The Importance of AI Governance

Summary

The video script addresses the exponential growth of artificial intelligence, highlighting both its potential and the risks associated with premature deployment. It emphasizes the importance of AI governance, which includes rules and processes to ensure ethical development and deployment. The script outlines the benefits of AI, such as cost reduction and efficiency, but also discusses the risks like bias, privacy infringement, and lack of transparency. It also touches on the need for continuous monitoring and adherence to regulations like the NIST AI regulation and the EU AI act to mitigate these risks and fully harness AI's potential.

Takeaways

• 🌟 Artificial Intelligence (AI) is growing exponentially, with new use cases emerging daily.
• ⚠️ There are risks associated with premature deployment of AI systems, including misdirecting customers and biased outcomes.
• 🛡️ AI governance is crucial for ensuring responsible and ethical development and deployment of AI systems.
• 🔍 AI governance provides guardrails to minimize risk and maximize benefits, ensuring ethical use of AI systems.
• 🤖 AI systems are designed to mimic, augment, or aid human decision-making, with AI models at their core.
• 📊 AI models learn from human-generated data, which can include biases that the models may inadvertently reflect.
• 🔒 Privacy and copyright infringement are risks when sensitive or copyrighted data is not properly overseen in AI systems.
• 🔍 Black box models, while more accurate, lack transparency, making it difficult to understand their decision-making processes.
• 🔄 Continuous monitoring is necessary to prevent deterioration of AI models due to changes in incoming data.
• 📜 Global organizations are creating regulations and guidelines, like the NIST AI regulation and the EU AI act, to manage AI systems and penalize non-compliance.

Q & A

What is the current pace of growth in the field of artificial intelligence?

The field of artificial intelligence is growing at an exponential pace, with new use cases and applications emerging daily that were unimaginable in the past few years.

Why is AI governance becoming increasingly important?

AI governance is becoming increasingly important because premature deployment and adoption of AI systems without proper governance can lead to reputational and financial loss due to misdirected decisions, biased outcomes, and other issues.

What does AI governance refer to?

AI governance refers to a set of rules, standards, and processes that ensure the responsible and ethical development and deployment of artificial intelligence systems, acting as guardrails to minimize risk while maximizing potential benefits.

What are the potential benefits of artificial intelligence?

The benefits of artificial intelligence include reduced costs, improved efficiency, and automation of repetitive and manual tasks.

What are the risks associated with AI systems?

Risks associated with AI systems include susceptibility to bias due to human-generated data, privacy or copyright infringement, lack of transparency or trust due to the use of black box models, and the need for continuous monitoring to maintain high-quality outcomes.

How does an AI model mimic human decision-making?

An AI model mimics human decision-making by taking in data, deriving patterns from it, and learning how to generate outputs that a human would normally produce.

Why do biases exist in AI systems?

Biases exist in AI systems because the data used to train these models is human-generated and may contain latent cognitive biases that the AI model can inadvertently pick up and reflect in its outcomes.

What is the significance of black box models in AI?

Black box models are significant in AI because they tend to provide higher levels of accuracy in outcomes compared to glass box models. However, they also pose a risk due to their lack of transparency, as the inner workings of the algorithms are not easily understood or explainable.

Why is continuous monitoring of AI models necessary?

Continuous monitoring of AI models is necessary because models can deteriorate over time if the incoming data differs from what the models were trained on, leading to inconsistent outcomes and potentially compromising the quality of decisions made by the AI.

What are some of the regulations and guidelines that organizations are adopting to manage AI systems?

Organizations are adopting regulations and guidelines such as the NIST AI regulation and AI risk management framework, as well as the EU AI act, to manage AI systems. These regulations not only provide guidance but also penalize companies for noncompliance, emphasizing the importance of AI governance.

How does proper AI governance help organizations realize the potential of artificial intelligence?

Proper AI governance helps organizations to mitigate risks such as bias, privacy infringement, lack of transparency, and model deterioration, thereby allowing them to fully realize the potential of artificial intelligence in a responsible and ethical manner.

Outlines

00:00

🚀 AI Growth and Governance

The paragraph discusses the rapid growth of artificial intelligence (AI) and its expanding applications. It highlights the risks associated with premature deployment of AI systems, such as misdirected decisions, hallucinated responses, and biased outcomes, which can lead to significant reputational and financial losses. The concept of AI governance is introduced as a critical topic, defined as a framework of rules, standards, and processes to ensure the ethical and responsible development and deployment of AI. The paragraph emphasizes the need for 'guardrails' to minimize risks while maximizing benefits, and outlines the benefits of AI, such as cost reduction, efficiency improvement, and automation of manual tasks. It also touches on the构成 of an AI system, focusing on the AI model at its core, which is designed to mimic, augment, or aid human decision-making through the analysis of human-generated data.

05:02

🔒 Addressing AI Risks and Regulations

This paragraph delves into the specific risks associated with AI systems, including the potential for bias in data and outcomes, privacy and copyright infringement due to the use of sensitive or unstructured data, and the lack of transparency with black box models that do not provide explanations for their decisions. It also addresses the need for continuous monitoring of AI models to prevent deterioration in performance over time. The paragraph concludes by discussing the importance of adhering to regulations and guidelines, such as the NIST AI regulation and the EU AI act, which can penalize non-compliance and underscores the necessity of AI governance to mitigate these risks and ethical dilemmas, allowing organizations to safely harness the potential of AI.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, AI is highlighted as a rapidly growing field with numerous applications, but also with significant risks if not properly governed. The video emphasizes the importance of AI governance to ensure responsible and ethical development and deployment of AI systems, which is crucial to mitigate risks and maximize benefits.

💡AI Governance

AI governance is defined as a set of rules, standards, and processes established to ensure the responsible and ethical development and deployment of AI systems. It acts as a framework to guide the use of AI, aiming to minimize risks and maximize benefits. The video underscores AI governance as a critical topic due to the potential for AI systems to cause reputational and financial damage if not managed properly.

💡Bias

Bias in AI refers to the tendency of AI systems to favor certain outcomes over others due to the presence of prejudiced or unrepresentative data in their training. The video points out that human-generated data, which AI models learn from, can contain latent biases that the models may inadvertently learn and reflect in their outputs, leading to unfair or discriminatory results.

💡Data

Data is the raw material that AI systems use to learn and make decisions. The video discusses how AI models are trained on data, which can be structured, semi-structured, or unstructured. It also highlights the risks associated with data, such as privacy infringement and copyright issues, if not properly managed.

💡Model

In the context of AI, a model refers to the algorithm or set of algorithms that process input data to produce output. The video explains that AI models are designed to mimic human decision-making by learning from data. However, it also warns about the potential for these models to deteriorate over time if not continuously monitored and updated.

💡Privacy Infringement

Privacy infringement occurs when sensitive or private information is improperly collected, used, or disclosed without consent. The video mentions this as a risk associated with AI systems, particularly when they are trained on data that may contain personal information without proper oversight.

💡Copyright Infringement

Copyright infringement happens when someone uses copyrighted material without permission from the copyright holder. The video points out that AI systems, especially when trained on unstructured data, might inadvertently incorporate copyrighted content into their outputs, leading to legal issues.

💡Black Box Models

Black box models in AI are models where the internal workings and decision-making processes are not transparent or understandable. The video discusses how these models, despite offering high accuracy, pose a risk due to their lack of transparency, making it difficult for users to trust or understand the rationale behind their outputs.

💡Continuous Monitoring

Continuous monitoring in the context of AI refers to the ongoing process of evaluating and updating AI models to ensure they perform consistently and accurately. The video emphasizes the importance of this practice to prevent model deterioration and to maintain the quality of AI system outcomes.

💡Regulations

Regulations are legal rules or orders that have the force of law and are enforced by a governing authority. The video mentions various AI regulations and guidelines, such as the NIST AI regulation and the EU AI act, which are designed to manage AI systems and can penalize companies for non-compliance, highlighting the legal implications of improper AI governance.

Highlights

Artificial intelligence is growing exponentially with new use cases emerging daily.

Some AI systems deployed in production are not yielding expected outcomes.

Instances of chatbots misdirecting customers and generating biased outcomes are increasing.

Premature deployment of AI systems can lead to reputational and financial risks for companies.

AI governance is crucial for the responsible and ethical development of AI systems.

AI governance includes a set of rules, standards, and processes to ensure ethical use.

AI systems are designed to mimic, augment, or aid human decision-making.

AI models are at the core of AI systems, aiming to generate human-like outputs.

Data, particularly human-generated data, is essential for training AI models.

Human biases can be inadvertently embedded in AI systems through the data used for training.

AI systems risk privacy infringement if sensitive data is not properly overseen.

Black box AI models can lack transparency, making it difficult to understand decision-making processes.

AI models require continuous monitoring to ensure consistent quality of outcomes.

Global organizations are creating regulations and guidelines for AI system management.

Non-compliance with AI regulations can result in penalties and reputational damage.

Ethical dilemmas arise from the risks associated with AI systems.

Governing AI systems is essential to fully realize their potential while mitigating risks.

Proper AI governance is key for organizations to harness the benefits of artificial intelligence.

Transcripts

If you have been following the news in artificial intelligence,

you probably already know that this field is growing

at an exponential pace.

Each day we are hearing about new use cases in AI,

use cases and applications that we haven't even dreamt of

in the past few years.

But in the same news,

you probably are also hearing about AI systems

that have been deployed to production

that are not yielding the expected outcomes.

We are hearing about chat bots that

have been misdirecting customers and

employees into making the wrong decisions.

We also heard about chatbots

that are hallucinating response to the customers.

And we also heard about models that

have been generating biased outcomes.

There is no denying that there is huge

potential in artificial intelligence,

but a premature

deployment and adoption of AI systems

could put the companies at a huge risk

of reputational and financial loss.

And this exactly is the reason why

artificial intelligence governance

has become one of the most relevant and

important topics today.

So let's dive in and understand a

bit more about AI governance.

Let's start off by defining what it means.

AI governance refers to a set of rules,

standards,

and processes ...

... that have been set in place

in order to ensure the responsible and ethical

development and deployment of artificial intelligence systems.

Think of it as a set of guardrails,

that ensure the ethical use

of these artificial intelligence systems

so that we minimize the risk in the systems,

while maximizing the potential benefit.

Now we all know what the benefits are of artificial intelligence.

It leads to reduced costs,

improved efficiency,

and leads to automation

of repetitive and manual tasks.

While these benefits make artificial intelligence

a highly important topic and most sought after technology today,

there is still at risk in artificial intelligence

that is making AI governance

an even more important topic to be discussed.

In order to understand what these risks are,

let's, in a very broad sense,

understand what constitutes an AI system.

Now, an AI system

is a system that is designed to take in inputs

and produce outputs that in some way mimic,

augment or aid human decision making.

At the heart of the system is what we call the AI model.

Now, the goal of this model

is to look at the input and generate an output

that a human would normally do.

So how does an AI model do that?

And in order for this model to do exactly that,

we need to supply it with data.

Because we wanted to mimic or augment

or aid human decision making

we need to supply it data that is human generated.

And this data could be in any format.

It could be structured data with columns and values.

Or it could be semi-structured data,

such as XML files or unstructured data

such as PDF documents, text documents,

audio files, video files, etc.

Now, this AI model,

which you can think of as highly engineered code

that uses complex mathematical algorithms,

looks at the data, derives patterns from data,

and learns how to mimic the human behavior that is expected from it.

Now since we are talking about data, right?

And this data is human generated

and we humans unfortunately are not devoid of bias.

We have several cognitive biases within us.

An example of one such biases,

some of us tend to put undue importance

on certain factors while making decisions,

while completely ignoring another set of factors.

Now that can cause biases in the data.

Yes, the biases are not blatantly visible.

You cannot look at the data and say,

"yes, I see biases", but these are latent and hidden.

And this highly engineered mathematical code

has a tendency to pick up on these biases.

And in worst case scenarios,

it reflects those biases in the outcomes.

So that makes your AI systems susceptible to bias.

Secondly, we are talking about data here.

The data is being sent as input.

Data is being used for training the model

and if there is no proper oversight,

this data could contain private and sensitive information.

And when there is no proper oversight,

this data can seep into the model

and seep into the output of the model,

leading to privacy infringement.

Or in other cases

in cases of unstructured data it could also contain copyright information

and that can also be reflected in your model's output.

So privacy or copyright infringement.

Some of the models that we use are black box models.

So the reason we use these black box models,

as opposed to the glass box models,

is that black box models tend to provide a higher level of accuracy in the outcomes.

So when we use black box models,

what it means is that the people who are creating these models

and systems have little control over the inner workings of the algorithms.

So when you ask them why their model is making a certain decision,

they won't be able to give you an an explanation.

So in that case, your system is not transparent.

So that puts you at a risk of lack of transparency, or trust.

So how would you trust a system that is not able to explain why and how it made a certain decision?

And that is a third risk in artificial intelligence systems.

Now, these AI models are not something that you once create and they continue to generate high quality outcomes.

These models can deteriorate

and the deterioration can happen because the incoming data

could be very different from the data that the models have been trained on.

And because this deterioration happens,

there is a need for continuously monitoring these models.

So that is another factor that can put your models at risk

because if you are not continuously monitoring them,

then you are not ... your model is not producing consistently high quality outcomes.

So because of risks like these,

there are organizations across the world that are coming up with regulations

and guidelines on how to manage these systems.

Now the guidelines, you can think of something like the NIST AI regulation,

AI risk management framework.

And as for regulations, we all heard about the EU AI act.

And these regulations are much more serious because

these are not just guidance for AI system deployment,

but can actually penalize companies for noncompliance.

So when your company or when your AI systems

are not complying with the guidelines stipulated by the regulation,

then you will be at a risk of reputational loss and financial loss as well.

And several ethical dilemmas as well.

So due to factors such as these:

bias, privacy or copyright infringement,

lack of trust or transparency,

and lack of continuous monitoring

and the presence of regulations,

it is extremely important that we govern our artificial intelligence systems.

I think we can all agree that the promise of AI is undeniable,

but the risks it poses are very much real.

And a properly governed AI system

is extremely important for

organizations to fully realize the

potential of artificial intelligence.

I hope you found this video helpful.

Please let us know what you think about

it in the comments.

Thank you.

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