Ethics

Training data are construction materials for a language models. A language model can never be inspired. It is itself a cultural artefact derived from other cultural artefacts.

The machine learning process is loosely based on decades-old grossly simplified models of how brains work.

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It’s important to remember this so that we don’t fall for marketing claims that constantly imply that these tools are fully functioning assistants.

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Who was the first man on the moon?

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What you’re likely to get back from that prompt would be something like:

On July 20, 1969, Neil Armstrong became the first human to step on the moon.

This is NASA’s own phrasing. Most answers on the web are likely to be variations on this, so the answer from a language model is likely to be so too.

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The prompt we provided is strongly associated in the training data set with other sentences that are all variations of NASA’s phrasing of the answer. The model won’t answer with just “Neil Armstrong” because it isn’t actually answering the question, it’s responding with the text that correlates with the question. It doesn’t “know” anything.

A core aspect of the theory-building model of software development is code that developers don’t understand is a liability. It means your mental model of the software is inaccurate which will lead you to create bugs as you modify it or add other components that interact with pieces you don’t understand.

Language model tools for software development are specifically designed to create large volumes of code that the programmer doesn’t understand. They are liability engines for all but the most experienced developer. You can’t solve this problem by having the “AI” understand the codebase and how its various components interact with each other because a language model isn’t a mind. It can’t have a mental model of anything. It only works through correlation.

For years, we studied teams rolling out new designs, to see if we could mitigate negative reaction to new releases and design changes. We studied hundreds of product and service rollouts. We watched and learned from the reactions of thousands of users.

When we dug into what those users’ reactions [were], patterns emerged. The users told us the changes inconvenienced them. They had no idea the change was coming and suddenly it was in their face. Users were upset because they were surprised.

They also told us the old version worked fine. Even when it took a while to get comfortable, they learned it. Many users mastered difficult-to-use designs.

Everything was different when the new version arrived. What they’d mastered before didn’t help them now. The company said it was an improved design, but they couldn’t see the improvements. Why should these users learn something new that doesn’t help them? Users were upset because they couldn’t see the value.

We also saw many instances where users didn’t react negatively to changes. Often, they didn’t react at all. We saw new designs that didn’t affect the users’ behaviors and they didn’t pay attention to it.

In these cases, the changes were often not noticeable. Sometimes the changes were small and isolated. Yet, we also saw users seemingly not notice several updates with extensive changes. (In more than one instance, an entire application’s infrastructure had been rewritten without a single user noticing.)

In cases when the design changes were noticeable, the designers gave the users control to switch when they wanted. The designers showed why the change was valuable to the users. And the designers made the transition easy by taking the knowledge and experience their users already had with the product into account.

Andy and I have put a lot of work and thought into Every Layout. We want it to be the best resource it can be. Naturally, we would like to be paid for the value we are giving you. However, we know that not everyone can afford the things they’d like to have. We’re trying to address this in two ways:

1. A large selection of free content, including all of the “rudiment” articles that cover the basics of (our take on) contemporary CSS
2. An honor system, wherein you can claim to be eligible for the full Every Layout for free

What makes you eligible for (2)? If you are currently out of work, you are a full-time student or under 19 years old, you are trying to get your first job as a web developer or designer, or you are an unpaid volunteer for a charitable organization not involved in proselytism: consider yourself a match. Also, if you are the sole person of your ethnicity, gender, or sexual orientation at your company or in your local developer community: we will gift Every Layout to you. If you have a disability that makes accessing equivalent resources difficult, you can have this resource for free. We are trying to make it as accessible as possible.

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It would be logistically and ethically implausible for us to vet, or otherwise judge, if you deserve to have Every Layout without a charge. If you believe you fit into the criteria above, let us know. That’s the honor part.

Are we suckers? Perhaps, to some. But while you alone decide if you qualify, we decide if you disqualify. That is, if we see you saying or sharing racist, homophobic, transphobic, sexist, or fascist sentiments, or you’re caught engaging in what we consider, in any way, punching down, you get fuck all for free from us.

Two years ago, I wrote about prefers-reduced-motion, a media query introduced into Safari 10.1 to help people with vestibular and seizure disorders use the web. The article provided some background about the media query, why it was needed, and how to work with it to avoid creating disability-triggering visual effects.

The article was informed by other people’s excellent work, namely Orde Saunders’ post about user queries, and Val Head’s article on web animation motion sensitivity.

We’re now four months into 2019, and it makes me happy to report that we have support for the feature in all major desktop browsers! Safari was first, with Firefox being a close second. Chrome was a little late to the party, but introduced it as of version 74.

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Reduce isn’t necessarily remove

We may not need to throw the baby out with the bathwater when it comes to using animation. Remember, it’s prefers-reduced-motion, not prefers-no-motion.

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If the meaning of a component is diminished by removing its animation altogether, we could slow down and simplify the component’s animation to the point where the concept can be communicated without potentially being an accessibility trigger.

There’s an age-old problem with algorithms that can learn that some advocates don’t seem to fully grasp: without human ethics built in, the potential for harm can be enormous. Isaac Asimov was onto something with the three laws of robotics.

Shooting the moon: In one of the more chilling examples, there was an algorithm that was supposed to figure out how to apply a minimum force to a plane landing on an aircraft carrier. Instead, it discovered that if it applied a *huge* force, it would overflow the program’s memory and would register instead as a very *small* force. The pilot would die but, hey, perfect score.

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Something as apparently benign as a list-sorting algorithm could also solve problems in rather innocently sinister ways.

Well, it’s not unsorted: For example, there was an algorithm that was supposed to sort a list of numbers. Instead, it learned to delete the list, so that it was no longer technically unsorted.

Solving the Kobayashi Maru test: Another algorithm was supposed to minimize the difference between its own answers and the correct answers. It found where the answers were stored and deleted them, so it would get a perfect score.

How to win at tic-tac-toe: In another beautiful example, in 1997 some programmers built algorithms that could play tic-tac-toe remotely against each other on an infinitely large board. One programmer, rather than designing their algorithm’s strategy, let it evolve its own approach. Surprisingly, the algorithm suddenly began winning all its games. It turned out that the algorithm’s strategy was to place its move very, very far away, so that when its opponent’s computer tried to simulate the new greatly-expanded board, the huge gameboard would cause it to run out of memory and crash, forfeiting the game.

It turns out that there’s an industry out there making money by algorithmically mashing up kids videos in ways to manipulate search rankings, without much apparent care about how their content could potentially traumatize some children. This is well worth a read, if only as another piece of evidence that algorithms often do not have human ethics baked in and the unintended effects this can have.

I think this is a good example of the is-ought problem in philosophy, transplanted into the world of software development:

A/B testing is a great way of finding out what happens when you introduce a change. But it can’t tell you why.

I used the site earlier in the year, and actually felt my heart rate increase. Never again.
— Paul Robert Lloyd (@paulrobertlloyd) October 24, 2017

The problem is that, in a data-driven environment, decisions ultimately come down to whether something works or not. But just because something works, doesn’t mean it’s a good thing.

If I were trying to convince you to buy a product, or use a service, one way I could accomplish that would be to literally put a gun to your head. It would work. Except it’s not exactly a good solution, is it? But if we were to judge by the numbers (100% of people threatened with a gun did what we wanted), it would appear to be the right solution.

Dark Patterns are tricks used in websites and apps that make you buy or sign up for things that you didn’t mean to. The purpose of this site is to spread awareness and to shame companies that use them.

Relevant points [of disabling Disqus] are:

• Load-time goes from 6 seconds to 2 seconds.
• There are 105 network requests vs. 16.
• There are a lot of non-relevant requests going through to networks that will be tracking your movements.

Among the networks you can find:

• disqus.com - Obviously!
• google-analytics.com - Multiple requests; no idea who’s capturing your movements.
• connect.facebook.net - If you’re logged into Facebook, they know you visit this site.
• accounts.google.com - Google will also map your visits to this site with any of your Google accounts.
• pippio.com - LiveRamp identify mapping for harvesting your details for commercial gain.
• bluekai.com - Identity tracking for marketing campaigns.
• crwdcntrl.net - Pretty suspect site listed as referenced by viruses and spyware.
• exelator.com - More identity and movement tracking site which even has a virus named after it!
• doubleclick.net - We all know this one: ad services and movement tracking, owned by Google.
• tag.apxlv.net - Very shady and tricky to pin-point an owner as they obsfuscate their domain (I didn’t even know this was a thing!). Adds a tracking pixel to your site.
• adnxs.com - More tracking garbage, albeit slightly more prolific.
• adsymptotic.com - Advertising and tracking that suppposedly uses machine learning.
• rlcdn.com - Obsfuscated advertising/tracking from Rapleaf.
• adbrn.com - “Deliver a personalized customer journey across devices, channels and platforms with Adbrain customer ID mapping technology.”
• nexac.com - Oracle’s Datalogix, their own tracking and behavioural pattern rubbish.
• tapad.com - OK, I cant’t be bothered to search to look this up anymore.
• liadm.com - More? Oh, ok, then…
• sohern.com - Yup. Tracking.
• demdex.net - Tracking. From Adobe.
• bidswitch.net - I’ll give you one guess…
• agkn.com - …
• mathtag.com - Curious name, maybe it’s… no. It’s tracking you.

I can’t visit many of these sites because I have them blocked in uBlock Origin so information was gleaned from google crawl results of the webpages and 3rd parties. Needless to say, it’s a pretty disgusting insight into how certain free products turn you into the product. What’s more worrying are the services that go to lengths to hide who they are and what their purposes are for tracking your movements.