For Florida, creativity is a valuable inborn quality within humans that can be grown and harvested by policymakers. If done right, this process can lead to explosive economic growth.

None of these major platforms officially make any mention of “influencers.” It’s as if they don’t exist

The elasticity of  “creativity” as a term — it offers a way to discuss “human capital” building in more conventional and organic sounding ways — has made it appealing for policymakers, particularly in the UK, where the 1997 Labour Government renamed the cultural industries (art, performance, theatre) as “creative industries”, a move that opened up this area of funding and policymaking to also encompass software companies, graphic design, and advertising agencies. Creative industries, moreso than fine arts and the humanities in their conventional conceptions, can be measured by their economic value. As a term, “creative” connotes this system of value: of cultural worth being a matter of how much economic activity it can generate. 

The governmental logic of “creative industries” dovetails with a neoliberal fixation on the figure of the entrepreneur: independent, self-reliant and economically generative. Under this movement toward the “creative,” art school merged with business school, as sociologist Angela McRobbie points out. Marketing and entrepreneurship are now standard parts of an arts education, with artists are encouraged to develop their personal brand. Social media platforms, upon their advent, were perfectly placed to attract these “creators”: people who had been encouraged to produce stuff independently, while also marketing themselves as brands. The platforms, in turn, can highlight the autonomy and initiative of their “creators” as testimony for how they nurture and incentivize entrepreneurship, demonstrating that creators can attract visibility and become successful without state support or social security. Where previous culture industries were marked by nepotism and exclusion, “creativity” and “creator” suggest an inner resource, an organic quality that any individual can claim provided they take responsibility for tapping on their own. Anyone can be (if not must be) creative.

Creator is useful in that it implies a binary opposition with consumption. The relationship between social media platforms and advertisers — and all the involuntary surveillance and efforts to manipulate users that it has entailed — has been the source of constant critique and scandal, summed up in the popular feeling that if the services are free, then you are the product. The term influencer only reinforces that perspective. But creator allows social media platforms to celebrate their role in supporting a burgeoning wave of talented young people, amplifying and supporting their technological and artistic potential.

Monopolistic social media hope to obfuscate their exploitative reputations through celebrating the productive creativity of those whom they “platform”

If both social media platforms and digital producers strictly avoid the word influencer in their public-facing communication, then who actually uses it? Talent agents, for one. M&C Saatchi’s talent management arm manage “social influencers”; another big agency in the UK is simply called Influencer Ltd. It has latched onto this term in their brand-facing literature precisely because it summarizes their talent’s ability to “activate” audiences. Influencers are valuable for their ability to shift products; as an influencer marketing agency AsireIQ put it, “while influencers’ content might not be the most beautifully crafted, they make up for it with their ability to capture their audience’s attention and ultimately influence purchase decisions.”

Not all agencies embrace “influencer,” though: Gleam Futures, one of the biggest agencies managing online content producers in the UK and the United States, have historically distanced themselves from the term in favor of their own coinage, “digital-first talent.” In 2019, Gleam executive Lucy Loveridge told the Guardian that the label influencer was “misrepresentative and degrading,” because, as she noted in another interview, the term “implies that content creators are just one homogenized group of people and of course, there is so much variety within the industry.” Yet despite their fraught relationship with the word, Gleam’s website clearly describes the firm as an “influencer marketing agency,” a concession, perhaps, to the necessity of shared language in professional markets and the desirability of “influence” from the marketing perspective.


Journalists, particularly in fashion, beauty and tech fields, have also promulgated the use of influencer in popular culture. It may appeal to many of them because it distinguishes their occupation — with its implied framework of professional training and ethics and editorial support — from the work of content producers, who operate in similar media. With the dismantling of legacy media outlets and print media, journalists are increasingly found on blogging platforms like Kinja or Medium, not to mention Substack. Media companies like Condé Nast have moved longstanding properties like Architectural Digest and Bon Appetit onto YouTube, where they mingle with homespun prank videos and makeup tutorials.

As a result, the lines between content creators and journalists are increasingly blurred for audiences. Someone whose job involves doing cultural criticism or news reporting could be described using terms like blogger, vlogger, or content creator. They would not, however, be described with the job title of “influencer.” Journalists are expected to explicitly reject being influenced and maintain an attitude of “objectivity” in order to prioritize “accuracy and fairness.” Influencers, by contrast, are happy to accept compensation for promoting things; it’s their primary source of income.

Drawing on these distinctions, journalistic accounts of influencers often focus on how content producers violate journalistic ethics with advertorial content. In a 2019 “guide to influencers,” Wired states that “what once seemed corrupting is now the norm, and given the dismal state of truth online, it’s unlikely the lines will ever get unblurred.” In a 2016 write-up of Fashion Week, editors for Vogue Italia wrote: “Note to bloggers who change head-to-toe, paid-to-wear outfits every hour: Please stop. Find another business. You are heralding the death of style.”

Ultimately, different people invested in these industries choose to use terms like influencer or creator because they are trying to say something about the work that they do or the work that they hope to do. Monopolistic social media hope to obfuscate their exploitative reputations through celebrating the productive creativity of those whom they “platform.” Producers avoid terms like influencer in audience-facing content because they want to be considered authentic and unsponsored; they are advertising a genuine potential for connection with their audiences. Talent agencies and marketers take up and manage the commercial relationships that influencers want to avoid publicly managing; they are happy to promote sell influence to brands, for the right price. At different times content producers want to either escape from or embrace commercialism, exploitation, and power. Influencer and creator are two sides of the same coin. Which term appears depends on which face they want to show us.

Read the whole story
tante
92 days ago
reply
This essay on the terms "(content) creator" and "influencer" manages to separate both by not essentialist definitions but their function and use. Great read.
Berlin/Germany
Share this story
Delete

Verkehrsforscher über Sprache: „Die Straße war mal für Kinder“

1 Comment
Berichte über Verkehrsunfälle halten die Schuld häufig von Autofahrern fern. Laut Dirk Schneidemesser prägt das unser Bewusstsein. mehr...
Read the whole story
tante
134 days ago
reply
Über die Art wie Sprache Verkehrspolitik formt.
Berlin/Germany
Share this story
Delete

Mietendeckel gekippt: Sie stehen auf der anderen Seite

1 Comment
Nach dem Scheitern des Mietendeckels ist offensichtlich: Die Wohnungsfrage wird Wahlkampfthema. Es muss jedem klar sein, wofür FDP und Union stehen. mehr...
Read the whole story
tante
159 days ago
reply
"Dass überhaupt das Einfrieren von Mieten über einen begrenzten Zeitraum und das Festlegen einer Mietobergrenze als radikaler Schritt gelabelt wird, zeigt nur, wie asozial die Wohnraumfrage von manchen beantwortet wird."
Berlin/Germany
Share this story
Delete
Next Page of Stories