Journal tags: humans

My phone rang today. I didn’t recognise the number so although I pressed the big button to answer the call, I didn’t say anything.

I didn’t say anything because usually when I get a call from a number I don’t know, it’s some automated spam. If I say nothing, the spam voice doesn’t activate.

But sometimes it’s not a spam call. Sometimes after a few seconds of silence a human at the other end of the call will say “Hello?” in an uncertain tone. That’s the point when I respond with a cheery “Hello!” of my own and feel bad for making this person endure those awkward seconds of silence.

Those spam calls have made me so suspicious that real people end up paying the price. False positives caught in my spam-detection filter.

Now it’s happening on the web.

I wrote about how Google search, Bing, and Mozilla Developer network are squandering trust:

Trust is a precious commodity. It takes a long time to build trust. It takes a short time to destroy it.

But it’s not just limited to specific companies. I’ve noticed more and more suspicion related to any online activity.

I’ve seen members of a community site jump to the conclusion that a new member’s pattern of behaviour was a sure sign that this was a spambot. But it could just as easily have been the behaviour of someone who isn’t neurotypical or who doesn’t speak English as their first language.

Jessica was looking at some pictures on an AirBnB listing recently and found herself examining some photos that seemed a little too good to be true, questioning whether they were in fact output by some generative tool.

Every email that lands in my inbox is like a little mini Turing test. Did a human write this?

Our guard is up. Our filters are activated. Our default mode is suspicion.

This is most apparent with web search. We’ve always needed to filter search results through our own personal lenses, but now it’s like playing whack-a-mole. First we have to find workarounds for avoiding slop, and then when we click through to a web page, we have to evaluate whether’s it’s been generated by some SEO spammer making full use of the new breed of content-production tools.

There’s been a lot of hand-wringing about how this could spell doom for the web. I don’t think that’s necessarily true. It might well spell doom for web search, but I’m okay with that.

Back before its enshittification—an enshittification that started even before all the recent AI slop—Google solved the problem of accurate web searching with its PageRank algorithm. Before that, the only way to get to trusted information was to rely on humans.

Humans made directories like Yahoo! or DMOZ where they categorised links. Humans wrote blog posts where they linked to something that they, a human, vouched for as being genuinely interesting.

There was life before Google search. There will be life after Google search.

Look, there’s even a new directory devoted to cataloging blogs: websites made by humans. Life finds a way.

All of the spam and slop that’s making us so suspicious may end up giving us a new appreciation for human curation.

It wouldn’t be a straightforward transition to move away from search. It would be uncomfortable. It would require behaviour change. People don’t like change. But when needs must, people adapt.

The first bit of behaviour change might be a rediscovery of bookmarks. It used to be that when you found a source you trusted, you bookmarked it. Browsers still have bookmarking functionality but most people rely on search. Maybe it’s time for a bookmarking revival.

A step up from that would be using a feed reader. In many ways, a feed reader is a collection of bookmarks, but all of the bookmarks get polled regularly to see if there are any updates. I love using my feed reader. Everything I’ve subscribed to in there is made by humans.

The ultimate bookmark is an icon on the homescreen of your phone or in the dock of your desktop device. A human source you trust so much that you want it to be as accessible as any app.

Right now the discovery mechanism for that is woeful. I really want that to change. I want a web that empowers people to connect with other people they trust, without any intermediary gatekeepers.

The evangelists of large language models (who may coincidentally have invested heavily in the technology) like to proclaim that a slop-filled future is inevitable, as though we have no choice, as though we must simply accept enshittification as though it were a force of nature.

But we can always walk away.

The last talk at the last dConstruct was by local clever clogs Anil Seth. It was called Your Brain Hallucinates Your Conscious Reality. It’s well worth a listen.

Anil covers a lot of the same ground in his excellent book, Being You. He describes a model of consciousness that inverts our intuitive understanding.

We tend to think of our day-to-day reality in a fairly mechanical cybernetic manner; we receive inputs through our senses and then make decisions about reality informed by those inputs.

As another former dConstruct speaker, Adam Buxton, puts it in his interview with Anil, it feels like that old Beano cartoon, the Numskulls, with little decision-making homonculi inside our head.

But Anil posits that it works the other way around. We make a best guess of what the current state of reality is, and then we receive inputs from our senses, and then we adjust our model accordingly. There’s still a feedback loop, but cause and effect are flipped. First we predict or guess what’s happening, then we receive information. Rinse and repeat.

The book goes further and applies this to our very sense of self. We make a best guess of our sense of self and then adjust that model constantly based on our experiences.

There’s a natural tendency for us to balk at this proposition because it doesn’t seem rational. The rational model would be to make informed calculations based on available data …like computers do.

Maybe that’s what sets us apart from computers. Computers can make decisions based on data. But we can make guesses.

Enter machine learning and large language models. Now, for the first time, it appears that computers can make guesses.

The guess-making is not at all like what our brains do—large language models require enormous amounts of inputs before they can make a single guess—but still, this should be the breakthrough to be shouted from the rooftops: we’ve taught machines how to guess!

And yet. Almost every breathless press release touting some revitalised service that uses AI talks instead about accuracy. It would be far more honest to tout the really exceptional new feature: imagination.

Using AI, we will guess who should get a mortgage.

Using AI, we will guess who should get hired.

Using AI, we will guess who should get a strict prison sentence.

Reframed like that, it’s easy to see why technologists want to bury the lede.

Alas, this means that large language models are being put to use for exactly the wrong kind of scenarios.

(This, by the way, is also true of immersive “virtual reality” environments. Instead of trying to accurately recreate real-world places like meeting rooms, we should be leaning into the hallucinatory power of a technology that can generate dream-like situations where the pleasure comes from relinquishing control.)

Take search engines. They’re based entirely on trust and accuracy. Introducing a chatbot that confidentally conflates truth and fiction doesn’t bode well for the long-term reputation of that service.

But what if this is an interface problem?

Currently facts and guesses are presented with equal confidence, hence the accurate descriptions of the outputs as bullshit or mansplaining as a service.

What if the more fanciful guesses were marked as such?

As it is, there’s a “temperature” control that can be adjusted when generating these outputs; the more the dial is cranked, the further the outputs will stray from the safest predictions. What if that could be reflected in the output?

I don’t know what that would look like. It could be typographic—some markers to indicate which bits should be taken with pinches of salt. Or it could be through content design—phrases like “Perhaps…”, “Maybe…” or “It’s possible but unlikely that…”

I’m sure you’ve seen the outputs when people request that ChatGPT write their biography. Perfectly accurate statements are generated side-by-side with complete fabrications. This reinforces our scepticism of these tools. But imagine how differently the fabrications would read if they were preceded by some simple caveats.

A little bit of programmed humility could go a long way.

Right now, these chatbots are attempting to appear seamless. If 80% or 90% of their output is accurate, then blustering through the other 10% or 20% should be fine, right? But I think the experience for the end user would be immensely more empowering if these chatbots were designed seamfully. Expose the wires. Show the workings-out.

Mind you, that only works if there is some way to distinguish between fact and fabrication. If there’s no way to tell how much guessing is happening, then that’s a major problem. If you can’t tell me whether something is 50% true or 75% true or 25% true, then the only rational response is to treat the entire output as suspect.

I think there’s a fundamental misunderstanding behind the design of these chatbots that goes all the way back to the Turing test. There’s this idea that the way to make a chatbot believable and trustworthy is to make it appear human, attempting to hide the gears of the machine. But the real way to gain trust is through honesty.

I want a machine to tell me when it’s guessing. That won’t make me trust it less. Quite the opposite.

After all, to guess is human.