Group items tagged

News about politics is viewed as particularly suspect and platforms are seen by many as contentious places for political conversation – at least for those most interested in politics. Rates of trust in news in general are comparatively higher than trust in news when it pertains to coverage of political affairs.

Negative perceptions about journalism are widespread and social media is one of the most often-cited places people say they see or hear criticism of news and journalism

Despite positive feelings towards most platforms, large majorities in all four countries agree that false and misleading information, harassment, and platforms using data irresponsibly are ‘big problems’ in their country for many platforms

Because the term “AI” is ambiguous, it makes having clear discussions more difficult. In one sense, it is the name of a subfield of computer science. In another, it can refer to the computing techniques developed in that subfield, most of which are now focused on pattern matching based on large data sets and the generation of new media based on those patterns. Finally, in marketing copy and start-up pitch decks, the term “AI” serves as magic fairy dust that will supercharge your business.

output can seem so plausible that without a clear indication of its synthetic origins, it becomes a noxious and insidious pollutant of our information ecosystem

Not only do we risk mistaking synthetic text for reliable information, but also that noninformation reflects and amplifies the biases encoded in its training data—in this case, every kind of bigotry exhibited on the Internet. Moreover the synthetic text sounds authoritative despite its lack of citations back to real sources. The longer this synthetic text spill continues, the worse off we are, because it gets harder to find trustworthy sources and harder to trust them when we do.

the people selling this technology propose that text synthesis machines could fix various holes in our social fabric: the lack of teachers in K–12 education, the inaccessibility of health care for low-income people and the dearth of legal aid for people who cannot afford lawyers, just to name a few

the systems rely on enormous amounts of training data that are stolen without compensation from the artists and authors who created it in the first place

the task of labeling data to create “guardrails” that are intended to prevent an AI system’s most toxic output from seeping out is repetitive and often traumatic labor carried out by gig workers and contractors, people locked in a global race to the bottom for pay and working conditions.

employers are looking to cut costs by leveraging automation, laying off people from previously stable jobs and then hiring them back as lower-paid workers to correct the output of the automated systems. This can be seen most clearly in the current actors’ and writers’ strikes in Hollywood, where grotesquely overpaid moguls scheme to buy eternal rights to use AI replacements of actors for the price of a day’s work and, on a gig basis, hire writers piecemeal to revise the incoherent scripts churned out by AI.

too many AI publications come from corporate labs or from academic groups that receive disproportionate industry funding. Much is junk science—it is nonreproducible, hides behind trade secrecy, is full of hype and uses evaluation methods that lack construct validity

We urge policymakers to instead draw on solid scholarship that investigates the harms and risks of AI—and the harms caused by delegating authority to automated systems, which include the unregulated accumulation of data and computing power, climate costs of model training and inference, damage to the welfare state and the disempowerment of the poor, as well as the intensification of policing against Black and Indigenous families. Solid research in this domain—including social science and theory building—and solid policy based on that research will keep the focus on the people hurt by this technology.

In a fifth of a second, [x+1] says it can access and analyze thousands of pieces of information about a single user

When he saw the 3,748 lines of code that passed in an instant between his computer and Capital One's website, Mr. Burney said: "There's a shocking amount of information there."

[x+1]'s assessment of Mr. Burney's location and Nielsen demographic segment are specific enough that it comes extremely close to identifying him as an individual—that is, "de- anonymizing" him—according to Peter Eckersley, staff scientist at the Electronic Frontier Foundation, a privacy-advocacy group.

Media companies using this technology claim it is now possible for the “mood” of the reader or viewer to be tracked in real time and the content of the advertising to be changed accordingly

ads targeted at readers based on their predicted moods rather than their previous behaviour improved the click-through rate by 40%.

Given that the average click through rate (the number of times anyone actually clicks on an ad) is about 0.4%, this number (in gross terms) is probably less impressive than it sounds.

Cambridge Analytica, the company that misused Facebook data and, according to its own claims, helped Donald Trump win the 2016 election, used psychographic segmentation.

For many years “contextual” ads served by not very intelligent algorithms were the bane of digital editors’ lives. Improvements in machine learning should help eradicate the horrible business of showing insurance advertising to readers in the middle of an article about a devastating fire.

The words “brand safety” are increasingly used by publishers when demonstrating products such as Project Feels. It is a way publishers can compete on micro-targeting with platforms such as Facebook and YouTube by pointing out that their targeting will not land you next to a conspiracy theory video about the dangers of chemtrails.

the exploitation of psychographics is not limited to the responsible and transparent scientists at the NYT. While publishers were showing these shiny new tools to advertisers, Amazon was advertising for a managing editor for its surveillance doorbell, Ring, which contacts your device when someone is at your door. An editor for a doorbell, how is that going to work? In all kinds of perplexing ways according to the ad. It’s “an exciting new opportunity within Ring to manage a team of news editors who deliver breaking crime news alerts to our neighbours. This position is best suited for a candidate with experience and passion for journalism, crime reporting, and people management.” So if instead of thinking about crime articles inspiring fear and advertising doorbells in the middle of them, what if you took the fear that the surveillance-device-cum-doorbell inspires and layered a crime reporting newsroom on top of it to make sure the fear is properly engaging?

The media has arguably already played an outsized role in making sure that people are irrationally scared, and now that practice is being strapped to the considerably more powerful engine of an Amazon product.

This will not be the last surveillance-based newsroom we see. Almost any product that produces large data feeds can also produce its own “news”. Imagine the Fitbit newsroom or the managing editor for traffic reports from dashboard cams – anything that has a live data feed emanating from it, in the age of the Internet of Things, can produce news.

the justice system continues to be slow to understand the link between online harassment and real-life violence

In order to increase public safety this decade, it is imperative that police — and everyone else — become more familiar with the kinds of communities that engender toxic, militant systems of harassment, and the online and offline spaces where these communities exist. Increasingly, that means understanding social media’s dark corners, and the types of extremism they can foster.

Businesses have to learn when online outrage is manufactured

There’s a difference between organic outrage that arises because an employee actually does something outrageous, and invented outrage that’s an excuse to harass someone whom a group has already decided to target for unrelated reasons — for instance, because an employee is a feminist. A responsible business would ideally figure out which type of outrage is occurring before it punished a client or employee who was just doing their job.

Social media platforms didn’t learn how to shut down disingenuous conversations over ethics and free speech before they started to tear their cultures apart

Dedication to free speech over the appearance of bias is especially important within tech culture, where a commitment to protecting free speech is both a banner and an excuse for large corporations to justify their approach to content moderation — or lack thereof.

Reddit’s free-speech-friendly moderation stance resulted in the platform tacitly supporting pro-Gamergate subforums like r/KotakuInAction, which became a major contributor to Reddit’s growing alt-right community. Twitter rolled out a litany of moderation tools in the wake of Gamergate, intended to allow harassment targets to perpetually block, mute, and police their own harassers — without actually effectively making the site unwelcome for the harassers themselves. And YouTube and Facebook, with their algorithmic amplification of hateful and extreme content, made no effort to recognize the violence and misogyny behind pro-Gamergate content, or police them accordingly.

All of these platforms are wrestling with problems that seem to have grown beyond their control; it’s arguable that if they had reacted more swiftly to slow the growth of the internet’s most toxic and misogynistic communities back when those communities, particularly Gamergate, were still nascent, they could have prevented headaches in the long run — and set an early standard for how to deal with ever-broadening issues of extremist content online.

Gamergate simultaneously masqueraded as legitimate concern about ethics that demanded audiences take it seriously, and as total trolling that demanded audiences dismiss it entirely. Both these claims served to obfuscate its real aim — misogyny, and, increasingly, racist white supremacy

Somehow, the idea that all of that sexism and anti-feminist anger could be recruited, harnessed, and channeled into a broader white supremacist movement failed to generate any real alarm, even well into 2016

many of the perpetrators of real-world violence are radicalized online first

It remains difficult for many to accept the throughline from online abuse to real-world violence against women, much less the fact that violence against women, online and off, is a predictor of other kinds of real-world violence

Politicians and the media must take online “ironic” racism and misogyny seriously

Gamergate masked its misogyny in a coating of shrill yelling that had most journalists in 2014 writing off the whole incident as “satirical” and immature “trolling,” and very few correctly predicting that Gamergate’s trolling was the future of politics

Gamergate was all about disguising a sincere wish for violence and upheaval by dressing it up in hyperbole and irony in order to confuse outsiders and make it all seem less serious.

Violence against women is a predictor of other kinds of violence. We need to acknowledge it.

The public’s failure to understand and accept that the alt-right’s misogyny, racism, and violent rhetoric is serious goes hand in hand with its failure to understand and accept that such rhetoric is identical to that of President Trump

deploying offensive behavior behind a guise of mock outrage, irony, trolling, and outright misrepresentation, in order to mask the sincere extremism behind the message.

many members of the media, politicians, and members of the public still struggle to accept that Trump’s rhetoric is having violent consequences, despite all evidence to the contrary.

The movement’s insistence that it was about one thing (ethics in journalism) when it was about something else (harassing women) provided a case study for how extremists would proceed to drive ideological fissures through the foundations of democracy: by building a toxic campaign of hate beneath a veneer of denial.

teaching the software to associate certain kinds of images, text and hashtags with a ‘dangerous’ protest results in politically and racially-biased definitions of what dangerous protests look like. This is because, to make these predictions, the system has to decide whether the event resembles other previous events that were labelled ‘dangerous’ – for example, past BLM protests. 

When in 2016 the ACLU proved that Dataminr’s interventions were contributing to racist policing, the company was subsequently banned from granting fusion centres in the US direct access to Twitter’s API. Fusion centres are state-owned and operated facilities and serve as focal points to gather, analyse and redistribute intelligence among state, local, tribal and territorial (SLTT), federal and private sector partners to detect criminal and terrorist activity.  However, US law enforcement found  a way around these limitations by continuing to receive Dataminr alerts outside of fusion centres.

Use of these technologies have, in the past, not been subject to public consultation and, without basic scrutiny at either a public or legislative level, there remains no solid mechanism for independent oversight of their use by law enforcement.