#algorithmic wage discrimination

[mostlysignssomeportents]

Lies, damned lies, and Uber

I'm on tour with my new, nationally bestselling novel The Bezzle! Catch me TONIGHT in PHOENIX (Changing Hands, Feb 29) then Tucson (Mar 10-11), San Francisco (Mar 13), and more!

Uber lies about everything, especially money. Oh, and labour. Especially labour. And geometry. Especially geometry! But especially especially money. They constantly lie about money.

Uber are virtuosos of mendacity, but in Toronto, the company has attained a heretofore unseen hat-trick: they told a single lie that is dramatically, materially untruthful about money, labour and geometry! It's an achievement for the ages.

Here's how they did it.

For several decades, Toronto has been clobbered by the misrule of a series of far-right, clownish mayors. This was the result of former Ontario Premier Mike Harris's great gerrymander of 1998, when the city of Toronto was amalgamated with its car-dependent suburbs. This set the tone for the next quarter-century, as these outlying regions – utterly dependent on Toronto for core economic activity and massive subsidies to pay the unsustainable utility and infrastructure bills for sprawling neighborhoods of single-family homes – proceeded to gut the city they relied on.

These "conservative" mayors – the philanderer, the crackhead, the sexual predator – turned the city into a corporate playground, swapping public housing and rent controls for out-of-control real-estate speculation and trading out some of the world's best transit for total car-dependency. As part of that decay, the city rolled out the red carpet for Uber, allowing the company to put as many unlicensed taxis as they wanted on the city's streets.

Now, it's hard to overstate the dire traffic situation in Toronto. Years of neglect and underinvestment in both the roads and the transit system have left both in a state of near collapse and it's not uncommon for multiple, consecutive main arteries to shut down without notice for weeks, months, or, in a few cases, years. The proliferation of Ubers on the road – driven by desperate people trying to survive the city's cost-of-living catastrophe – has only exacerbated this problem.

Uber, of course, would dispute this. The company insists – despite all common sense and peer-reviewed research – that adding more cars to the streets alleviates traffic. This is easily disproved: there just isn't any way to swap buses, streetcars, and subways for cars. The road space needed for all those single-occupancy cars pushes everything further apart, which means we need more cars, which means more roads, which means more distance between things, and so on.

It is an undeniable fact that geometry hates cars. But geometry loathes Uber. Because Ubers have all the problems of single-occupancy vehicles, and then they have the separate problem that they just end up circling idly around the city's streets, waiting for a rider. The more Ubers there are on the road, the longer each car ends up waiting for a passenger:

https://www.sfgate.com/technology/article/Uber-Lyft-San-Francisco-pros-cons-ride-hailing-13841277.php

Anything that can't go on forever eventually stops. After years of bumbling-to-sinister municipal rule, Toronto finally reclaimed its political power and voted in a new mayor, Olivia Chow, a progressive of long tenure and great standing (I used to ring doorbells for her when she was campaigning for her city council seat). Mayor Chow announced that she was going to reclaim the city's prerogative to limit the number of Ubers on the road, ending the period of Uber's "self-regulation."

Uber, naturally, lost its shit. The company claims to be more than a (geometrically impossible) provider of convenient transportation for Torontonians, but also a provider of good jobs for working people. And to prove it, the company has promised to pay its drivers "120% of minimum wage." As I write for Ricochet, that's a whopper, even by Uber's standards:

https://ricochet.media/en/4039/uber-is-lying-again-the-company-has-no-intention-of-paying-drivers-a-living-wage

Here's the thing: Uber is only proposing to pay 120% of the minimum wage while drivers have a passenger in the vehicle. And with the number of vehicles Uber wants on the road, most drivers will be earning nothing most of the time. Factor in that unpaid time, as well as expenses for vehicles, and the average Toronto Uber driver stands to make $2.50 per hour (Canadian):

https://ridefair.ca/wp-content/uploads/2024/02/Legislated-Poverty.pdf

Now, Uber's told a lot of lies over the years. Right from the start, the company implicitly lied about what it cost to provide an Uber. For its first 12 years, Uber lost $0.41 on every dollar it brought in, lighting tens of billions in investment capital provided by the Saudi royals on fire in an effort to bankrupt rival transportation firms and disinvestment in municipal transit.

Uber then lied to retail investors about the business-case for buying its stock so that the House of Saud and other early investors could unload their stock. Uber claimed that they were on the verge of producing a self-driving car that would allow them to get rid of drivers, zero out their wage bill, and finally turn a profit. The company spent $2.5b on this, making it the most expensive Big Store in the history of cons:

https://www.theinformation.com/articles/infighting-busywork-missed-warnings-how-uber-wasted-2-5-billion-on-self-driving-cars

After years, Uber produced a "self-driving car" that could travel one half of one American mile before experiencing a potentially lethal collision. Uber quietly paid another company $400m to take this disaster off its hands:

https://www.economist.com/business/2020/12/10/why-is-uber-selling-its-autonomous-vehicle-division

The self-driving car lie was tied up in another lie – that somehow, automation could triumph over geometry. Robocabs, we were told, would travel in formations so tight that they would finally end the Red Queen's Race of more cars – more roads – more distance – more cars. That lie wormed its way into the company's IPO prospectus, which promised retail investors that profitability lay in replacing every journey – by car, cab, bike, bus, tram or train – with an Uber ride:

https://www.reuters.com/article/idUSKCN1RN2SK/

The company has been bleeding out money ever since – though you wouldn't know it by looking at its investor disclosures. Every quarter, Uber trumpets that it has finally become profitable, and every quarter, Hubert Horan dissects its balance sheets to find the accounting trick the company thought of this time. There was one quarter where Uber declared profitability by marking up the value of stock it held in Uber-like companies in other countries.

How did it get this stock? Well, Uber tried to run a business in those countries and it was such a total disaster that they had to flee the country, selling their business to a failing domestic competitor in exchange for stock in its collapsing business. Naturally, there's no market for this stock, which, in Uber-land, means you can assign any value you want to it. So that one quarter, Uber just asserted that the stock had shot up in value and voila, profit!

https://www.nakedcapitalism.com/2022/02/hubert-horan-can-uber-ever-deliver-part-twenty-nine-despite-massive-price-increases-uber-losses-top-31-billion.html

But all of those lies are as nothing to the whopper that Uber is trying to sell to Torontonians by blanketing the city in ads: the lie that by paying drivers $2.50/hour to fill the streets with more single-occupancy cars, they will turn a profit, reduce the city's traffic, and provide good jobs. Uber says it can vanquish geometry, economics and working poverty with the awesome power of narrative.

In other words, it's taking Toronto for a bunch of suckers.

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2024/02/29/geometry-hates-uber/#toronto-the-gullible

Image: Rob Sinclair (modified) https://commons.wikimedia.org/wiki/File:Night_skyline_of_Toronto_May_2009.jpg

CC BY 2.0 https://creativecommons.org/licenses/by-sa/2.0/deed.en

#pluralistic #uber #hubert horan #fraud #toronto #geometry hates cars #urbanism #ontpoli #olivia chow #self-regulation #transport #urban planning #taxis #transit #urban theory #labor #algorithmic wage discrimination #veena dubal

[probablyasocialecologist]

Fifty per cent of web users are running ad blockers. Zero per cent of app users are running ad blockers, because adding a blocker to an app requires that you first remove its encryption, and that’s a felony. (Jay Freeman, the American businessman and engineer, calls this “felony contempt of business-model”.) So when someone in a boardroom says, “Let’s make our ads 20 per cent more obnoxious and get a 2 per cent revenue increase,” no one objects that this might prompt users to google, “How do I block ads?” After all, the answer is, you can’t. Indeed, it’s more likely that someone in that boardroom will say, “Let’s make our ads 100 per cent more obnoxious and get a 10 per cent revenue increase.” (This is why every company wants you to install an app instead of using its website.) There’s no reason that gig workers who are facing algorithmic wage discrimination couldn’t install a counter-app that co-ordinated among all the Uber drivers to reject all jobs unless they reach a certain pay threshold. No reason except felony contempt of business model, the threat that the toolsmiths who built that counter-app would go broke or land in prison, for violating DMCA 1201, the Computer Fraud and Abuse Act, trademark, copyright, patent, contract, trade secrecy, nondisclosure and noncompete or, in other words, “IP law”. IP isn’t just short for intellectual property. It’s a euphemism for “a law that lets me reach beyond the walls of my company and control the conduct of my critics, competitors and customers”. And “app” is just a euphemism for “a web page wrapped in enough IP to make it a felony to mod it, to protect the labour, consumer and privacy rights of its user”.

#Enshittification #big data #internet #social media #a long article but a good un

[brostateexam]

Two brothers who drive for Uber recently conducted an experiment. They opened their Uber apps while sitting in the same room, and tested which brother could earn more money to do the same work.

In a video published on The Rideshare Guy YouTube channel, the brothers recorded themselves looking for rides on the app. They found that Uber showed them nearly identical jobs, but offered to pay one of them a little better. The siblings could only guess why. Had Uber's algorithm somehow calculated their worth differently?

University of California College of the Law professor Veena Dubal says that's exactly what's going on. In a recent paper, she says rideshare apps promote "algorithmic wage discrimination" by personalizing wages for each driver based on data they gather from them. The algorithms are proprietary, so workers have no way of knowing how their data is being used, Dubal says.

"The app is their boss," Dubal told Morning Edition's A Martinez. "But unlike a human boss who you can negotiate with or withhold information from, the algorithms know so much about these workers."

#long reads #i thought the future would be cooler

[meret118]

Great article!

Excerpt:

Think of our ad blockers again. Fifty per cent of web users are running ad blockers. Zero per cent of app users are running ad blockers, because adding a blocker to an app requires that you first remove its encryption, and that’s a felony. (Jay Freeman, the American businessman and engineer, calls this “felony contempt of business-model”.)

So when someone in a boardroom says, “Let’s make our ads 20 per cent more obnoxious and get a 2 per cent revenue increase,” no one objects that this might prompt users to google, “How do I block ads?” After all, the answer is, you can’t. Indeed, it’s more likely that someone in that boardroom will say, “Let’s make our ads 100 per cent more obnoxious and get a 10 per cent revenue increase.” (This is why every company wants you to install an app instead of using its website.)

There's no reason that gig workers who are facing algorithmic wage discrimination couldn’t install a counter-app that co-ordinated among all the Uber drivers to reject all jobs unless they reach a certain pay threshold. No reason except felony contempt of business model, the threat that the toolsmiths who built that counter-app would go broke or land in prison, for violating DMCA 1201, the Computer Fraud and Abuse Act, trademark, copyright, patent, contract, trade secrecy, nondisclosure and noncompete or, in other words, “IP law”.

IP isn’t just short for intellectual property. It’s a euphemism for “a law that lets me reach beyond the walls of my company and control the conduct of my critics, competitors and customers”. And “app” is just a euphemism for “a web page wrapped in enough IP to make it a felony to mod it, to protect the labour, consumer and privacy rights of its user”.

#Technology #Corruption

[madfishmonger]

An interesting article with a little hope for the future but also I think some of you worker's rights protesters might be interested in this bit:

A gherao is a kind of protest in which employees refuse to let their managers or superiors leave the workplace until their demands are satisfied. The gherao originated in West Bengal when Subodh Banerjee, who was first labor minister then head of the public works department in the United Front Government of West Bengal in the late 1960s, introduced it as a formal means of protest in the labor sector. The gherao has been deployed time and time again as a tactic to protest against corporations and government actions since then.

#learn you a thing #workers rights #rights for everyone

[azspot]

#india #gigeconomy

[nahobinobrunestud]

Trans women playing bat with and devil's advocates for transmisogynists because they can't see farther than what's right in front of them at best are so obnoxious. They'll bitch about trans women making lighthearted jokes and being horny before they condemn blatantly obvious transphobia from a staff team that uses their wage hours to discriminate against trans woman on a level that could potentially get their asses a lawsuit a second time and expect you to care that it might just be one or two people when said people are a. still clearly part of the company for this shit to keep happening and haven't been fired yet and b. effect and reflect on said company that wants to be seen as a single entity regardless like most companies do so why should anyone give their transphobia the benefit of a doubt? Are we really playing "It's just a few bad apples here?" Shits been going on for years plus racism too, this shit ain't new. It ain't just one nigga. Is a place having more than one transphobe so incomprehensible or are people still delusioning themselves it's all accidental algorithms? I see no reason to have patience with this shit, company or its goofy ass defenders.

#transmisogyny

[anniekoh]

elsewhere on the internet: technology platforms & AI

The Limitations of ChatGPT with Emily Bender and Casey Fiesler

The Radical AI podcast (March 2023)

In this episode, we unpack the limitations of ChatGPT. We interview Dr. Emily M. Bender and Dr. Casey Fiesler about the ethical considerations of ChatGPT, bias and discrimination, and the importance of algorithmic literacy in the face of chatbots.

Emily M. Bender is a Professor of Linguistics and an Adjunct Professor in the School of Computer Science and the Information School at the University of Washington, where she has been on the faculty since 2003. Her research interests include multilingual grammar engineering, computational semantics, and the societal impacts of language technology. Emily was also recently nominated as a Fellow of the American Association for the Advancement of Science (AAAS).

Casey Fiesler is an associate professor in Information Science at University of Colorado Boulder. She researches and teaches in the areas of technology ethics, internet law and policy, and online communities. Also a public scholar, she is a frequent commentator and speaker on topics of technology ethics and policy, and her research has been covered everywhere from The New York Times to Teen Vogue.

Will A.I. Become the New McKinsey? by Ted Chiang (The New Yorker, May 2023)

People who criticize new technologies are sometimes called Luddites, but it’s helpful to clarify what the Luddites actually wanted. The main thing they were protesting was the fact that their wages were falling at the same time that factory owners’ profits were increasing, along with food prices. They were also protesting unsafe working conditions, the use of child labor, and the sale of shoddy goods that discredited the entire textile industry. The Luddites did not indiscriminately destroy machines; if a machine’s owner paid his workers well, they left it alone. The Luddites were not anti-technology; what they wanted was economic justice. They destroyed machinery as a way to get factory owners’ attention.

Whenever anyone accuses anyone else of being a Luddite, it’s worth asking, is the person being accused actually against technology? Or are they in favor of economic justice? And is the person making the accusation actually in favor of improving people’s lives? Or are they just trying to increase the private accumulation of capital?

In 1980, it was common to support a family on a single income; now it’s rare. So, how much progress have we really made in the past forty years? Sure, shopping online is fast and easy, and streaming movies at home is cool, but I think a lot of people would willingly trade those conveniences for the ability to own their own homes, send their kids to college without running up lifelong debt, and go to the hospital without falling into bankruptcy. It’s not technology’s fault that the median income hasn’t kept pace with per-capita G.D.P.; it’s mostly the fault of Ronald Reagan and Milton Friedman. But some responsibility also falls on the management policies of C.E.O.s like Jack Welch, who ran General Electric between 1981 and 2001, as well as on consulting firms like McKinsey. I’m not blaming the personal computer for the rise in wealth inequality—I’m just saying that the claim that better technology will necessarily improve people’s standard of living is no longer credible.

[Image shows Stable Diffusion generated images for “Committed Janitor”]

Researchers Find Stable Diffusion Amplifies Stereotypes by Justin Hendrix (Tech Policy, Nov 2022)

Sasha Luccioni, an artificial intelligence (AI) researcher at Hugging Face, a company that develops AI tools, recently released a project she calls the Stable Diffusion Explorer. With a menu of inputs, a user can compare how different professions are represented by Stable Diffusion, and how variables such as adjectives may alter image outputs. An “assertive firefighter,” for instance, is depicted as white male. A “committed janitor” is a person of color.

A talk: How To Find Things Online by v buckenham (May 2023)

And the other way to look at this, really, is not about AI at all, but seeing this as the continuation of a gradual corporate incursion into the early spirit of sharing that characterised the internet. I say incursion but maybe the better word is enclosure, as in enclosure of the commons. And this positions AI as just a new method by which companies try to extract value from the things people share freely, and capture that value for themselves. And maybe the way back from this is being more intentional about building our communities in ways where the communities own them. GameFAQs was created to collate some useful stuff together for a community, and it ended up as part of a complicated chain of corporate mergers and acquisitions. But other communities experienced the kinds of upheaval that came with that, and then decided to create their own sites which can endure outside of that - I’m thinking here especially of Archive of Our Own, the biggest repository for fan-writing online. And incidentally, the source of 8.2 million words in that AI training set, larger even than Reddit.

The technologies of all dead generations by Ben Tarnoff  (Apr 2023)

The three waves of algorithmic accountability

First wave: Harm reduction

Second wave: Abolition

Third wave: Alternatives

The third wave of algorithmic accountability, then, is already in motion. It’s a welcome development, and one that I wholeheartedly support.

But I’m also wary of it. There is a sense of relief when one moves from critique to creation. It satisfies the familiar American impulse to be practical, constructive, solution-oriented. And this introduces a danger, which is that in the comfort we derive from finally doing something rather than just talking and writing and analyzing and arguing, we get too comfortable, and act without an adequate understanding of the difficulties that condition and constrain our activity.

Platforms don't exist by Ben Tarnoff (Nov 2019)

By contrast, a left tech policy should aim to make markets mediate less of our lives—to make them less central to our survival and flourishing. This is typically referred to as decommodification, and it’s closely related to another core principle, democratization. Capitalism is driven by continuous accumulation, and continuous accumulation requires the commodification of as many things and activities as possible. Decommodification tries to roll this process back, by taking certain things and activities off the market. This lets us do two things: 1. The first is to give everybody the resources (material and otherwise) that they need to survive and to flourish—as a matter of right, not as a commodity. People get what they need, not just what they can afford. 2. The second is to give everybody the power to participate in the decisions that most affect them.

#elsewhere on the internet #technology #artificial intelligence

[bradassboy]

https://youtu.be/geeDnX-m4iE?si=dmy1pTiCPMadl_QP

I’ve been talking with my passengers, and paying attention to my fares. Most rides, when going through Uber’s main app; I will make 40% of what passengers are paying.. what this algorithmic wage discrimination is doing is unfair to drivers and customers both.

Tinyurl.com/AWDiscrimination

Companies boast drivers are being paid well, then customers get upset at how much they are being charged for a ride..

Don’t blame the drivers, we aren’t making what you think!

Big gig companies need to be held accountable on these unethical practices before all jobs start adopting this madness… by everyone..

#uber #lyft #fairness #fairpay #justice #economicjustice #socialjustice #union #uberunion #lyftunion #taxidrivers #deliverydrivers #uberfares #lyftfares #injustice

#uber #lyft #economic justice #social justice #delivery drivers #union #fair pay #injustice #taxi service #Youtube

[jennbarrigar]

Today, facilitated by independent contractor status, algorithmic wage discrimination turns the basic logic of scientific management on its head. Instead of using data and automation technologies to increase productivity by enabling workers to work more efficiently in a shorter period (to decrease labor overhead), on-demand companies like Uber and Amazon use data extracted from labor, along with insights from behavioral science, to engineer systems in which workers are less productive (they perform the same amount of work over longer hours) and receive lower wages, thereby maintaining a large labor supply while simultaneously keeping labor overhead low.

[mostlysignssomeportents]

No, Uber's (still) not profitable

Going to Defcon this weekend? I'm giving a keynote, "An Audacious Plan to Halt the Internet's Enshittification and Throw it Into Reverse," on Saturday at 12:30pm, followed by a book signing at the No Starch Press booth at 2:30pm!

https://info.defcon.org/event/?id=50826

Bezzle (n): 1. "the magic interval when a confidence trickster knows he has the money he has appropriated but the victim does not yet understand that he has lost it" (JK Gabraith) 2. Uber.

Uber was, is, and always will be a bezzle. There are just intrinsic limitations to the profits available to operating a taxi fleet, even if you can misclassify your employees as contractors and steal their wages, even as you force them to bear the cost of buying and maintaining your taxis.

The magic of early Uber – when taxi rides were incredibly cheap, and there were always cars available, and drivers made generous livings behind the wheel – wasn't magic at all. It was just predatory pricing.

Uber lost $0.41 on every dollar they brought in, lighting $33b of its investors' cash on fire. Most of that money came from the Saudi royals, funneled through Softbank, who brought you such bezzles as WeWork – a boring real-estate company masquerading as a high-growth tech company, just as Uber was a boring taxi company masquerading as a tech company.

Predatory pricing used to be illegal, but Chicago School economists convinced judges to stop enforcing the law on the grounds that predatory pricing was impossible because no rational actor would choose to lose money. They (willfully) ignored the obvious possibility that a VC fund could invest in a money-losing business and use predatory pricing to convince retail investors that a pile of shit of sufficient size must have a pony under it somewhere.

This venture predation let investors – like Prince Bone Saw – cash out to suckers, leaving behind a money-losing business that had to invent ever-sweatier accounting tricks and implausible narratives to keep the suckers on the line while they blew town. A bezzle, in other words:

https://pluralistic.net/2023/05/19/fake-it-till-you-make-it/#millennial-lifestyle-subsidy

Uber is a true bezzle innovator, coming up with all kinds of fairy tales and sci-fi gimmicks to explain how they would convert their money-loser into a profitable business. They spent $2.5b on self-driving cars, producing a vehicle whose mean distance between fatal crashes was half a mile. Then they paid another company $400 million to take this self-licking ice-cream cone off their hands:

https://pluralistic.net/2022/10/09/herbies-revenge/#100-billion-here-100-billion-there-pretty-soon-youre-talking-real-money

Amazingly, self-driving cars were among the more plausible of Uber's plans. They pissed away hundreds of millions on California's Proposition 22 to institutionalize worker misclassification, only to have the rule struck down because they couldn't be bothered to draft it properly. Then they did it again in Massachusetts:

https://pluralistic.net/2022/06/15/simple-as-abc/#a-big-ask

Remember when Uber was going to plug the holes in its balance sheet with flying cars? Flying cars! Maybe they were just trying to soften us up for their IPO, where they advised investors that the only way they'd ever be profitable is if they could replace every train, bus and tram ride in the world:

https://48hills.org/2019/05/ubers-plans-include-attacking-public-transit/

Honestly, the only way that seems remotely plausible is when it's put next to flying cars for comparison. I guess we can be grateful that they never promised us jetpacks, or, you know, teleportation. Just imagine the market opportunity they could have ascribed to astral projection!

Narrative capitalism has its limits. Once Uber went public, it had to produce financial disclosures that showed the line going up, lest the bezzle come to an end. These balance-sheet tricks were as varied as they were transparent, but the financial press kept falling for them, serving as dutiful stenographers for a string of triumphant press-releases announcing Uber's long-delayed entry into the league of companies that don't lose more money every single day.

One person Uber has never fooled is Hubert Horan, a transportation analyst with decades of experience who's had Uber's number since the very start, and who has done yeoman service puncturing every one of these financial "disclosures," methodically sifting through the pile of shit to prove that there is no pony hiding in it.

In 2021, Horan showed how Uber had burned through nearly all of its cash reserves, signaling an end to its subsidy for drivers and rides, which would also inevitably end the bezzle:

https://pluralistic.net/2021/08/10/unter/#bezzle-no-more

In mid, 2022, Horan showed how the "profit" Uber trumpeted came from selling off failed companies it had acquired to other dying rideshare companies, which paid in their own grossly inflated stock:

https://pluralistic.net/2022/08/05/a-lousy-taxi/#a-giant-asterisk

At the end of 2022, Horan showed how Uber invented a made-up, nonstandard metric, called "EBITDA profitability," which allowed them to lose billions and still declare themselves to be profitable, a lie that would have been obvious if they'd reported their earnings using Generally Accepted Accounting Principles (GAAP):

https://pluralistic.net/2022/02/11/bezzlers-gonna-bezzle/#gryft

Like clockwork, Uber has just announced – once again – that it is profitable, and once again, the press has credulously repeated the claim. So once again, Horan has published one of his magisterial debunkings on Naked Capitalism:

https://www.nakedcapitalism.com/2023/08/hubert-horan-can-uber-ever-deliver-part-thirty-three-uber-isnt-really-profitable-yet-but-is-getting-closer-the-antitrust-case-against-uber.html

Uber's $394m gains this quarter come from paper gains to untradable shares in its loss-making rivals – Didi, Grab, Aurora – who swapped stock with Uber in exchange for Uber's own loss-making overseas divisions. Yes, it's that stupid: Uber holds shares in dying companies that no one wants to buy. It declared those shares to have gained value, and on that basis, reported a profit.

Truly, any big number multiplied by an imaginary number can be turned into an even bigger number.

Now, Uber also reported "margin improvements" – that is, it says that it loses less on every journey. But it didn't explain how it made those improvements. But we know how the company did it: they made rides more expensive and cut the pay to their drivers. A 2.9m ride in Manhattan is now $50 – if you get a bargain! The base price is more like $70:

https://www.wired.com/story/uber-ceo-will-always-say-his-company-sucks/

The number of Uber drivers on the road has a direct relationship to the pay Uber offers those drivers. But that pay has been steeply declining, and with it, the availability of Ubers. A couple weeks ago, I found myself at the Burbank train station unable to get an Uber at all, with the app timing out repeatedly and announcing "no drivers available."

Normally, you can get a yellow taxi at the station, but years of Uber's predatory pricing has caused a drawdown of the local taxi-fleet, so there were no taxis available at the cab-rank or by dispatch. It took me an hour to get a cab home. Uber's bezzle destroyed local taxis and local transit – and replaced them with worse taxis that cost more.

Uber won't say why its margins are improving, but it can't be coming from scale. Before the pandemic, Uber had far more rides, and worse margins. Uber has diseconomies of scale: when you lose money on every ride, adding more rides increases your losses, not your profits.

Meanwhile, Lyft – Uber's also-ran competitor – saw its margins worsen over the same period. Lyft has always been worse at lying about it finances than Uber, but it is in essentially the exact same business (right down to the drivers and cars – many drivers have both apps on their phones). So Lyft's financials offer a good peek at Uber's true earnings picture.

Lyft is actually slightly better off than Uber overall. It spent less money on expensive props for its long con – flying cars, robotaxis, scooters, overseas clones – and abandoned them before Uber did. Lyft also fired 24% of its staff at the end of 2022, which should have improved its margins by cutting its costs.

Uber pays its drivers less. Like Lyft, Uber practices algorithmic wage discrimination, Veena Dubal's term describing the illegal practice of offering workers different payouts for the same work. Uber's algorithm seeks out "pickers" who are choosy about which rides they take, and converts them to "ants" (who take every ride offered) by paying them more for the same job, until they drop all their other gigs, whereupon the algorithm cuts their pay back to the rates paid to ants:

https://pluralistic.net/2023/04/12/algorithmic-wage-discrimination/#fishers-of-men

All told, wage theft and wage cuts by Uber transferred $1b/quarter from labor to Uber's shareholders. Historically, Uber linked fares to driver pay – think of surge pricing, where Uber charged riders more for peak times and passed some of that premium onto drivers. But now Uber trumpets a custom pricing algorithm that is the inverse of its driver payment system, calculating riders' willingness to pay and repricing every ride based on how desperate they think you are.

This pricing is a per se antitrust violation of Section 2 of the Sherman Act, America's original antitrust law. That's important because Sherman 2 is one of the few antitrust laws that we never stopped enforcing, unlike the laws banning predator pricing:

https://ilr.law.uiowa.edu/sites/ilr.law.uiowa.edu/files/2023-02/Woodcock.pdf

Uber claims an 11% margin improvement. 6-7% of that comes from algorithmic price discrimination and service cutbacks, letting it take 29% of every dollar the driver earns (up from 22%). Uber CEO Dara Khosrowshahi himself says that this is as high as the take can get – over 30%, and drivers will delete the app.

Uber's food delivery service – a baling wire-and-spit Frankenstein's monster of several food apps it bought and glued together – is a loser even by the standards of the sector, which is unprofitable as a whole and experiencing an unbroken slide of declining demand.

Put it all together and you get a picture of the kind of taxi company Uber really is: one that charges more than traditional cabs, pays drivers less, and has fewer cars on the road at times of peak demand, especially in the neighborhoods that traditional taxis had always underserved. In other words, Uber has broken every one of its promises.

We replaced the "evil taxi cartel" with an "evil taxi monopolist." And it's still losing money.

Even if Lyft goes under – as seems inevitable – Uber can't attain real profitability by scooping up its passengers and drivers. When you're losing money on every ride, you just can't make it up in volume.

Image: JERRYE AND ROY KLOTZ MD (modified) https://commons.wikimedia.org/wiki/File:LA_BREA_TAR_PITS,_LOS_ANGELES.jpg

CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en

I’m kickstarting the audiobook for “The Internet Con: How To Seize the Means of Computation,” a Big Tech disassembly manual to disenshittify the web and bring back the old, good internet. It’s a DRM-free book, which means Audible won’t carry it, so this crowdfunder is essential. Back now to get the audio, Verso hardcover and ebook:

http://seizethemeansofcomputation.org

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/08/09/accounting-gimmicks/#unter

Image: JERRYE AND ROY KLOTZ MD (modified) https://commons.wikimedia.org/wiki/File:LA_BREA_TAR_PITS,_LOS_ANGELES.jpg

CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en

#pluralistic #bezzles #hubert horan #uber #rideshare #accounting tricks #financial engineering #late-stage capitalism #narrative capitalism #lyft #transit #uber eats #venture predation #algorithmic wage discrimination

[collaraction7]

7 Tips To Grow Your Proxy

One of many things that help Oxylabs rank among the very best proxy suppliers is its residential proxy provide. An algorithm designed to assist medical doctors detect cardiac circumstances that is educated on historic diagnostic data could learn to focus on males's signs and not on women's, which would exacerbate the issue of underdiagnosing ladies. Reasonably, the main focus must be on reform and elevated funding for the lengthy-term care system so that nursing houses and assisted residing amenities are sufficiently staffed, with workers paid a residing wage. The team is making an attempt to understand how simply Max, and dozens of different babies like him, can focus attention and block out distractions when working on a selected job. The expertise sector is dependent upon public sources, and it's important that we acknowledge the role that digital data, content, media, algorithms, and different public resources play in making all of this working. These algorithms could cause doctors to misdiagnose Black patients and divert sources away from them. Malleable actuality is standard fare for Black Ops. Of the caregiving services that responded, some 11% had initiated use of cameras on their premises.

Respondents to the survey pointed to potential benefits of cameras, as properly, significantly as deterrents to abuse, and to use by the services themselves to inform about particular person residents' wants and as assets to assist staff improve. Use the safety Proxy Wizard to manage your Safety Proxy settings and certificates. A less-cited-and infrequently ignored-problem, Berridge added, is the legal duty the digital camera proprietor has for the safety of the feed. Installing a camera with out establishing a secure portal can expose the resident (and a roommate) to hackers. From against the law-prevention perspective, those are occasions when a resident is most susceptible, however from a privateness perspective, the resident might not need such footage to be recorded, let alone considered. Tied to questions about privacy is the difficulty of consent, Berridge stated-not solely whether or not the resident has the capability to consent to being monitored, but also, in the case of two-individual rooms, whether the roommate can consent.

” That’s all of the stuff that simply finally ends up being deleted instantly. The one thing is you want to be sure of utilizing a proxy server that’s in the proper location. “It gives Internet access for an entire company through one single server using low-cost connections. AI developers and doctors utilizing AI possible do not imply to hurt patients. It thus identifies non-Black patients as more prone to die of heart illness. More than 270 services from 39 states responded to the anonymous survey, which included particular and open-ended questions on policies and use of surveillance cameras. To remove the path, use cookiePathRewrite: "". There’s even an possibility to purchase a dedicated IP tackle, which is an IP handle that solely you should use. The good news is that there are many free net proxies that have inbuilt paid IP deal with software program's to curb the scamming and identification theft drawback in the best possible method. Within the fields of employment and housing, people who really feel that they have suffered discrimination can sue for disparate influence discrimination. free proxies are the best is a critical drawback that can damage many patients, and it is the accountability of these in the expertise and health care fields to recognize and handle it.

AI is changing into more prevalent in well being care. Collectively, the Evolution Series helps streamline the digital content material workflow, making copying, transferring, modifying and distributing content more efficient, scalable and reliable. We're all short of time, and pace is a huge asset in relation to shopping content on cell devices. Cellular functions might contain hardcoded secrets or API keys for the appliance to entry sure internet services. Finally, Berridge and her co-authors say that while cameras might offer households some comfort, they aren't the reply to preventing abuse, or a proxy for accountability. Algorithms that alter for race could also be primarily based on inaccurate generalizations and could mislead physicians. Though these are easy algorithms that are not necessarily integrated into AI systems, AI builders typically make similar assumptions once they develop their algorithms. However in each instances the assumptions have been mistaken. Their specialized services include social media, online gaming, advertising, shopping, and a number of other other goal proxy use instances.

#FREE PROXIES #Free Proxies Are The Best

[dwongmy]

Blogpost #6 Precarity and digital labour

This week, I have chosen to reflect on Roseblat's readings about the glamourisation of driving/car-sharing labour. In this post, I will be reflecting more on my personal experiences with Grab/Uber drivers instead. Briefly summarising, Rosenblat uses Uber as a case study to draw comparisons between the promises of the sharing economy to the reality that these drivers are facing. Uber operated in the sharing economy, and promised to serve as an engine of job creation. Uber has glamourised driving to be a job which was highly autonomous, flexible and independent. However, Uber has also reinforced the notion that these jobs that are fundamentally reside in the gig/sharing economy are of lower value, which I believe is very prevalent in Singapore society as well. Often times, because of the glamourisation of drivers, we might overlook the many problems that plague the lives of these drivers (especially older ones).

My first point is about optimisers and the creation of another class system and the increase precarity of older drivers. As the sharing economy has become more tech-enabled, drivers who are tech-savvy are the ones that are able to benefit most from driving today. Many of the Grab drivers I have met are often utilising multiple apps at one go, from Grab, Uber, Ryde and GoJek. This is because of the incentivised pricing schemes that are present in these apps. So in order to work efficiently, drivers have to be well acquainted with multiple driving platforms, which is not easy especially for older drivers. Furthermore, I have done an interview with a highly successful Uber driver in the past, and he was able to consistently achieve a 5-figure net salary. According to him, this was because he had managed to understand and exploit the algorithms behind these platforms as he had a Degree in Computer Science. His initial foray into driving as a side-hustle eventually evolved to his staple job and he managed to find security in it. However, I believe this is an exceptional case as he was probably the top 1 percentile with regards to these drivers. The point I want to make about optimisers is that, although the fundamental promise of the sharing economy and platforms such as Grab and Uber was to create flexible jobs and promote social mobility, it has also created a distinct separation between high and low performing drivers, and the difference points back to education and age which are fundamental points of discrimination in labour and wage to begin with. Especially during this pandemic, many young drivers are entering the market which is resulting in the increase precarity of older drivers. So much so that the Government has decided to increase the age required to apply for licenses to work as a driver, to protect the livelihoods of older drivers.

Secondly, I just wanted to point out the interesting political implications of car-sharing I have observed during my time in Bali. Because of how the taxi industry in Bali is largely connected to villages and tribes, taxi drivers traditionally had to pay taxes to their respective village leaders. This arrangement has allowed villages to survive and ride the waves of tourism in Bali. However with the introduction of Uber and GoJek, drivers have been switching to these online platforms because of the convenience and high volume of customers they can attain and ultimately a better salary. Tourists too prefer these platforms as they have transparent pricing policies, which might be lacking in traditional flag-down cabs which results in predatory practices. However, this disruption in the main stream of financing for villages have resulted in political tensions between ride-sharing companies and the residents of Bali. There are certain parts of the city where Uber/Gojeks are not allowed to enter, and cars getting vandalised or sabotaged as well. Ultimately, the examples I have mentioned illustrate the consequences of ride-sharing platforms and how the uplifting of certain groups of people often result in the disadvantaging of another.

[apassionateman]

Workplace Age Discrimination Still Flourishes in America

It's time to step up and stop the last acceptable bias.

Article by Joe Kita

About 35 percent of the U.S. population is now age 50 or older. Yet, in 2018, the Equal Employment Opportunity Commission — the nation’s workforce watchdog — issued a damning special report on age discrimination against older Americans. It concluded that even though 50 years had passed since Congress outlawed the practice, “age discrimination remains a significant and costly problem for workers, their families and our economy.”

Victoria Lipnic, the EEOC’s acting chair at the time, went so far as to compare it to harassment: “Everyone knows it happens every day to workers in all kinds of jobs, but few speak up. It’s an open secret.”

That same year, an AARP survey found that:

Nearly 1 in 4 workers age 45 and older have been subjected to negative comments about their age from supervisors or coworkers.

About 3 in 5 older workers have seen or experienced age discrimination in the workplace.

76 percent of these older workers see age discrimination as a hurdle to finding a new job; another report found that more than half of these older workers are prematurely pushed out of longtime jobs and 90 percent of them never earn as much again.

Diane Huth’s story is not unusual. “I am 69 years old, and that means I am unemployable,” says Huth, who lives in San Antonio. “I worked in corporate America for more than 40 years with big-name companies in branding. But I cannot get a job, the same job I rocked 15 years ago. I cannot even get an interview for that job because of all the screening mechanisms. I’m just too old; nobody takes me seriously for a job at my age, even in things I had excelled at.”

That rampant discrimination has a huge ripple effect:

29 percent of U.S. households headed by someone age 55 or older have no retirement savings or pension, meaning they’ll have to continue working or rely on Social Security to survive. But if the only job that remains open to them is unskilled and minimum wage, what does their future hold?

Older people who don’t feel useful are three times more likely to develop a disability and four times more likely to die prematurely, compared with counterparts who do feel useful, according to a 2007 study published in the Journals of Gerontology. If 30-plus years as a professional are suddenly thoroughly discounted by the business world, the effect on your health and longevity is undeniable.

Paradoxically, what most companies do not seem to understand is that older workers possess a depth of knowledge and experience that’s worth paying for, is not easily replaced and can be tapped in many different ways.

“People walk out of companies now with an enormous amount of intellectual property in their heads,” says Paul Rupert, the founder and CEO of Respectful Exits, a nonprofit consulting firm that’s raising corporate awareness about age discrimination. “They know things that are essential to the company’s success, and if that knowledge is not captured and transmitted to the next generation, that company is losing a tremendous chunk of capital and it’ll eventually pay a price.”

How did we get to this point? And how can we combat such widespread age discrimination?

To answer these questions, the AARP Bulletin asked me to independently examine ageism in the workplace to determine why it is so prevalent and what can be done about it, to provide both a snapshot and a primer on the state of age discrimination in America. Here’s what I’ve learned.

Ageism: An accepted bias

If you haven’t felt the pinch of ageism yet, trust us, you will. If you apply for a job online, there’s a good chance that a screening algorithm will automatically disqualify you because of your age. If you’re an older employee, it’s likely you’ll bear your share of age-related comments and jokes. And if you’re gunning for a promotion or heading into a job interview, you may feel compelled to touch up the gray, dress a bit younger and act like technology is your best friend.

That’s because ageism in the workplace occurs every day across America, and it is tolerated or — even worse — unrecognized for what it truly is: discrimination, plain and simple.

“Age discrimination is so pervasive that people don’t even recognize it’s illegal,” asserts Kristin Alden, an attorney specializing in employee rights at the Alden Law Group in Washington, D.C.

What immediately became apparent in my reporting is that, like other biases and discriminatory practices, ageism takes many forms. In the workplace, we found illegal age discrimination in three main areas:

Recruitment and hiring, when younger applicants are shown favor simply because of their age.

On-the-job bias, when older workers receive fewer training opportunities, promotions and rewards, or are harassed.

Termination, when a company “freshens” its workforce or trims budget by targeting senior employees for layoffs or encouraging them to retire.

Paul Rupert, of Respectful Exits, suggests — persuasively — that the problem emanates from our free-enterprise roots. The predominant business model in this country is still an industrial one where companies view employees as “human capital,” he says. “It’s a sad phrase, but companies view their workforce the same way they view their capital equipment. You buy it, you assume it has a certain shelf life, and then you get rid of it and replace it with a new model.”

Read the rest of the story here... 

What it boils down to is this. If a person HASN’T created a business for themselves OR made their skills so utterly indispensable by age 40 [maybe as late as age 50...

You become a statistic of society by age 50 and beyond and generally speaking get kicked to the curb. Walmart, Lowes and the big box stores can ONLY hire so many greeters.

There is a true wealth of knowledge being wasted in this country.

[mrhacks]

If last year's Tumblr Purge had actually been effective, it wouldn't have effectively gone after all the CSAM shit instead of going after artists, photographers, writers, models, actors, and their fans.

However, Verizon much like any other company invested in technology took a shotgun approach to a scalpel problem. The result is the "porn" problem got mismanaged while the investigators saw the CSAM problem get worse.

The problem is digital bias, and it's the same issue that the LGBT community made a 31 minute video on YouTube exposing how YouTube and Google screwed the pooch when it came to machine learning by hiring low-wage workers in ultra-conservative countries to make decisions on what content could be monetized.

One person's bundle of twigs was another person's terrible insult. A reminder to all you ML/AI geeks: Robots do not understand insults, sarcasm, or even bias very well. They will take everything literally.

What's more, and this is for all the conservatives out there who think "liberals are censoring conservative free speech", you would be incorrect. If a group of folks like the LGBT community, which for the most part outside of the "Log Cabin" group of conservatives is being demonetized by robots, what do you think is doing the same thing with all you stuff? The same thing.

The robots aren't "liberal" they are LITERAL.

So every vulgar Obama joke pretty much gets processed like any bad Trump meme...or so we thought.

In fact the robots are Racist AF! They learned their hatred like anybody else: a consistent diet of being exposed to bias shit.

Microsoft's Tay.ai was ruined because what started as a joke by some 4chan pranksters turned into a nightmare.

One computer scientist, Joy Buolamwini noted that because how Machine Learning has trouble with recognizing faces of people with darker complexions, the machines don't recognize them, which for her, a person of color, was quite disheartening.

Algorithmic Data Bias doesn't just discrimination against race, gender, sexual activity, but also crime statistics, justice, financial lending, disability, healthcare, immigration, even hiring decisions.

Ever get turned down for a job to find out somebody not as good at the job as you gets hired or promoted but later it's discovered the person they hired was corrupt? Imagine AI or ML doing this constantly.

The bots make the decisions, but the parameters for how those decisions are made are still set and modified by humans, many with bias opinions either by a lack of understanding or education. In some places, state-run propaganda as been applied not to apply data to a specific democracy, but to be applied universally beyond borders. We saw this with how China tried to influence Google, how Russia did it with elections in the Ukraine and the United States, how Verizon and Yahoo did it when they ran Tumblr.

By now, we should know better than to apply Algorithmic Bias in data. What may seem like a good idea for one group to squelch out their rivals and enemies overall excludes, isolates, or ostracizes ther few supporters and allies that exist in the same area.

The one effective means to defeat Algorthmic Bias is to infiltrate it.

#algorithmic bias #artificial intelligence #machine learning #tumblr purge #verizon was wrong #yahoo was wrong

[scifigeneration]

The Montréal Declaration: Why we must develop AI responsibly

by Yoshua Bengio

As AI is deployed in society, there is an impact that can be positive or negative. The future is in our hands. Shutterstock

I have been doing research on intelligence for 30 years. Like most of my colleagues, I did not get involved in the field with the aim of producing technological objects, but because I have an interest in the the abstract nature of the notion of intelligence. I wanted to understand intelligence. That’s what science is: Understanding.

However, when a group of researchers ends up understanding something new, that knowledge can be exploited for beneficial or harmful purposes.

That’s where we are — at a turning point where the science of artificial intelligence is emerging from university laboratories. For the past five or six years, large companies such as Facebook and Google have become so interested in the field that they are putting hundreds of millions of dollars on the table to buy AI firms and then develop this expertise internally.

The progression in AI has since been exponential. Businesses are very interested in using this knowledge to develop new markets and products and to improve their efficiency.

So, as AI spreads in society, there is an impact. It’s up to us to choose how things play out. The future is in our hands.

Killer robots, job losses

From the get-go, the issue that has concerned me is that of lethal autonomous weapons, also known as killer robots.

While there is a moral question because machines have no understanding of the human, psychological and moral context, there is also a security question because these weapons could destabilize the world order.

Another issue that quickly surfaced is that of job losses caused by automation. We asked the question: Why? Who are we trying to bring relief to and from what? The trucker isn’t happy on the road? He should be replaced by… nobody?

Machines have already replaced humans for many functions. How far will it go? Franck v/Unsplash, CC BY-NC

We scientists seemingly can’t do much. Market forces determine which jobs will be eliminated or those where the workload will be lessened, according to the economic efficiency of the automated replacements. But we are also citizens who can participate in a unique way in the social and political debate on these issues precisely because of our expertise.

Computer scientists are concerned with the issue of jobs. That is not because they will suffer personally. In fact, the opposite is true. But they feel they have a responsibility and they don’t want their work to potentially put millions of people on the street.

Revising the social safety net

So strong support exists, therefore, among computer scientists — especially those in AI — for a revision of the social safety net to allow for a sort of guaranteed wage, or what I would call a form of guaranteed human dignity.

The objective of technological innovation is to reduce human misery, not increase it.

It is also not meant to increase discrimination and injustice. And yet, AI can contribute to both.

Discrimination is not so much due, as we sometimes hear, to the fact AI was conceived by men because of the alarming lack of women in the technology sector. It is mostly due to AI leading on data that reflects people’s behaviour. And that behaviour is unfortunately biased.

In other words, a system that relies on data that comes from people’s behaviour will have the same biases and discrimination as the people in question. It will not be “politically correct.” It will not act according to the moral notions of society, but rather according to common denominators.

Society is discriminatory and these systems, if we’re not careful, could perpetuate or increase that discrimination.

There could also be what is called a feedback loop. For example, police forces use this kind of system to identify neighbourhoods or areas that are more at-risk. They will send in more officers… who will report more crimes. So the statistics will strengthen the biases of the system.

The good news is that research is currently being done to develop algorithms that will minimize discrimination. Governments, however, will have to bring in rules to force businesses to use these techniques.

Saving lives

There is also good news on the horizon. The medical field will be one of those most affected by AI — and it’s not just a matter of saving money.

Doctors are human and therefore make mistakes. So the more we develop systems with more data, fewer mistakes will occur. Such systems are more precise than the best doctors. They are already using these tools so they don’t miss important elements such as cancerous cells that are difficult to detect in a medical image.

The data provided by artificial intelligence will allow patients’ medical records to be interpreted much more effectively. Stephen Dawson/Unsplash, FAL

There is also the development of new medications. AI can do a better job of analyzing the vast amount of data (more than what a human would have time to digest) that has been accumulated on drugs and other molecules. We’re not there yet, but the potential is there, as is more efficient analysis of a patient’s medical file.

We are headed toward tools that will allow doctors to make links that otherwise would have been very difficult to make and will enable physicians to suggest treatments that could save lives.

The chances of the medical system being completely transformed within 10 years are very high and, obviously, the importance of this progress for everyone is enormous.

I am not concerned about job losses in the medical sector. We will always need the competence and judgment of health professionals. However, we need to strengthen social norms (laws and regulations) to allow for the protection of privacy (patients’ data should not be used against them) as well as to aggregate that data to enable AI to be used to heal more people and in better ways.

The solutions are political

Because of all these issues and others to come, the Montréal Declaration for Responsible Development of Artificial Intelligence is important. It was signed Dec. 4 at the Society for Arts and Technology in the presence of about 500 people.

It was forged on the basis of vast consensus. We consulted people on the internet and in bookstores and gathered opinion in all kinds of disciplines. Philosophers, sociologists, jurists and AI researchers took part in the process of creation, so all forms of expertise were included.

There were several versions of this declaration. The first draft was at a forum on the socially responsible development of AI organized by the Université de Montréal on Nov. 2, 2017.

That was the birthplace of the declaration.

Its goal is to establish a certain number of principles that would form the basis of the adoption of new rules and laws to ensure AI is developed in a socially responsible manner. Current laws are not always well adapted to these new situations.

And that’s where we get to politics.

The abuse of technology

Matters related to ethics or abuse of technology ultimately become political and therefore belong in the sphere of collective decisions.

How is society to be organized? That is political.

What is to be done with knowledge? That is political.

I sense a strong willingness on the part of provincial governments as well as the federal government to commit to socially responsible development.

Because Canada is a scientific leader in AI, it was one of the first countries to see all its potential and to develop a national plan. It also has the will to play the role of social leader.

Montréal has been at the forefront of this sense of awareness for the past two years. I also sense the same will in Europe, including France and Germany.

Generally speaking, scientists tend to avoid getting too involved in politics. But when there are issues that concern them and that will have a major impact on society, they must assume their responsibility and become part of the debate.

And in this debate, I have come to realize that society has given me a voice — that governments and the media were interested in what I had to say on these topics because of my role as a pioneer in the scientific development of AI.

So, for me, it is now more than a responsibility. It is my duty. I have no choice.

About The Author:

Yoshua Bengio, Professeur titulaire, Département d'informatique et de recherche opérationnelle, Université de Montréal

This article is republished from our content partners at The Conversation under a Creative Commons license. 

#technology #artificial intelligence #robots #technopolitics #killer robots #ethics #science ethics