How ‘Analog Privilege’ Spares Elites From The Downsides Of Flawed AI Decision-Making Systems
from the that’s-not-fair dept
We live in a world where there are often both analog and digital versions of a product. For example, we can buy books or ebooks, and choose to listen to music on vinyl or via streaming services. The fact that digital goods can be copied endlessly and perfectly, while analog ones can’t, has led some people to prefer the latter for what often amounts to little more than snobbery. But a fascinating Tech Policy Press article by Maroussia Lévesque points out that in the face of increased AI decision-making, there are very real advantages to going analog:
The idea of “analog privilege” describes how people at the apex of the social order secure manual overrides from ill-fitting, mass-produced AI products and services. Instead of dealing with one-size-fits-all AI systems, they mobilize their economic or social capital to get special personalized treatment. In the register of tailor-made clothes and ordering off menu, analog privilege spares elites from the reductive, deterministic and simplistic downsides of AI systems.
One example given by Lévesque concerns the use of AI by businesses to choose new employees, monitor them as they work, and pass judgement on their performance. As she points out:
Analog privilege begins before high-ranking employees even start working, at the hiring stage. Recruitment likely occurs through headhunting and personalized contacts, as opposed to applicant tracking systems automatically sorting out through resumes. The vast majority of people have to jump automated one-size-fits all hoops just to get into the door, whereas candidates for positions at the highest echelons are ushered through a discretionary and flexible process.
Another example in the article involves the whitelisting of material on social networks when it is posted by high-profile users. Masnick’s Impossibility Theorem pointed out five years ago that moderation at scale is impossible to do well. One response to that problem has been the increasing use of AI to make quick but often questionable judgements about what is and isn’t acceptable online. This, in its turn, has led to another kind of analog privilege:
In light of AI’s limitations, platforms give prominent users special analog treatment to avoid the mistakes of crude automated content moderation. Meta’s cross-check program adds up to four layers of human review for content violation detection to shield high-profile users from inadvertent enforcement. For a fraction of one percent of users, the platform dedicates special human reviewers to tailor moderation decisions to each individual context. Moreover, the content stays up pending review.
In terms of addressing analog privilege, wherever it may be found, Lévesque suggests that creating a “right to be an exception” might be a good start. But she also notes that implementing such a right in AI laws won’t be enough, and that the creators of AI systems need to improve “intelligibility so people subject to AI systems can actually understand and contest decisions.” More generally:
looking at analog privilege and the detrimental effects of AI systems side by side fosters a better understanding of AI’s social impacts. Zooming out from AI harms and contrasting them with corresponding analog privileges makes legible a subtle permutation of longstanding patterns of exceptionalism. More importantly, putting the spotlight on privilege provides a valuable opportunity to interrupt unearned advantages and replace them with equitable, reasoned approaches to determining who should be subject to or exempt from AI systems.
Well, we can dream, can’t we?
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Filed Under: ai, analog, decision making, digital, elites, exceptions, masnick's impossibility theorem, moderation, privilege, recruitment, social media
Companies: meta
Comments on “How ‘Analog Privilege’ Spares Elites From The Downsides Of Flawed AI Decision-Making Systems”
And we’ll also have a fight to avoid “AI”-driven personal possessions, which will probably be as easy as avoiding surveillance-orientated personal possessions.
“The fact that digital goods can be copied endlessly and perfectly, while analog ones can’t, has led some people to prefer the latter for what often amounts to little more than snobbery.”
An LP (vinyl) in good condition usually sounds better (on a good system) than a file played on an mp3 player, on a laptop or from a stream. The digital to analog converter can make or break the sound quality and compression can cause degradation of same.
Making your own digital copy from a good LP, using a good analog to digital converter, can result in a good quality digital copy that is not compressed and does not have ‘loudness’ added to enhance the bass.
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Bad compression was a big problem in the Napster days, and not so much since. Lossless files with precise-ripping-logs are readily available now; as are well-encoded high-bitrate MP3 files, which people mostly cannot differentiate from lossless in blind testing.
I’m not sure bad digital-to-analog converters are much of a problem, either. If your computer’s or phone’s is bad, you can get a high-quality USB DAC (also, some high-end motherboards and PCIe sound cards have user-replaceable op-amps).
All else being equal, whether CDs or vinyl records sound better is mostly a matter of opinion. The big difference, probably, is that because vinyl is now targeted at “snobs”, “all else being equal” might not apply: the vinyl albums may be better mastered than CDs, sidestepping the “loudness war” problems.
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FYI, vinyl tends to actually give poorer quality sound compared to other formats. To get the rich warm sounds of vinyl without the distortion, I recommend ripping from a CD, then adding the necessary effects to the resulting WAV or FLAC file in Audacity before exporting the result as a VBR MP3.
How ‘Analog Privilege’ Spares Elites From The Downsides Of Flawed AI Decision-Making Systems
This is a really interesting take on “analog privilege” and how it plays into the limitations of AI systems. From my experience, AI tools, like chatbots, can offer personalized interactions for many users, but there’s definitely a difference in how these systems handle high-profile users versus the general public. For businesses, using AI-powered chatbots can streamline customer service, but it’s important to ensure the system is adaptable and not too rigid, so it doesn’t feel impersonal or unfair. Finding a balance between automation and the human touch, even within chatbot systems, is key to providing a better experience for everyone, not just those with “analog privilege.” Anyone else notice how this privilege shows up in AI-driven customer service?
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Several chatbots I have encountered on business websites were rather useless, there was one that actually helped but usually a phone call or email is required to straighten w=everything out.
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Sure, if you’re using them as intended. The key is to manipulate them into making promises that benefit you. Record your session, and hold the company to those promises.
For what it’s worth, I’ve gotten bullshit copy-and-paste responses via e-mail on several occasions. Like, an actual person saw a keyword and pasted some superficially-relevant response, obviously without reading my message at all.
Precision versus Accuracy
A pet peeve of mine, especially with the show “how it’s made,” is the confusion between accuracy and precision. The sentence in the article below really gets to the heart of precision.
Now that is precision. Each copy is an exact rendering of the other copies.
In some cases, this does affect the accuracy, however. Every copy may be exact, but there will be some effect when an analog sample is digitized. Computing power is enabling resolution to improve continually to the point that preference for analog audio can be a matter of snobbery, especially if riding in a moving vehicle. Until recently, I had dismissed the analog preference for audio as all snobbery. I cannot dismiss the difference between my ICOM 7300 (a pretty good shortwave receiver) versus a 1950s R-390A. Especially with rough conditions or weak signals, the 390A hears messages that the ICOM completely misses. The 390A is more accurate.
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It is snobbery if you are a snob.
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The Nyquist theorem says that, with a sufficiently high sampling rate (and good equipment), a signal can be reconstructed exactly. And actual listening tests tend to support this; people may be able to tell vinyl from CD, but that’s because of other effects. When hearing a vinyl record directly from a record-player, or a good digital rip sourced from the same record-player, people can’t tell them apart.
Digital systems tend to be poorly optimized for noisy signals, but they don’t have to be. Also, you’re comparing an “entry-level” receiver to one “widely considered an example of the best of vacuum tube technology”. A quick search shows that the R-390A sold for $2,210 in 1951, equivalent to roughly $26,600 today (or more like $14,000, inflation-adjusted, if purchased in the 1970s). And the 7300 sells for about $1,200 today.
“Now that is precision. Each copy is an exact rendering of the other copies.”
If you copy a bad digitization, you have an exact copy of a bad digitization.