July 2024 - nexa - server-nexa.polito.it

OpenAI illegally stopped staff from sharing dangers, whistleblowers say - The Washington Post
by Alberto Cammozzo July 13, 2024

July 13, 2024

<https://www.washingtonpost.com/technology/2024/07/13/openai-safety-risks-wh…> OpenAI whistleblowers have filed a complaint with the Securities and Exchange Commission alleging the artificial intelligence company illegally prohibited its employees from warning regulators about the grave risks its technology may pose to humanity, calling for an investigation. The whistleblowers said OpenAI issued its employees overly restrictive employment, severance and nondisclosure agreements that could have led to penalties against workers who raised concerns about OpenAI to federal regulators, according to a seven-page letter sent to the SEC commissioner earlier this month that referred to the formal complaint. The letter was obtained exclusively by The Washington Post. OpenAI made staff sign employee agreements that required them to waive their federal rights to whistleblower compensation, the letter said. These agreements also required OpenAI staff to get prior consent from the company if they wished to disclose information to federal authorities. OpenAI did not create exemptions in its employee nondisparagement clauses for disclosing securities violations to the SEC. These overly broad agreements violated long-standing federal laws and regulations meant to protect whistleblowers who wish to reveal damning information about their company anonymously and without fear of retaliation, the letter said. “These contracts sent a message that ‘we don’t want … employees talking to federal regulators,’” said one of the whistleblowers, who spoke on the condition of anonymity for fear of retaliation. “I don’t think that AI companies can build technology that is safe and in the public interest if they shield themselves from scrutiny and dissent.” In a statement, Hannah Wong, a spokesperson for OpenAI said, “Our whistleblower policy protects employees’ rights to make protected disclosures. Additionally, we believe rigorous debate about this technology is essential and have already made important changes to our departure process to remove nondisparagement terms.” The whistleblowers’ letter comes amid concerns that OpenAI, which started as a nonprofit with an altruistic mission, is putting profit before safety in creating its technology. The Post reported Friday that OpenAI rushed out its latest AI model that fuels ChatGPT to meet a May release date set by company leaders, despite employee concerns that the company “failed” to live up to its own security testing protocol that it said would keep its AI safe from catastrophic harms, like teaching users to build bioweapons or helping hackers develop new kinds of cyberattacks. In a statement, OpenAI spokesperson Lindsey Held said the company “didn’t cut corners on our safety process, though we recognize the launch was stressful for our teams.” Tech companies’ strict confidentiality agreements have long vexed workers and regulators. During the #MeToo movement and national protests in response to the murder of George Floyd, workers warned that such legal agreements limited their ability to report sexual misconduct or racial discrimination. Regulators, meanwhile, have worried that the terms muzzle tech employees who could alert them to misconduct in the opaque tech sector, especially amid allegations that companies’ algorithms promote content that undermines elections, public health and children’s safety. The rapid advance of artificial intelligence sharpened policymakers’ concerns about the power of the tech industry, prompting a flood of calls for regulation. In the United States, AI companies are largely operating in a legal vacuum, and policymakers say they cannot effectively create new AI policies without the help of whistleblowers, who can help explain the potential threats posed by the fast-moving technology. “OpenAI’s policies and practices appear to cast a chilling effect on whistleblowers’ right to speak up and receive due compensation for their protected disclosures,” said Sen. Chuck Grassley (R-Iowa) in a statement to The Post. “In order for the federal government to stay one step ahead of artificial intelligence, OpenAI’s nondisclosure agreements must change.” A copy of the letter, addressed to SEC chairman Gary Gensler, was sent to Congress. The Post obtained the whistleblower letter from Grassley’s office. The official complaints referred to in the letter were submitted to the SEC in June. Stephen Kohn, a lawyer representing the OpenAI whistleblowers, said the SEC has responded to the complaint. It could not be determined whether the SEC has launched an investigation. The agency did not respond to a request for comment. The SEC must take “swift and aggressive” steps to address these illegal agreements, the letter says, as they might be relevant to the wider AI sector and could violate the October White House executive order that demands AI companies develop the technology safely. “At the heart of any such enforcement effort is the recognition that insiders … must be free to report concerns to federal authorities,” the letter said. “Employees are in the best position to detect and warn against the types of dangers referenced in the Executive Order and are also in the best position to help ensure that AI benefits humanity, instead of having the opposite effect.” These agreements threatened employees with criminal prosecutions if they reported violations of law to federal authorities under trade secret laws, Kohn said. Employees were instructed to keep company information confidential and threatened with “severe sanctions” without recognition of their right to report such information to the government, he said. “In terms of oversight of AI, we are at the very beginning,” Kohn said. “We need employees to step forward, and we need OpenAI to be open.” The SEC should require OpenAI to produce every employment, severance and investor agreement that contains nondisclosure clauses to ensure they don’t violate federal laws, the letter said. Federal regulators should require OpenAI to notify all past and current employees of the violations the company committed as well as notify them that they have the right to confidentially and anonymously report any violations of law to the SEC. The SEC should issue fines to OpenAI for “each improper agreement” under SEC law and direct OpenAI to cure the “chilling effect” of its past practices, according to the whistleblowers letter. Multiple tech employees, including Facebook whistleblower Frances Haugen, have filed complaints with the SEC, which established a whistleblower program in the wake of the 2008 financial crisis. Fighting back against Silicon Valley’s use of NDAs to “monopolize information” has been a protracted battle, said Chris Baker, a San Francisco lawyer. He won a $27 million settlement for Google employees in December against claims that the tech giant used onerous confidentiality agreements to block whistleblowing and other protected activity. Now tech companies are increasingly fighting back with clever ways to deter speech, he said. “Employers have learned that the cost of leaks is sometimes way greater than the cost of litigation, so they are willing to take the risk,” Baker said.

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Google Chrome favour google.com
by Giacomo Tesio July 11, 2024

July 11, 2024

It turns out Google Chrome (via Chromium) includes a default extension which makes extra services available to code running on the *.google.com domains - tweeted about today by Luca Casonato, but the code has been there in the public repo since October 2013 as far as I can tell. It looks like it's a way to let Google Hangouts (or presumably its modern predecessors) get additional information from the browser, including the current load on the user's CPU. Update: On Hacker News a Googler confirms that the Google Meet "troubleshooting" feature uses this to review CPU utilization. [...] The code doesn't do anything on non-Google domains. Luca says this - I'm inclined to agree: ``` This is interesting because it is a clear violation of the idea that browser vendors should not give preference to their websites over anyone elses. ``` <https://simonwillison.net/2024/Jul/9/hangout_servicesthunkjs/> Giacomo

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Intelligenza artificiale, monopoli e narrazioni
by de petra giulio July 10, 2024

July 10, 2024

Condivido con piacere questo contributo di Daniela Tafani appena pubblicato sul sito del Crs https://centroriformastato.it/le-big-tech-e-il-racconto-dellintelligenza-ar…

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Survey su human-ai ecosystems
by Daniele Gambetta July 9, 2024

July 9, 2024

Salve a tutte/i, vi scrivo per segnalarvi questo recente lavoro del nostro gruppo di ricerca (Unipi, SNS, CNR) nel quale abbiamo catalogato 141 paper su studi e analisi dell'effetto di recommender basati su IA nel comportamento umano... abbiamo sviluppato una tassonomia per catalogare metodologie di indagine e effetti studiati, in 4 diversi settori (Urban, Market, Social Media e Generativi)... ogni feedback è ben accetto :) https://arxiv.org/html/2407.01630v1 Saluti Daniele (PhD IA)

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Multiple nations enact mysterious export controls on quantum computers
by Fabio Alemagna July 8, 2024

July 8, 2024

> > *Identical wording placing limits on the export of quantum computers has > appeared in regulations across the globe. There doesn't seem to be any > scientific reason for the controls, and all can be traced to secret > international discussions* *Secret* international discussions have resulted in governments across the > world imposing *identical* export controls on quantum computers, while > *refusing* to disclose the scientific rationale behind the regulations. > Although quantum computers theoretically have the potential to threaten > national security by breaking encryption techniques, even the most advanced > quantum computers currently in public existence are too small and too > error-prone to achieve this, rendering the bans seemingly pointless. > The UK is one of the countries that has prohibited the export of > <https://www.newscientist.com/article/2431853-uk-ban-on-quantum-computer-exp…> quantum > computers with 34 or more quantum bits, or qubits, and error rates below a > certain threshold. The intention seems to be to restrict machines of a > certain capability, but the UK government hasn’t explicitly said this. A *New > Scientist* *freedom of information request* for a rationale behind these > numbers *was turned down on the grounds of national security*. https://www.newscientist.com/article/2436023-multiple-nations-enact-mysteri…

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MEMENTO | 174° Mercoledì di Nexa | 10 luglio 2024, ore 17.00
by Nexa - Media July 8, 2024

July 8, 2024

Gentilissime, gentilissimi, Vi ricordiamo che mercoledì 10 luglio, alle ore 17.00, si terrà il 174° Mercoledì di Nexa con un incontro dal titolo "La sfida per un diritto alla riparazione universale". Ospite dell'incontro: Ugo Vallauri (The Restart Project). L'incontro si terrà IN PRESENZA e ONLINE. SEDE FISICA dell'incontro: Centro Nexa su Internet e Società, Politecnico di Torino, Via Boggio 65/a, Torino (1° piano). Per accedere alla sala si raccomanda di suonare al citofono Portineria e di seguire le indicazioni segnalate lungo il percorso. QUI<https://nexa.polito.it/contatti> maggiori informazioni su come raggiungerci. STANZA VIRTUALE dell'incontro: https://didattica.polito.it/VClass/NexaEvent Maggiori informazioni alla pagina: https://nexa.polito.it/mercoledi-174 Ricordiamo che l'incontro si concluderà con un brindisi e un piccolo rinfresco per salutarci in vista della pausa estiva. Cordiali saluti, -- Valeria Bergantino Communication Manager Nexa Center for Internet & Society Politecnico di Torino - DAUIN Via Pier Carlo Boggio, 65/A - 10138 Torino web: https://nexa.polito.it/ mail: valeria.bergantino(a)polito.it<mailto:valeria.bergantino@polito.it> tel: 3473443585

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Meet Mercy and Anita – the African workers driving the AI revolution, for just over a dollar an hour | Artificial intelligence (AI) | The Guardian
by Alberto Cammozzo July 7, 2024

July 7, 2024

<https://www.theguardian.com/technology/article/2024/jul/06/mercy-anita-afri…> Mercy craned forward, took a deep breath and loaded another task on her computer. One after another, disturbing images and videos appeared on her screen. As a Meta content moderator working at an outsourced office in Nairobi, Mercy was expected to action one “ticket” every 55 seconds during her 10-hour shift. This particular video was of a fatal car crash. Someone had filmed the scene and uploaded it to Facebook, where it had been flagged by a user. Mercy’s job was to determine whether it had breached any of the company’s guidelines that prohibit particularly violent or graphic content. She looked closer at the video as the person filming zoomed in on the crash. She began to recognise one of the faces on the screen just before it snapped into focus: the victim was her grandfather. Mercy pushed her chair back and ran towards the exit, past rows of colleagues who looked on in concern. She was crying. Outside, she started calling relatives. There was disbelief – nobody else had heard the news yet. Her supervisor came out to comfort her, but also to remind her that she would need to return to her desk if she wanted to make her targets for the day. She could have a day off tomorrow in light of the incident – but given that she was already at work, he pointed out, she may as well finish her shift. New tickets appeared on the screen: her grandfather again, the same crash over and over. Not only the same video shared by others, but new videos from different angles. Pictures of the car; pictures of the dead; descriptions of the scene. She began to recognise everything now. Her neighbourhood, around sunset, only a couple of hours ago – a familiar street she had walked along many times. Four people had died. Her shift seemed endless. We spoke with dozens of workers just like Mercy at three data annotation and content moderation centres run by one company across Kenya and Uganda. Content moderators are the workers who trawl, manually, through social media posts to remove toxic content and flag violations of the company’s policies. Data annotators label data with relevant tags to make it legible for use by computer algorithms. Behind the scenes, these two types of “data work” make our digital lives possible. Mercy’s story was a particularly upsetting case, but by no means extraordinary. The demands of the job are intense. “Physically you are tired, mentally you are tired, you are like a walking zombie,” said one data worker who had migrated from Nigeria for the job. Shifts are long and workers are expected to meet stringent performance targets based on their speed and accuracy. Mercy’s job also requires close attention – content moderators can’t just zone out, because they have to correctly tag videos according to strict criteria. Videos need to be examined to find the highest violation as defined by Meta’s policies. Violence and incitement, for instance, are a higher violation than simple bullying and harassment – so it isn’t enough to identify a single violation and then stop. You have to watch the whole thing, in case it gets worse. “The most disturbing thing was not just the violence,” another moderator told us, “it was the sexually explicit and disturbing content.” Moderators witness suicides, torture and rape “almost every day”, commented the same moderator; “you normalise things that are just not normal.” Workers in these moderation centres are continually bombarded with graphic images and videos, and given no time to process what they are witnessing. They’re expected to action between 500 and 1,000 tickets a day. Many reported never feeling the same again: the job had made an indelible mark on their lives. The consequences can be devastating. “Most of us are damaged psychologically, some have attempted suicide … some of our spouses have left us and we can’t get them back,” commented one moderator who had been let go by the company. “The company policies were even more strenuous than the job itself,” remarked another. Workers at one of the content moderation centres we visited were left crying and shaking after witnessing beheading videos, and were told by management that at some point during the week they could have a 30-minute break to see a “wellness counsellor” – a colleague who had no formal training as a psychologist. Workers who ran away from their desks in response to what they’d seen were told they had committed a violation of the company’s policy because they hadn’t remembered to enter the right code on their computer indicating they were either “idle” or on a “bathroom break” – meaning their productivity scores could be marked down accordingly. The stories were endless: “I collapsed in the office”; “I went into a severe depression”; “I had to go to hospital”; “they had no concern for our wellbeing”. Workers told us that management was understood to monitor hospital records to verify whether an employee had taken a legitimate sick day – but never to wish them better, or out of genuine concern for their health. Job security at this particular company is minimal – the majority of workers we interviewed were on rolling one- or three-month contracts, which could disappear as soon as the client’s work was complete. They worked in rows of up to a hundred on production floors in a darkened building, part of a giant business park on the outskirts of Nairobi. Their employer was a client of Meta’s, a prominent business process outsourcing (BPO) company with headquarters in San Francisco and delivery centres in east Africa where insecure and low-income work could be distributed to local employees of the firm. Many of the workers, like Mercy herself, had once lived in the nearby Kibera slum – the largest urban slum in Africa – and were hired under the premise that the company was helping disadvantaged workers into formal employment. The reality is that many of these workers are too terrified to question management for fear of losing their jobs. Workers reported that those who complain are told to shut up and reminded that they could easily be replaced. While many of the moderators we spoke to were Kenyan, some had migrated from other African countries to work for the BPO and assist Meta in moderating other African languages. A number of these workers spoke about being identifiable on the street as foreigners, which added to their sense of being vulnerable to harassment and abuse from the Kenyan police. Police harassment wasn’t the only danger they faced. One woman we interviewed described how members of a “liberation front” in a neighbouring African country found names and pictures of Meta moderators and posted them online with menacing threats, because they disagreed with moderation decisions that had been made. These workers were terrified, of course, and went to the BPO with the pictures. The company informed them they would see about enhancing security at the production facilities; aside from that, they said, there was nothing else they could do – the workers should just “stay safe”. Most of us can hope never to experience the inhumane working conditions endured by Mercy and her colleagues. But data work of this kind is performed by millions of workers in different circumstances and locations around the world. At this particular centre, some of the working conditions changed after our research was conducted. However large companies such as Meta tend to have multiple outsourced providers of moderation services who compete for the most profitable contracts from the company. This data work is essential for the functioning of the everyday products and services we use – from social media apps to chatbots and new automated technologies. It’s a precondition for their very existence – were it not for content moderators constantly scanning posts in the background, social networks would be immediately flooded with violent and explicit material. Without data annotators creating datasets that can teach AI the difference between a traffic light and a street sign, autonomous vehicles would not be allowed on our roads. And without workers training machine learning algorithms, we would not have AI tools such as ChatGPT. One such worker we spoke to, Anita, worked for a BPO in Gulu, the largest city in northern Uganda. Anita has been working on a project for an autonomous vehicle company. Her job is to review hour after hour of footage of drivers at the wheel. She’s looking for any visual evidence of a lapse in concentration, or something resembling a “sleep state”. This assists the manufacturer in constructing an “in-cabin behaviour monitoring system” based on the driver’s facial expressions and eye movements. Sitting at a computer and concentrating on this footage for hours at a time is draining. Sometimes, Anita feels the boredom as a physical force, pushing her down in her chair and closing her eyelids. But she has to stay alert, just like the drivers on her screen. In return for 45 hours of intense, stressful work a week – possibly with unpaid overtime on top – annotators can expect to earn in the region of 800,000 Ugandan shillings a month, a little over US$200 or approximately $1.16 per hour. On the production floor, hundreds of data annotators sit in silence, lined up at rows of desks. The setup will be instantly familiar to anyone who’s worked at a call centre – the system of management is much the same. The light is dimmed in an attempt to reduce the eye strain that results from nine hours of intense concentration. The workers’ screens flicker with a constant stream of images and videos requiring annotation. Like Anita, workers are trained to identify elements of the image in response to client specifications: they may, for example, draw polygons around different objects, from traffic lights to stop signs and human faces. Every aspect of Anita and her fellow annotators’ working lives is digitally monitored and recorded. From the moment they use the biometric scanners to enter the secure facilities, to the extensive network of CCTV cameras, workers are closely surveilled. Every second of their shift must be accounted for according to the efficiency-monitoring software on their computer. Some workers we spoke to even believe managers cultivate a network of informers among the staff to make sure that attempts to form a trade union don’t sneak under the radar. Working constantly, for hours on end, is physically and psychologically draining. It offers little opportunity for self-direction; the tasks are reduced to their simplest form to maximise the efficiency and productivity of the workers. Annotators are disciplined into performing the same routine actions over and over again at top speed. As a result, they experience a curious combination of complete boredom and suffocating anxiety at the same time. This is the reality at the coalface of the AI revolution: people working under oppressive surveillance at furious intensity just to keep their jobs and support their families. When we think about the world of AI development our minds might naturally turn to engineers working in sleek, air-conditioned offices in Silicon Valley. What most people don’t realise is that roughly 80% of the time spent on training AI consists of annotating datasets. Frontier technologies such as autonomous vehicles, machines for nanosurgery and drones are all being developed in places like Gulu. As tech commentator Phil Jones puts it: “In reality, the magic of machine learning is the grind of data labelling.” This is where the really time-consuming and laborious work takes place. There is a booming global marketplace for data annotation, which was estimated to be worth $2.22bn in 2022 and is expected to grow at around 30% each year until it reaches over $17bn in 2030. As AI tools are taken up in retail, healthcare and manufacturing – to name just a few sectors that are being transformed – the demand for well-curated data will increase by the day. The majority of workers in the global south work in the informal jobs sector. Photograph: Yannick Tylle/Getty Images Today’s tech companies can use their wealth and power to exploit a deep division in how the digital labour of AI work is distributed across the globe. The majority of workers in countries in the global south work in the informal sector. Unemployment rates remain staggeringly high and well-paid jobs with employment protections remain elusive for many. Vulnerable workers in these contexts are not only likely to work for lower wages; they will also be less ready to demand better working conditions, because they know how easily they can be replaced. The process of outsourcing work to the global south is popular with businesses not because it provides much-needed economic opportunities for the less well off, but because it provides a clear route to a more tightly disciplined workforce, higher efficiency and lower costs. By using AI products we are directly inserting ourselves into the lives of workers dispersed across the globe. We are connected whether we like it or not. Just as drinking a cup of coffee implicates the coffee drinker in a global production network from bean to cup, we should all understand how using a search engine, a chatbot – or even something as simple as a smart robot vacuum – sets in motion global flows of data and capital that connect workers, organisations and consumers in every corner of the planet. Many tech companies therefore do what they can to hide the reality of how their products are actually made. They present a vision of shining, sleek, autonomous machines – computers searching through large quantities of data, teaching themselves as they go – rather than the reality of the poorly paid and gruelling human labour that both trains them and is managed by them. Back in Gulu, Anita has just arrived home from work. She sits outside with her children in plastic chairs under her mango tree. She’s tired. Her eyes start to close as the sun falls below the horizon. The children go to bed, and she will not be long after them. She needs to rest before her 5am start tomorrow, when she will be annotating again. Nobody ever leaves the BPO willingly – there’s nothing else to do. She sees her ex-colleagues when she’s on her way to work, hawking vegetables on the market or trying to sell popcorn by the side of the road. If there were other opportunities, people would seize them. She just has to keep her head down, hit her targets, and make sure that whatever happens, she doesn’t get laid off. Maybe another project will come in; maybe she could change to a new workflow. That would be a relief, something a bit different. Maybe labelling streets, drawing outlines around signs and trying to work out what it would be like to live at the other end of the lens, in a country with big illuminated petrol signs and green grass lawns. This is an edited extract from Feeding the Machine: The Hidden Human Labour Powering AI, by James Muldoon, Mark Graham and Callum Cant (Canongate £20). To support the Guardian and Observer, order your copy from guardianbookshop.com. Delivery charges may apply

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Meet Mercy and Anita – the African workers driving the AI revolution, for just over a dollar an hour | Artificial intelligence (AI) | The Guardian
by Alberto Cammozzo July 6, 2024

July 6, 2024

<https://www.theguardian.com/technology/article/2024/jul/06/mercy-anita-afri…> Mercy craned forward, took a deep breath and loaded another task on her computer. One after another, disturbing images and videos appeared on her screen. As a Meta content moderator working at an outsourced office in Nairobi, Mercy was expected to action one “ticket” every 55 seconds during her 10-hour shift. This particular video was of a fatal car crash. Someone had filmed the scene and uploaded it to Facebook, where it had been flagged by a user. Mercy’s job was to determine whether it had breached any of the company’s guidelines that prohibit particularly violent or graphic content. She looked closer at the video as the person filming zoomed in on the crash. She began to recognise one of the faces on the screen just before it snapped into focus: the victim was her grandfather. Mercy pushed her chair back and ran towards the exit, past rows of colleagues who looked on in concern. She was crying. Outside, she started calling relatives. There was disbelief – nobody else had heard the news yet. Her supervisor came out to comfort her, but also to remind her that she would need to return to her desk if she wanted to make her targets for the day. She could have a day off tomorrow in light of the incident – but given that she was already at work, he pointed out, she may as well finish her shift. New tickets appeared on the screen: her grandfather again, the same crash over and over. Not only the same video shared by others, but new videos from different angles. Pictures of the car; pictures of the dead; descriptions of the scene. She began to recognise everything now. Her neighbourhood, around sunset, only a couple of hours ago – a familiar street she had walked along many times. Four people had died. Her shift seemed endless. We spoke with dozens of workers just like Mercy at three data annotation and content moderation centres run by one company across Kenya and Uganda. Content moderators are the workers who trawl, manually, through social media posts to remove toxic content and flag violations of the company’s policies. Data annotators label data with relevant tags to make it legible for use by computer algorithms. Behind the scenes, these two types of “data work” make our digital lives possible. Mercy’s story was a particularly upsetting case, but by no means extraordinary. The demands of the job are intense. “Physically you are tired, mentally you are tired, you are like a walking zombie,” said one data worker who had migrated from Nigeria for the job. Shifts are long and workers are expected to meet stringent performance targets based on their speed and accuracy. Mercy’s job also requires close attention – content moderators can’t just zone out, because they have to correctly tag videos according to strict criteria. Videos need to be examined to find the highest violation as defined by Meta’s policies. Violence and incitement, for instance, are a higher violation than simple bullying and harassment – so it isn’t enough to identify a single violation and then stop. You have to watch the whole thing, in case it gets worse. “The most disturbing thing was not just the violence,” another moderator told us, “it was the sexually explicit and disturbing content.” Moderators witness suicides, torture and rape “almost every day”, commented the same moderator; “you normalise things that are just not normal.” Workers in these moderation centres are continually bombarded with graphic images and videos, and given no time to process what they are witnessing. They’re expected to action between 500 and 1,000 tickets a day. Many reported never feeling the same again: the job had made an indelible mark on their lives. The consequences can be devastating. “Most of us are damaged psychologically, some have attempted suicide … some of our spouses have left us and we can’t get them back,” commented one moderator who had been let go by the company. “The company policies were even more strenuous than the job itself,” remarked another. Workers at one of the content moderation centres we visited were left crying and shaking after witnessing beheading videos, and were told by management that at some point during the week they could have a 30-minute break to see a “wellness counsellor” – a colleague who had no formal training as a psychologist. Workers who ran away from their desks in response to what they’d seen were told they had committed a violation of the company’s policy because they hadn’t remembered to enter the right code on their computer indicating they were either “idle” or on a “bathroom break” – meaning their productivity scores could be marked down accordingly. The stories were endless: “I collapsed in the office”; “I went into a severe depression”; “I had to go to hospital”; “they had no concern for our wellbeing”. Workers told us that management was understood to monitor hospital records to verify whether an employee had taken a legitimate sick day – but never to wish them better, or out of genuine concern for their health. Job security at this particular company is minimal – the majority of workers we interviewed were on rolling one- or three-month contracts, which could disappear as soon as the client’s work was complete. They worked in rows of up to a hundred on production floors in a darkened building, part of a giant business park on the outskirts of Nairobi. Their employer was a client of Meta’s, a prominent business process outsourcing (BPO) company with headquarters in San Francisco and delivery centres in east Africa where insecure and low-income work could be distributed to local employees of the firm. Many of the workers, like Mercy herself, had once lived in the nearby Kibera slum – the largest urban slum in Africa – and were hired under the premise that the company was helping disadvantaged workers into formal employment. The reality is that many of these workers are too terrified to question management for fear of losing their jobs. Workers reported that those who complain are told to shut up and reminded that they could easily be replaced. While many of the moderators we spoke to were Kenyan, some had migrated from other African countries to work for the BPO and assist Meta in moderating other African languages. A number of these workers spoke about being identifiable on the street as foreigners, which added to their sense of being vulnerable to harassment and abuse from the Kenyan police. Police harassment wasn’t the only danger they faced. One woman we interviewed described how members of a “liberation front” in a neighbouring African country found names and pictures of Meta moderators and posted them online with menacing threats, because they disagreed with moderation decisions that had been made. These workers were terrified, of course, and went to the BPO with the pictures. The company informed them they would see about enhancing security at the production facilities; aside from that, they said, there was nothing else they could do – the workers should just “stay safe”. Most of us can hope never to experience the inhumane working conditions endured by Mercy and her colleagues. But data work of this kind is performed by millions of workers in different circumstances and locations around the world. At this particular centre, some of the working conditions changed after our research was conducted. However large companies such as Meta tend to have multiple outsourced providers of moderation services who compete for the most profitable contracts from the company. This data work is essential for the functioning of the everyday products and services we use – from social media apps to chatbots and new automated technologies. It’s a precondition for their very existence – were it not for content moderators constantly scanning posts in the background, social networks would be immediately flooded with violent and explicit material. Without data annotators creating datasets that can teach AI the difference between a traffic light and a street sign, autonomous vehicles would not be allowed on our roads. And without workers training machine learning algorithms, we would not have AI tools such as ChatGPT. One such worker we spoke to, Anita, worked for a BPO in Gulu, the largest city in northern Uganda. Anita has been working on a project for an autonomous vehicle company. Her job is to review hour after hour of footage of drivers at the wheel. She’s looking for any visual evidence of a lapse in concentration, or something resembling a “sleep state”. This assists the manufacturer in constructing an “in-cabin behaviour monitoring system” based on the driver’s facial expressions and eye movements. Sitting at a computer and concentrating on this footage for hours at a time is draining. Sometimes, Anita feels the boredom as a physical force, pushing her down in her chair and closing her eyelids. But she has to stay alert, just like the drivers on her screen. In return for 45 hours of intense, stressful work a week – possibly with unpaid overtime on top – annotators can expect to earn in the region of 800,000 Ugandan shillings a month, a little over US$200 or approximately $1.16 per hour. On the production floor, hundreds of data annotators sit in silence, lined up at rows of desks. The setup will be instantly familiar to anyone who’s worked at a call centre – the system of management is much the same. The light is dimmed in an attempt to reduce the eye strain that results from nine hours of intense concentration. The workers’ screens flicker with a constant stream of images and videos requiring annotation. Like Anita, workers are trained to identify elements of the image in response to client specifications: they may, for example, draw polygons around different objects, from traffic lights to stop signs and human faces. Every aspect of Anita and her fellow annotators’ working lives is digitally monitored and recorded. From the moment they use the biometric scanners to enter the secure facilities, to the extensive network of CCTV cameras, workers are closely surveilled. Every second of their shift must be accounted for according to the efficiency-monitoring software on their computer. Some workers we spoke to even believe managers cultivate a network of informers among the staff to make sure that attempts to form a trade union don’t sneak under the radar. Working constantly, for hours on end, is physically and psychologically draining. It offers little opportunity for self-direction; the tasks are reduced to their simplest form to maximise the efficiency and productivity of the workers. Annotators are disciplined into performing the same routine actions over and over again at top speed. As a result, they experience a curious combination of complete boredom and suffocating anxiety at the same time. This is the reality at the coalface of the AI revolution: people working under oppressive surveillance at furious intensity just to keep their jobs and support their families. When we think about the world of AI development our minds might naturally turn to engineers working in sleek, air-conditioned offices in Silicon Valley. What most people don’t realise is that roughly 80% of the time spent on training AI consists of annotating datasets. Frontier technologies such as autonomous vehicles, machines for nanosurgery and drones are all being developed in places like Gulu. As tech commentator Phil Jones puts it: “In reality, the magic of machine learning is the grind of data labelling.” This is where the really time-consuming and laborious work takes place. There is a booming global marketplace for data annotation, which was estimated to be worth $2.22bn in 2022 and is expected to grow at around 30% each year until it reaches over $17bn in 2030. As AI tools are taken up in retail, healthcare and manufacturing – to name just a few sectors that are being transformed – the demand for well-curated data will increase by the day. The majority of workers in the global south work in the informal jobs sector. Photograph: Yannick Tylle/Getty Images Today’s tech companies can use their wealth and power to exploit a deep division in how the digital labour of AI work is distributed across the globe. The majority of workers in countries in the global south work in the informal sector. Unemployment rates remain staggeringly high and well-paid jobs with employment protections remain elusive for many. Vulnerable workers in these contexts are not only likely to work for lower wages; they will also be less ready to demand better working conditions, because they know how easily they can be replaced. The process of outsourcing work to the global south is popular with businesses not because it provides much-needed economic opportunities for the less well off, but because it provides a clear route to a more tightly disciplined workforce, higher efficiency and lower costs. By using AI products we are directly inserting ourselves into the lives of workers dispersed across the globe. We are connected whether we like it or not. Just as drinking a cup of coffee implicates the coffee drinker in a global production network from bean to cup, we should all understand how using a search engine, a chatbot – or even something as simple as a smart robot vacuum – sets in motion global flows of data and capital that connect workers, organisations and consumers in every corner of the planet. Many tech companies therefore do what they can to hide the reality of how their products are actually made. They present a vision of shining, sleek, autonomous machines – computers searching through large quantities of data, teaching themselves as they go – rather than the reality of the poorly paid and gruelling human labour that both trains them and is managed by them. Back in Gulu, Anita has just arrived home from work. She sits outside with her children in plastic chairs under her mango tree. She’s tired. Her eyes start to close as the sun falls below the horizon. The children go to bed, and she will not be long after them. She needs to rest before her 5am start tomorrow, when she will be annotating again. Nobody ever leaves the BPO willingly – there’s nothing else to do. She sees her ex-colleagues when she’s on her way to work, hawking vegetables on the market or trying to sell popcorn by the side of the road. If there were other opportunities, people would seize them. She just has to keep her head down, hit her targets, and make sure that whatever happens, she doesn’t get laid off. Maybe another project will come in; maybe she could change to a new workflow. That would be a relief, something a bit different. Maybe labelling streets, drawing outlines around signs and trying to work out what it would be like to live at the other end of the lens, in a country with big illuminated petrol signs and green grass lawns. This is an edited extract from Feeding the Machine: The Hidden Human Labour Powering AI, by James Muldoon, Mark Graham and Callum Cant (Canongate £20). To support the Guardian and Observer, order your copy from guardianbookshop.com. Delivery charges may apply

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Can the climate survive the insatiable energy demands of the AI arms race? | Technology sector | The Guardian
by Alberto Cammozzo July 5, 2024

July 5, 2024

<https://www.theguardian.com/business/article/2024/jul/04/can-the-climate-su…> Can the climate survive the insatiable energy demands of the AI arms race? New computing infrastructure means big tech is likely to miss emissions targets but they can’t afford to get left behind in a winner takes all market Dan Milmo, Alex Hern and Jillian Ambrose The artificial intelligence boom has driven big tech share prices to fresh highs, but at the cost of the sector’s climate aspirations. Google admitted on Tuesday that the technology is threatening its environmental targets after revealing that datacentres, a key piece of AI infrastructure, had helped increase its greenhouse gas emissions by 48% since 2019. It said “significant uncertainty” around reaching its target of net zero emissions by 2030 – reducing the overall amount of CO2 emissions it is responsible for to zero – included “the uncertainty around the future environmental impact of AI, which is complex and difficult to predict”. It follows Microsoft, the biggest financial backer of ChatGPT developer OpenAI, admitting that its 2030 net zero “moonshot” might not succeed owing to its AI strategy. So will tech be able to bring down AI’s environmental cost, or will the industry plough on regardless because the prize of supremacy is so great? Why does AI pose a threat to tech companies’ green goals? Datacentres are a core component of training and operating AI models such as Google’s Gemini or OpenAI’s GPT-4. They contain the sophisticated computing equipment, or servers, that crunch through the vast reams of data underpinning AI systems. They require large amounts of electricity to run, which generates CO2 depending on the energy source, as well as creating “embedded” CO2 from the cost of manufacturing and transporting the necessary equipment. According to the International Energy Agency, total electricity consumption from datacentres could double from 2022 levels to 1,000 TWh (terawatt hours) in 2026, equivalent to the energy demand of Japan, while research firm SemiAnalysis calculates that AI will result in datacentres using 4.5% of global energy generation by 2030. Water usage is significant too, with one study estimating that AI could account for up to 6.6bn cubic metres of water use by 2027 – nearly two-thirds of England’s annual consumption. What do experts say about the environmental impact? A recent UK government-backed report on AI safety said that the carbon intensity of the energy source used by tech firms is “a key variable” in working out the environmental cost of the technology. It adds, however, that a “significant portion” of AI model training still relies on fossil fuel-powered energy. Indeed, tech firms are hoovering up renewable energy contracts in an attempt to meet their environmental goals. Amazon, for instance, is the world’s largest corporate purchaser of renewable energy. Some experts argue, though, that this pushes other energy users into fossil fuels because there is not enough clean energy to go round. “Energy consumption is not just growing, but Google is also struggling to meet this increased demand from sustainable energy sources,” says Alex de Vries, the founder of Digiconomist, a website monitoring the environmental impact of new technologies. Is there enough renewable energy to go round? Global governments plan to triple the world’s renewable energy resources by the end of the decade to cut consumption of fossil fuels in line with climate targets. But the ambitious pledge, agreed at last year’s COP28 climate talks, is already in doubt and experts fear that a sharp increase in energy demand from AI datacentres may push it further out of reach. The IEA, the world’s energy watchdog, has warned that even though global renewable energy capacity grew by the fastest pace recorded in the past 20 years in 2023, the world may only double its renewable energy by 2030 under current government plans. The answer to AI’s energy appetite may be for tech companies to invest more heavily in building new renewable energy projects to meet their growing power demand. How soon can we build new renewable energy projects? Onshore renewable energy projects such as wind and solar farms are relatively fast to build – they can take less than six months to develop. However, sluggish planning rules in many developed countries alongside a global logjam in connecting new projects to the power grid could add years to the process. Offshore windfarms and hydro power schemes face similar challenges in addition to construction times of between two and five years. This has raised concerns over whether renewable energy can keep pace with the expansion of AI. Major tech companies have already tapped a third of US nuclear power plants to supply low-carbon electricity to their datacentres, according to the Wall Street Journal. But without investing in new power sources these deals would divert low-carbon electricity away from other users leading to more fossil fuel consumption to meet overall demand. Will AI’s demand for electricity grow for ever? Normal rules of supply and demand would suggest that, as AI uses more electricity, the cost of energy rises and the industry is forced to economise. But the unique nature of the industry means that the largest companies in the world may instead decide to plough through spikes in the cost of electricity, burning billions of dollars as a result. The largest and most expensive datacentres in the AI sector are those used to train “frontier” AI, systems such as GPT-4o and Claude 3.5 which are more powerful and capable than any other. The leader in the field has changed over the years, but OpenAI is generally near the top, battling for position with Anthropic, maker of Claude, and Google’s Gemini. Already, the “frontier” competition is thought to be “winner takes all”, with very little stopping customers from jumping to the latest leader. That means that if one business spends $100m on a training run for a new AI system, its competitors have to decide to spend even more themselves or drop out of the race entirely. Worse, the race for so-called “AGI”, AI systems that are capable of doing anything a person can do, means that it could be worth spending hundreds of billions of dollars on a single training run – if doing so led your company to monopolise a technology that could, as OpenAI says, “elevate humanity”. Won’t AI firms learn to use less electricity? Every month, there are new breakthroughs in AI technology that enables companies to do more with less. In March 2022, for instance, a DeepMind project called Chinchilla showed researchers how to train frontier AI models using radically less computing power, by changing the ratio between the amount of training data and the size of the resulting model. But that didn’t result in the same AI systems using less electricity; instead, it resulted in the same amount of electricity being used to make even better AI systems. In economics, that phenomenon is known as “Jevons’ paradox”, after the economist who noted that the improvement of the steam engine by James Watt, which allowed for much less coal to be used, instead led to a huge increase in the amount of the fossil fuel burned in England. As the price of steam power plummeted following Watt’s invention, new uses were discovered that wouldn’t have been worthwhile when power was expensive.

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Fwd: [webscience-announce] NEW WSI Position Paper's and White Paper
by J.C. DE MARTIN July 4, 2024

July 4, 2024

-------- Forwarded Message -------- Subject: [webscience-announce] NEW WSI Position Paper's and White Paper Date: Thu, 4 Jul 2024 14:40:36 +0000 From: Web Science Institute <wsi(a)soton.ac.uk> Reply-To: Web Science Institute <wsi(a)soton.ac.uk> Dear all The Web Science Institute are pleased to share with you our latest Policy Papers. Democracy online: technologies for democratic deliberation <https://eprints.soton.ac.uk/491741/> Can the internet be designed to protect democracy and human rights <https://eprints.soton.ac.uk/491719/> Government Policy on AI: what should the next administration focus on <https://eprints.soton.ac.uk/491754/> To view all of our Policy Papers please visit our webpage here <https://www.southampton.ac.uk/research/institutes-centres/web-science-insti…>. Kind regards *Web Science Institute* University of Southampton, Building 32, Room 3053 Highfield Campus Southampton, SO17 1BJ

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