After four weeks of watching other teams do amazing jobs on their debates, it was great to finally have the chance to step up to the plate. What was the topic of the debate I took part in? What side was I on? Oh, I know you, the millions of my readers, are dying to know 😉
But, for the sake of hoping suspense will keep you reading… here are a few images and text citations to mull over while you try to figure out what the final debate of this class was all about. 🙂
In the image below, who has the best access to the technology that will give them the best opportunities for growth?
“The major driving force behind the changes in the U.S. wage structure is technology. This consensus is built on the notion of technology-skill complementarity: technical change favors more skilled (educated) workers, replaces tasks previously performed by the unskilled, and increases the demand for skills. Consequently, many commentators see a direct causal relationship between technological changes and these radical shifts in the distribution of wages taking place in the U.S. economy.” text source
We talk about the internet opening up opportunities for everyone in the world….
Is it that simple?
And what comes with the “wealth” of information, resources, opportunities that our current technology embodies?
Even here in Canada, a so-called “first-world nation,” are we all talking about the same people when we describe how technology in the classroom enhances learning, etc?
“Canada has a digital divide, a demographic that isn’t fully connected to the online world. In the past year, 20 per cent of Canadians haven’t used the internet once, from any location. And that number doesn’t include other kinds of disconnection, like those who don’t own a computer or cell phone, or who can’t use them effectively.” Text source
[W]hile 95 per cent of Canadians in the highest income quartile are connected, just 62 per cent in the lowest income quartile have internet access.
There is also a still a pronounced divide between access for those in urban centres versus rural and remote households. Broadband is available to 100 per cent of Canadians in urban areas, compared to 85 per cent in rural areas. Text source
Besides access to the internet, what are other differences between the have- and have-nots of technology?
“Systems such as the eligibility registration, homeless registry, child abuse and neglect algorithms have assumptions embedded in them.”
“These algorithms affect all of us, but they don’t affect all of us equally.” Text source
And if your skin isn’t white….
“More than 99 percent of the time, the systems correctly identified a lighter-skinned man. On the photographs of black women, the algorithm made mistakes nearly 34 percent of the time. And the darker the skin, the worse the programs performed, with error rates hovering around 47 percent – the equivalent of a coin toss. The systems didn’t know a black woman when they saw one.” Text source
“[Word embedding], which is already used in web search and machine translation, works by building up a mathematical representation of language, in which the meaning of a word is distilled into a series of numbers (known as a word vector) based on which other words most frequently appear alongside it. Perhaps surprisingly, this purely statistical approach appears to capture the rich cultural and social context of what a word means in the way that a dictionary definition would be incapable of. For instance, in the mathematical “language space”, words for flowers are clustered closer to words linked to pleasantness, while words for insects are closer to words linked to unpleasantness, reflecting common views on the relative merits of insects versus flowers. And the AI system was more likely to associate European American names with pleasant words such as “gift” or “happy”, while African American names were more commonly associated with unpleasant words.” Text source
… and if you’re not a man…
“Anyone who is surprised by the recent revelations of sexism spreading like wildfire through the technology industry has not been paying attention.”
“These models [are] based on the premise that words which appear near each other in texts share meaning. These spatial relationships are used in natural language-processing so that computers can engage with us conversationally. By reading a lot of text, a computer can learn that Paris is to France as Tokyo is to Japan. It develops a dictionary by association.”
“But this can create problems when the world is not exactly as it ought to be. For instance, researchers have experimented with one of these word-embedding models, Word2vec, a popular and freely available model trained on three million words from Google News. They found that it produces highly gendered analogies. For instance, when asked “Man is to woman as computer programmer is to ?”, the model will answer “homemaker”. Or for “father is to mother as doctor is to ?”, the answer is “nurse”. Of course the model reflects a certain reality: it is true that there are more male computer programmers, and nurses are more often women. But this bias, reflecting social discrimination, will now be reproduced and reinforced when we engage with computers using natural language that relies on Word2vec. It is not hard to imagine how this model could also be racially biased, or biased against other groups.” Text source
So! What was the debate topic that I signed up for?
Technology is a force for equity in society. DISAGREE!
My worthy opponents, Jen, Dawn, and Sapna, put up a good fight. It wasn’t their fault that they chose the difficult side to fight 😉
(okay, truth-be-told, according to our pre- and post-debate polls, 66% of my classmates believed that technology is a force for equity in society. Sigh). 🙂
In their video, team agree claim that “technology has removed many of the barriers that people have faced in the past, for example, not being able to read.” While I don’t believe that this is actually the case for the vast majority of people who face the struggle with illiteracy every day, I do agree with them that technology has “connected the world in ways previously unimaginable.” I also agree with them that technology offers people “classroom opportunities, community opportunities, personal opportunities.” Yes, this is true. For many people. Assistive technology tools can also, as they state, help students who “struggle with reading and writing at grade level.” I also agree that “children are growing up in a world where social media, mobile technology, and online communities are fundamental to the way they communicate, learn, and develop.”
Yes, tech can do great things.
But is it a force for equity in society?
As a member of the agree team said, “more than 4 billion of us now have access to the internet.” What about the other half of the world’s population?
In her blog, one of the agree team members, Dawn, wrote that
“Fewer people in poor countries than in rich ones own computers and have access to the internet simply because they are too poor, are illiterate, or have other more pressing concerns, such as food, health care and security. So even if it were possible to wave a magic wand and cause a computer to appear in every household on earth, it would not achieve very much: a computer is not useful if you have no food or electricity and cannot read.”
… and I think that this hits the nail on the head. Technology on its own is not the solution to the world’s problems any more than it is a basic necessity of survival.
Yes, technology can do amazing things to help all kinds of people – rich, poor; educated, uneducated; urban, rural; black, white; female, male… but it can only do what we, the people who create it, allow it to do. I’ve said I’m a pessimist before, but I’m more than that, to be honest… I’m also a bit of a nihilist, and a slight misanthrope. I don’t see that anything we create technology-wise is going to quickly fill in the major gaps we have in our moral “coding.” Ethically, we are still the same people we were before a few of us invented of this current technology. As a species, we will continue to be more selfish than selfless, more nearsighted than farsighted, more biased than unbiased, etc, etc. No amount of scientific or technological innovation will get us any further along in these regards.
I think that the credit for “my favourite quotation from the readings I’ve done for this debate” goes to Joanna Bryson, a computer scientist at the University of Bath (and hoot-hoot! She’s a female computer scientist, to boot!):
“A lot of people are saying this is showing that AI is prejudiced. No. This is showing we’re prejudiced and that AI is learning it.”
I think Rakan and I gave it our all for this project, and I’m pleased with how much ground we covered. I feel we had some good content in our debate. Of course there are so many other points we wanted to make, but a five-minute time limit proved to be our number one challenge to overcome. (We hope Alec didn’t notice that our vid actually went to five minutes and thirty-three seconds… oops. 🙂 )
If any of my millions of followers are still with me at this point and would like to see our debate video, here it is.
This brings me to the end of my final blog post for this class, apart from the upcoming Summary of Learning. It’s been a blast. 🙂