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Ethics in AI | SDL4 A3.1 |

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Big Data and AI | SDL4 A3.1 |
1
The Ethics of AI
PREVIEW7m 33s
The Ethics of AI
Overview
Difficulty
Beginner
Duration
8m
Students
69
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Description

Artificial Intelligence has opened the door to a whole world of new possibilities and ways of doing things. However, there are some questions around the ethics of AI practices. In this video, you'll learn more about some of these questions.

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Transcript

Let's now then have a look at some of the ethical concerns around the use of AI. So, the first issue here is one of mistakes and safety. And here is the principal. If the history of data that the machine has seen looks like or is very, very, very, very similar to the data it is seeing when it solves the problem, then you are okay. If it doesn't, then you are seriously not okay, you are possibly deep unsafe. So, that's one. Ethical concern number two. So, the origin. Let's say and quality of the historical data which is sometimes called the training data. The data the machine is fed when it is specialising the algorithms it will use. The training data or the historical data. Right. What are the concerns and the origins of this? Well here's the thing. Practitioners, experts, scientists are not always in control of the information the machine is considering when it is specialising itself. How might that be? Suppose a machine learning system considers social media posts to assess, let's say, the marketing quality of some user, to assess their political party, to sell them a product or to advise them about a decision. Suppose because the machine is considering social media data, I as a malicious agent create a social media post that I know the machine will, will consider, will see, will process, and I create them so as to bias or prejudice or disrupt its future operation. With this system you could cause, cause all kinds of chaos in people's lives. 

So, if we have, let's call it, expert control and good faith on the data then we're probably okay, and if we don't then maybe we're not okay. Issue three then, and the final issue we will consider is the system is not making a mistake, operating with high-quality information and in fact making the correct diagnosis, the correct prediction, the correct operation. There still may be concerns with its use. So we can call these moral concerns. Let me give you some examples. Let me give you some examples. So, there are ones around profiling a person with what we may call protected characteristics or, or let's say controversial features of a person. Now, I don't mean to say-, another kind of concern here, a moral concern is, let's call it perhaps even the empathetic one or the automated one. What I mean here is that, you know, there is a question around whether, were it possible for a machine to deliver a diagnosis, a health diagnosis to a person, whether we'd want to do that in an automated fashion. Suppose the diagnosis was cancer. Would we wish to live in a society in which people went to vending machines to discover that they had cancer? There are arguments for such a society, but there are a large number of more arguments perhaps against such a thing. And those arguments are things really to do with the emotional character of human beings. That people can actually be more stressed. People can feel more isolated, more alone, from engaging with automated systems that make no consideration for their state of mental health or their state of emotional health and physical well-being. 

So, we could even say perhaps there's a third-level issue here following on from that is too specialised. If we build a machine to solve one highly specific problem, by doing so we may end up seeing that the problem we have asked the machine to solve was not the problem we originally had, or even really have. It's one mere piece of the puzzle. And in fact a human being might have performed better as I say, across the board. So, we have profiling, we have the empathetic concern which, kind of, amounts to saying it's too specialised. 

Let's also perhaps even say we have the automation concern. So, yes, it's too specialised. What about it being automated? Is the concern for-, are there concerns about job losses here? About replacing people? For the entirety of human history, every single technological innovation has replaced human beings on the activity in which it was used. The automated loom replaced hand stitching of clothes. But what we see every time that that replacement is made is that demand for what can now be automated increased many fold. 

In other words, if we go back to 1800, or 1700, or even earlier, there is a-, there is a global demand in the year 1700 for everyone to have multiple shirts, more than one or two, many, like we have today. But there is no capacity whatsoever to deliver on that demand. The wish is there. If you ask anyone in the planet, in the year 1700, would you wish to have multiple shirts, they would say yes. But they can't have them. But when the automated loom was invented, suddenly everyone could have them. And because that demand existed, more people were employed to serve it now it was there then were lost in the jobs which disappeared because of the automation. And the reliable thing about human beings is that the demand is effectively infinite. If you can create a new technology, then the things that that new technology allows will be wished for by everyone. 

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