Tag Archives: technology

The Political Brinkmanship of Spam

I am never a fan of spam. But this is particularly true when there is an upcoming election. The level of spam I have been wading through seems to have doubled lately. We just had a provincial election here in British Columbia and all parties pulled out all stops, which included, but was not limited to; email, social media posts, robotexts and robocalls.

In Canada and the US, political campaigns are not subject to phone and text spam control laws such as our Canadian Do Not Call List legislation. There seems to be a little more restriction on email spam. A report from Nationalsecuritynews.com this past May warned that Americans would be subjected to over 16 billion political robocalls. That is a ton of spam.

During this past campaign here in B.C., I noticed that I do not respond to all spam with equal abhorrence. Ironically, the spam channels with the loosest restrictions are the ones that frustrate me the most.

There are places – like email – where I expect spam. It’s part of the rules of engagement. But there are other places where spam sneaks through and seems a greater intrusion on me. In these channels, I tend to have a more visceral reaction to spam. I get both frustrated and angry when I have to respond to an unwanted text or phone call. But with email spam, I just filter and delete without feeling like I was duped.

Why don’t we deal with all spam – no matter the channel – the same? Why do some forms of spam make us more irritated than others? It’s almost like we’ve developed a spam algorithm that dictates how irritated we get when we deal with spam.

According to an article in Scientific American, the answer might be in how the brain marshalls its own resources.

When it comes to capacity, the brain is remarkably protective. It usually defaults to the most efficient path. It likes to glide on autopilot, relying on instinct, habit and beliefs. All these things use much less cognitive energy than deliberate thinking. That’s probably why “mindfulness” is the most often quoted but least often used meme in the world today.

The resource we’re working with here is attention. Limited by the capacity of our working memory, attention is a spotlight we must use sparingly. Our working memory is only capable of handling a few discrete pieces of information at a time. Recent research suggests the limit may be around 3 to 5 “chunks” of information, and that research was done on young adults. Like most things with our brains, the capacity probably diminishes with age. Therefore, the brain is very stingy with attention. 

I think spam that somehow gets past our first line of defence – the feeling that we’re in control of filtering – makes us angry. We have been tricked into paying attention to something that was unsuspected. It becomes a control issue. In an information environment where we feel we have more control, we probably have less of a visceral response to spam. This would be true for email, where a quick scan of the items in our inbox is probably enough to filter out the spam. The amount of attention that gets hijacked by spam is minimal.

But when spam launches a sneak attack and demands a swing of attention that is beyond our control, that’s a different matter. We operate with a different mental modality when we answer a phone or respond to a text. Unlike email, we expect those channels to be relatively spam-free, or at least they are until an election campaign comes around. We go in with our spam defences down and then our brain is tricked into spending energy to focus on spurious messaging.

How does the brain conserve energy? It uses emotions. We get irritated when something commandeers our attention. The more unexpected the diversion, the greater the irritation.  Conversely, there is the equivalent of junk food for the brain – input that requires almost no thought but turns on the dopamine tap and becomes addictive. Social media is notorious for this.

This battle for our attention has been escalating for the past two decades. As we try to protect ourselves from spam with more powerful filters, those that spread spam try to find new ways to get past those filters. The reason political messaging was exempt from spam control legislation was that democracies need a well-informed electorate and during election campaigns, political parties should be able to send out accurate information about their platforms and positions.

That was the theory, anyway.

The Adoption of A.I.

Recently, I was talking to a reporter about AI. She was working on a piece about what Apple’s integration of AI into the latest iOS (cleverly named Apple Intelligence) would mean for its adoption by users. Right at the beginning, she asked me this question, “What previous examples of human adoption of tech products or innovations might be able to tell us about how we will fit (or not fit) AI into our daily lives?”

That’s a big question. An existential question, even. Luckily, she gave me some advance warning, so I had a chance to think about it.  Even with the heads up, my answer was still well short of anything resembling helpfulness. It was, “I don’t think we’ve ever dealt with something quite like this. So, we’ll see.”

Incisive? Brilliant? Erudite? No, no and no.

But honest? I believe so.

When we think in terms of technology adoption, it usually falls into two categories: continuous and discontinuous. Continuous innovation simply builds on something we already understand. It’s adoption that follows a straight line, with little risk involved and little effort required. It’s driving a car with a little more horsepower, or getting a smartphone with more storage.

Discontinuous innovation is a different beast. It’s an innovation that displaces what went before it. In terms of user experience, it’s a blank slate, so it requires effort and a tolerance for risk to adopt it. This is the type of innovation that is adopted on a bell curve, first identified by American sociologist Everett Rogers in 1962. The acceptance of these new technologies spreads along a timeline defined by the personalities of the marketplace. Some are the type to try every new gadget, and some hang on to the tried and true for as long as they possibly can. Most of us fall somewhere in between.

As an example, think about going from driving a tradition car to an electric vehicle. The change from one to the other requires some effort. There’s a learning curve involved. There’s also risk. We have no baseline of experience to measure against. Some will be ahead of the curve and adopt early. Some will drive their gas clunker until it falls apart.

Falling into this second category of discontinuous innovation, but different by virtue of both the nature of the new technology and the impact it wields, are a handful of innovations that usher in a completely different paradigm. Think of the introduction of electrical power distribution in the late 19th century, the introduction of computers in the second half of the 20th century, or the spread of the internet in the 21st Century.

Each of these was foundational, in that they sparked an explosion of innovation that wouldn’t have been possible if it were not for the initial innovation. These innovations not only change all the rules, they change the very game itself. And because of that, they impact society at a fundamental level. When these types of innovations come along, your life will change whether you choose to adopt the technology or not. And it’s these types of technological paradigm shifts that are rife with unintended consequences.

If I was trying to find a parallel for what AI means for us, I would look for it amongst these examples. And that presents a problem when we pull out our crystal ball and try to peer ahead at what might be. We can’t know. There’s just too much in flux – too many variables to compute with any accuracy. Perhaps we can project forward a few months or a year at the most, based on what we know today. But trying to peer any further forward is a fool’s game. Could you have anticipated what we would be doing on the Internet in 2024 when the first BBS (Bulletin Board System) was introduced in Chicago in 1978?

A.I. is like these previous examples, but it’s also different in one fundamental way. All these other innovations had humans at the switch. Someone needed to turn on the electrical light, boot up the computer or log on to the internet. At this point, we are still “using” A.I., whether it’s as an add-on in software we’re familiar with, like Adobe Photoshop, or a stand-alone app like ChatGPT, but generative A.I.’s real potential can only be discovered when it slips from the grasp of human control and starts working on its own, hidden under some algorithmic hood, safe from our meddling human hands.

We’ve never dealt with anything like this before. So, like I said, we’ll see.