battle of ideas

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This weekend the Barbican Centre was home of the Battle of Ideas 2016. Talks by relevant people of many areas, followed by debate and questions posed by the audience. Great stuff to Library and Information Science to reflect about, as this edition of the event brought some amazing people to talk about technology and ethics, millenials and education, science and society. Here are issues raised by panelists, audience, and me on five of the debates I attended that should interest Library and Information people—and all the other humans that like to think about the world we are living in, really.

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Big Data: Does Size Matter?

I have to say this one was my favourite. First, Timandra Harkness talked about her book, which borrowed its name to the session: in it she makes her point that maybe the size of big data doesn’t matter that much; what matters most is that now loads of different types of data can be put together very easily, and then say something new. Isn’t that great? Well, it can be great, but all this data put together can also be used to make predictions and detect trends that are actually based purely on the past (data and statistics)—and doesn’t this practice reinforce all sorts of prejudices and misinterpretations from the present (e.g. identifying a black young male as a “high risk person” when it comes to predictions about committing crimes)? Timandra sees a danger in there, and so do I.

Some of the other issues discussed (I can’t claim to be quoting people precisely as I wasn’t recording, but I’m pretty confident I got the meanings quite right):

Timandra Harkness:
• It’s default now to collect data, and it happens so seamlessly; you don’t need to know how it works!
• Aren’t we willing to trust each other anymore, so we prefer to outsource decisions to machines?

Zulfikar Abbany:
• Machines don’t make ‘neutral’ decisions. Technology is not neutral! Computers are binary, while humans are full of grey areas in their decision-making process… We don’t function on a yes/no, on/off, up/down way.
• Yes, we have an obsession with numbers, but what do they actually mean?

Will Moy:
• Big data and algorithms are all about redistribution of power; there is a power shift going on right now, and every time it happens in society it’s very significant.
• Algorithms are not transparent; we need an ethics and politics of algorithms.

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What Should Post-Millenials Know?

I had mixed feelings about this session: even though it ended up not being about what I thought it would be (and what was announced it would be, really), I enjoyed the diversion, and very good talks came out of it.

In the event brochure that was handed out, this absolutely intriguing question appears as an issue to be discussed on this session: Are there particular insights or ideas that we have a responsibility to pass on, or should we trust the kids to Google the answers? I don’t think the panelists touched on this, and very little was said about technology in education and learning, and its relationship with knowledge. Anyway, the three speakers were pretty awesome; broader issues related to education were raised, including: Do school years do any good to children, and if they don’t, should we just start ditching school, as people have been ditching universities? But if they do, then what is it? Isn’t the school an ideal place for generational transactions between teachers and students? And a very precise observation by Dr Jennie Bristow: If you teach history properly, you are teaching critical thinking. Ah, loved this one! My Twitter did, too.

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Teaching to the text: Textbooks or Technology?

Apparently, there has been a comeback of textbooks as medium for learning in schools, and these are new textbooks being written and produced more carefully than the ones that have been around for the last decades (regarded as very low-quality material, really). This comeback raises several questions related to teaching & learning methods, and this very interesting panel addressed some of them:

• Why there are so many terrible textbooks around in the UK? What can we learn from the notably good textbooks from places like Hong Kong, Singapore, and Finland? What makes a good textbook?

• Why did some attempts to include digital tools in learning, such as one-iPad-per-student practices, fail to deliver positive results in schools? One possible answer, as Colin Hughes suggested, is that students don’t want to study on the same device that they use for entertainment and to interact with friends. They don’t want to mix the media;

• We must identify what textbooks are particularly good at, just as what technology is particularly good at, when it comes to classroom activities; each medium has its strengths, and it seems that the best is to combine both textbook & technology;

• If the (bad) quality of recent textbooks have pushed them out of the classrooms, and if we agree that good textbooks are the answer for the many current education problems, then what next? The debate pointed to the important role of publishers and teachers to the production of successful, great quality teaching material;

• Is technology in the classroom engaging or distracting?

• And a last, short one: does Google teach?

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Have we lost the art of conversation?

This was a quick and very dynamic, conversation-like session; panelists presented quite different views about what makes a good conversation and more:

Eliza Filby:
• We should talk about the death of listening: everyone has a voice these days, but no one seems to have ears. So how can we even have a conversation? Does it reflect the individualism of our times?

Julia Hobsbawm:
• Conversation is supposed to be intimate. If we regard it as something intimate, we can understand that technology can’t fully succeed in permitting conversation between people;
• The scale & speed of things in our world today don’t match with the ideal conditions for conversations;
• Oversharing is different from intimacy;
• Can you talk to anyone?

Richard Mason:
• We may feel connected, but we are really deeply lonely;
• Social media puts a lot of pressure on people about what to say, what to share; this constant fear is a challenge to establishing conversations;

And just a couple more observations:

• Can you recall a very good Whatsapp conversation? What about an in-person very good conversation?
• What makes a good conversation, the exchanging of information or the intimacy achieved? Does the content really matter, or is it more about reciprocity, listening and understanding?

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Why, Robot? Can we teach A.I. to be ethical?

Driverless cars are here, and the things around us are getting more intelligent everyday. With the pervasiveness of the Internet of Things and the Internet of Us, it’s past time we worry about all the ethical issues Artificial Intelligence raises.

Trying to keep it brief, I would like just to point out some of the comments made by Daniel Glaser. One issue that really intrigued me was his observation about the lack of diversity in the Silicon Valley workforce as a source of ethical concern. If only white young males are designing, configuring, and applying algorithms, we can expect quite misogynist digital technology solutions to be on offer for us. They think that a computer winning a chess game is the ultimate intellectually-complex accomplishment to a machine; and what about having to dress two kids up, serve them breakfast on time to go to school, while thinking about the shopping list and that new project going on at the company, wouldn’t that be not only more complex, but also more human, more useful intelligent task for a computer to be able to sort out?

Glaser also suggested that while ethical concerns come after the design of digital technologies, we won’t be able to come up with genuinely intelligent machines. We must create an ethical way of building intelligence, and not creating intelligence and then trying to impose some sort of ethics in it. When the first layer—the ethical one—is solved, then robots can maybe be smarter than they are today.

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Reference

big-data

Harkness, T. Big Data: Does Size Matter? Bloomsbury, London, 2016.

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