In this lesson we are going to talk about responsible AI.
So responsible.
AI is a very new and down, uh, discipline which, uh, tries to uh.
Include the principles that you should follow in order to have an acceptable, socially acceptable,
uh, AI application.
So I think it may be interesting to review, at least very quickly, these main principles that right
now are included in responsible AI.
The first one is fairness and eliminating bias.
This principle emphasizes the importance of developing AI systems.
AI applications that treat all users fairly regardless of their race, gender, age, or other characteristics.
It involves eliminating bias in data and algorithms to prevent discrimination and ensure that AI decisions
are fair and equitable.
So remember, after immediately after the launch of ChatGPT, we started to see a news about how different
the ChatGPT responses were.
If the user asks about a the.
Perfect job for a man or for a woman, or the perfect candidate for a software engineer, a role in
terms of race or whatever.
So we saw that there was a huge polemic, you know, a and scandal, if you want, around the responses
that, uh, you know, the most popular, uh, artificial intelligence application was providing and
this, this, this talk to a lot of changes in the way ChatGPT was, uh, uh, managed and handled,
etc..
So this first principle is, is very important.
And as you can imagine, it can impact in the reputation of a company.
Uh, and uh, even in the, in the legal future of, of one company.
Right.
Second principle, reliability and safety.
Reliability refers to the ability of the LM applications to function consistently and predictably.
Safety.
Safety involves protecting AI applications from malicious manipulations and ensuring they do not harm
users or the environment.
This includes implementing robust measures to prevent errors and failures.
So reliability, as we have been talking about before in the program, is one of the most important
things, uh, to consider when you are preparing an LM application.
Remember hallucinations and all these problems and how, for example, in the second sites project,
we were, uh, working with this with this problem, right?
Uh, you know, uh, adding the sources, a link to, to the sources we were using to provide any response,
etc., etc..
Right.
So reliability is very important and safety as well.
And safety is talking about, you know, uh, malicious intents, uh, manipulations etc., etc..
So we need to check our LM application before we launch it.
Uh, in search of problems like this one's reliability very important.
And safety as well.
Third principle privacy and data protection.
This principle focuses on ensuring the confidentiality and security of personal data used by AI applications.
It requires effective policies and technologies to protect personal information from unauthorized access
and misuse, as well as compliance with data privacy laws and regulations.
Remember a immediately after the launch of ChatGPT?
It was forbidden in some European countries like Italy, for example, and this was the reason.
So a data protection laws are especially a.
Difficult or sophisticated in the European Union, as they will be in many other countries around the
world.
And this is one of the areas where, if not properly prepared, LLM applications can find a lot of trouble.
So before launching your LLM application, consider carefully where your data come from and if you are
a.
Keeping this principle of privacy and data protection well cover fourth principle transparency and explainability.
Transparency in LLM applications in AI applications means that processes and decisions should be open
and understandable to users and stakeholders.
Explainability refers to the ability of artificial intelligence systems to provide understandable and
meaningful explanations of their decisions and actions, which is crucial for building trust and understanding.
So this is one of the principles that, in my opinion, are not a.
100% follow by everybody.
So if you remember Elon Musk, the founder of Tesla, and right now the the owner of the previous Twitter
that right now is called ex Elon Musk, as you know, uh, which is one person closely, very closely
associated with artificial intelligence because he was the originator.
Uh, he had the initial idea for OpenAI, the company behind ChatGPT, and he was the person, you know,
building the group of people and and, you know, preparing the initial philosophy of the company,
etc..
Right now, he's not there anymore, and he is not in good terms with the team anymore.
And in a.
One of the last interviews of Elon Musk talking about Elon Musk, talking about artificial intelligence,
got the attention of many people because one of the things he said there is well.
Most LLM foundational models foundation models have been trained with.
Data that a in many cases a ha is copyrighted.
So this is one of the areas where LM a.
Foundation models.
Uh, the companies behind them, especially the private ones, are not going to be very specific, are
not going to provide a lot of details because they can get in trouble because it is, uh, supposed
or we think we can guess that the initial data they used to train their models may not be 100% open
source.
So this is one of the principles where you will have to well, you will have to think about and prepare
yourself, uh, but you will also need to realize that not everybody is in the same line here.
The principal number five accountability and governance is.
Also an interesting one.
Accountability implies that developers and users of LM applications and artificial intelligence applications
must be responsible for their decisions and actions, and governance refers to the need to establish
legal, ethical, and policy frameworks to guide the development and use of artificial intelligence,
ensuring compliance with ethical and legal standards.
So.
In this second part, the governance.
We have seen a lot of movements in the recent months, so we have seen a regulation comments coming
in the European Union, United States and other parts of the world.
So this is one of the areas where we are going to have a lot of movements in the coming months and years.
There is a debate, especially in the startup world, regarding the a convenience or not convenience
of a include a lot of regulations in a moment where we are start very, very very we are still very,
very green.
Right.
So this is a very early moment for the a artificial intelligence generative artificial intelligence
industry.
And maybe it is not the right moment to start limiting the possibilities of the industry, but.
Well, this is one very, very I would say a.
This debate is right now over the table.
Right.
So let's see what happens with the regulations associated with artificial intelligence.
But this is one of the most, uh, I would say, important principles in responsible AIS.
And the last one that we are going to, uh, focus on in this brief introduction about responsible AI
is inclusivity and accessibility.
So this principle promotes the development of artificial intelligence applications and LM applications
that are accessible and useful to a broad range of users, including those with disabilities or from
marginalized groups.
Inclusivity in artificial intelligence seeks to ensure that technology benefits the whole society,
not just privileged sectors.
So we have been talking about some problems that you, uh, may remember, uh, regarding the initial,
uh, days of ChatGPT and this, uh, principle.
Right.
So it is important to have responsible AI in mind when you are preparing the launch of a profession
of a professional LLM application.
Sometimes this is a matter that is not going to be in the hands of the engineer.
But so sometimes there it will be another department in your company handling handling this.
But in many cases you would be as as the AI engineer in the company or one of them, you would be one
of the people that should warn your company about possible problems in this area and how to prepare
or prevent, uh, this, this, these problems to happen.
In the next lesson, we are going to talk about a real world LM ops solutions you may find in the market,
because this is an area LM ops where you probably are going to use external tools in order to handle
this, this problem.
So let's see some real, uh, solutions in the next lesson.