- [Craig] In this video, we take a look at some of the other ethical and legal issues,
with particular interest on artificial intelligence,
machine learning and robotics.
(uplifting piano jingle)
So there's a lot of hype and excitement at the moment around emerging technologies
of artificial intelligence and machine learning
and what this could mean for the future of robotics.
There is also, however, a lot of confusion with these terms.
They often get used interchangeably.
So before we dive into this video,
what do these actual terms mean, and how do they relate to each other?
Well, John McCarthy is an American computer scientist born in 1956,
who coined the term artificial intelligence.
And he said for the present purpose,
the artificial intelligence problem is taken to be that of
making a machine behave in ways that would be called intelligent if a human were so behaving.
Put in slightly more plain English,
it's a broad term which describes any machine programmed
to think, work or react like humans do.
Now it should be pointed out that not all AI is smart.
And generally, we can break it down into two broad categories.
There's applied or what's known as weak or narrow AI.
And this is AI designed to manage a very specific task.
And most AI actually falls into this category.
For example, it will do image recognition,
for example, number plate or face recognition.
Then there's the much more interesting and emerging field known as generalised AI.
This is where technology can evolve and improve to handle different tasks and scenarios.
As we've said, it's an emerging and developing area of AI,
and it leads us nicely onto the concept of machine learning.
So Arthur Samuel, a pioneer in computer gaming and AI,
coined the term machine learning three years later in 1959,
and he defined it as the ability to learn without being explicitly programmed.
Machine learning is a subset of artificial intelligence.
In other words, it's one method which can be used to achieve AI.
It's the science of training devices or software to perform a task and improve its capabilities.
This is achieved typically by feeding an AI data, information and different scenarios
so it can learn and improve and adapt itself over time.
In 2019, 78 per cent of all the money invested in AI was directed at the machine learning concept.
Now the term robotics often gets thrown around in association with AI and machine learning,
but the vast majority of robots are not smart at all.
A robot is simply a machine which carries out work by itself
by following a set of programmed rules.
As mentioned already, the vast majority of robots can be thought of as dumb robots.
For example, those in a car assembly line.
They simply repeat the same task over and over again.
There's no AI involved at all.
Then we have what we can think of as smart robots.
And this is where AI and machine learning comes into play.
Robots trained to carry out more complex tasks.
Robots that can learn and adapt over time.
For example, a robot designed to play chess and get better as it plays more opponents.
So let's look at some examples
of where artificial intelligence is actually currently being used.
There's a lot of talk about autonomous self-drive cars,
and these are certainly going to be a thing
we're going to be seeing more and more of in the years to come.
Predicting when to make stock market trades is another area where AI is being heavily used.
You may be used to using online sites for buying products,
and you may notice how they often suggest similar products that you would like
based on your previous shopping habits and your trends.
This is another area where AI is being heavily used.
Another example could be in education,
a program which adapts the content to revise
based on a student's prior learning, its success and their failures.
And possibly in manufacturing,
alerting an operator about fixes and errors in a manufacturing process.
AI is also being heavily used throughout many other areas
of the scientific and medical research.
Now when it comes to the concept of artificial intelligence,
machine learning and robotics,
there are four areas that you need to specifically think about for your exam,
and these are accountability, safety, algorithmic bias and legal liability.
So in order to talk about these four areas in a bit more detail,
we're going to consider the situation through the lens of autonomous self-driving cars.
On the screen are a number of scenarios,
and we're going to focus on one of them moving ahead.
Let's take one particular scenario.
So we have an autonomous self-driving car travelling at the speed limit and suddenly,
without warning, a single person has run out into the road into the path of the car.
The car has calculated there's not enough safe stopping distance.
So if the car carries on its course,
it will hit the person.
If the car turns either left or right,
it will avoid the person
but will crash the car with its driver and passengers into a wall on either side.
So what should the autonomous AI do in this situation?
Now this is a great exercise in ethical and moral thinking,
as there's no easy right or wrong answer.
Although you have to realise that somewhere,
there are organisations and groups of people who are having to make these sort of decisions
and translate those choices into algorithms which control autonomous self-driving cars.
Two facts are true.
The AI algorithms behind self-drive cars
are going to massively reduce the number of road traffic-related deaths in the years ahead.
At the same time, the AI algorithms behind self-drive cars
will cause the unavoidable death of people in the years ahead.
So given this fact,
the important focus for this video
is looking at the choices the AI and machine learning algorithms are taking
and considering who should be accountable.
What are the safety concerns, what's the algorithmic bias,
and where's the legal liability?
So let's start with algorithmic bias.
This is the idea that when designing an algorithm,
you might favour certain outputs over others or favour one group of users over another.
Now one argument could be that the algorithm should be written with no algorithmic bias at all.
It doesn't matter.
It should just take all the facts as they are.
You could also argue an ethical point
that maybe it should take into consideration
the age of the potential person the car might collide with.
For example, if it's a very young or small child,
should the algorithm be making different decisions?
This person hasn't had any chance to live their life yet.
Should the rules of the program change?
And if that's the case,
is an algorithm determining whether to potentially harm or kill someone simply based on their age?
And is this right?
Secondly, we need to consider safety.
How can we ensure safety with the implementation of an algorithm
which is able to make its own choices, learn and adapt?
What rules should be programmed to make sure an algorithm does no harm?
What should it do when harm is unavoidable?
It's very easy to take the simple stance that an algorithm should simply do no harm.
But now think of the situation where an algorithm is deciding
whether it should hit a cyclist on the left without a helmet
or a cyclist on the right with a helmet.
You could argue that the cyclist with the helmet has a better chance of surviving.
Therefore, we should hit that cyclist because our algorithm says do no harm.
But surely we're now penalising the person who's chosen to be responsible and wear a helmet.
So should we now hit the person without the helmet?
Well, we've gone way beyond our original definition of doing no harm,
and our algorithm is now dishing out street justice.
Think about accountability.
So the choices the AI makes are going to have consequences.
And in our scenario of a self-drive car,
these consequences could actually be serious harm or even death.
Who should be held accountable for the actions carried out by a smart AI algorithm?
And this leads us nicely onto the related concept of legal liability.
So in the case of loss of life, who should,
or indeed, could,
be legally held responsible?
Should it be the person who purchased the car?
Should it be the original programmer,
the manufacturer or government group or body?
There are no easy answer to these questions,
but these are areas you have to think about for the exam.
So pause the video.
And here we've got some notes on the difference between artificial intelligence,
machine learning and robotics.
And in this section, we go over again the four areas that you need to consider for your exam
and some of the questions that you could pose around the ethical and legal issues.
(uplifting piano jingle)