AI-900 Exam EP 03: Responsible AI

Summary

In this AI 900 Microsoft Azure AI Fundamentals course, the trainer introduces key concepts of responsible AI in Module 1. The video highlights challenges and risks associated with AI, such as bias, errors, and data privacy concerns. It emphasizes Microsoft's six responsible AI principles: fairness, reliability, privacy, inclusiveness, transparency, and accountability. The trainer also discusses guidelines for human-AI interaction, showcasing examples of transparent AI systems from Microsoft, Apple, Amazon, and Facebook. The next module will focus on an introduction to machine learning.

Takeaways

• 📚 The course is AI-900 Microsoft Azure AI Fundamentals, focusing on responsible AI in module 1.
• 🤖 AI is a powerful tool but must be used responsibly to avoid risks like bias, errors, data exposure, and trust issues.
• ⚖️ Fairness: AI systems must treat all people fairly and avoid biases, especially in areas like loan approvals.
• 🛡️ Reliability and Safety: AI systems, such as those for autonomous vehicles or medical diagnostics, should be thoroughly tested to ensure they perform reliably.
• 🔒 Privacy and Security: AI systems handle large amounts of personal data, which must be protected to maintain privacy.
• 🌍 Inclusiveness: AI should be designed to empower everyone, ensuring no discrimination based on physical ability, gender, or other factors.
• 🔎 Transparency: AI systems should be understandable, with clear explanations of how they work and their limitations.
• 👥 Accountability: Developers must be accountable for their AI systems, ensuring they adhere to legal and ethical standards.
• 📖 Microsoft follows six principles of responsible AI: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
• 💡 Guidelines for human-AI interaction include clear communication upfront, during interaction, when errors occur, and over time, ensuring transparency and understanding.

Q & A

What is the main topic of this AI 900 course module?

The main topic of this module is an introduction to artificial intelligence, focusing on responsible AI and its associated risks and challenges.

What are some potential risks associated with artificial intelligence?

Some potential risks include bias in AI models, errors that can cause harm (such as system failures in autonomous vehicles), exposure of sensitive data, solutions not working for everyone, lack of trust in complex systems, and issues with liability for AI-driven decisions.

Can you provide an example of bias affecting AI results?

Yes, an example of bias in AI could be a loan approval model that discriminates by gender due to biased data used in training.

What are Microsoft's six guiding principles for responsible AI?

Microsoft's six guiding principles for responsible AI are fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.

How does Microsoft Azure ensure fairness in AI models?

Azure Machine Learning includes capabilities to interpret models and quantify how each data feature influences predictions. This helps identify and mitigate bias in the model to ensure fairness.

What is the importance of reliability and safety in AI systems?

AI systems should perform reliably and safely, especially in critical areas like autonomous vehicles or medical diagnostics, as failures or unreliability can pose substantial risks to human life.

Why is privacy and security important in AI systems?

AI systems rely on large amounts of data, which may include personal information. Ensuring privacy and security helps prevent misuse or exposure of sensitive data during and after the system's development.

How should AI systems promote inclusiveness?

AI systems should be designed to empower everyone, regardless of physical ability, gender, ethnicity, or other factors, ensuring that all parts of society benefit from AI.

What is the role of transparency in responsible AI?

Transparency means that AI systems should be understandable to users. They should be fully informed about the system's purpose, how it works, and its limitations.

What does accountability mean in the context of AI systems?

Accountability in AI means that developers and designers must ensure their systems comply with ethical and legal standards, and they should be responsible for the AI's outcomes.

Outlines

00:00

🎓 Introduction to AI and Responsible AI Challenges

The instructor, Sushant Sutish, introduces himself and the course on Microsoft Azure AI Fundamentals (AI-900). The module covers artificial intelligence and the importance of responsible AI. Key challenges and risks associated with AI are discussed, including bias in AI models, potential harm from system errors, data security issues, inclusivity problems, user trust, and accountability for AI-driven decisions. Examples include biased loan models, autonomous vehicle failures, insecure data handling, and challenges with AI accessibility for users with disabilities.

05:02

🛡️ Microsoft's Six Responsible AI Principles

This section delves into Microsoft’s six guiding principles for responsible AI: fairness, reliability, privacy, inclusiveness, transparency, and accountability. Each principle is explained with examples, such as the need for fair loan approval models, reliable AI systems like autonomous vehicles and medical diagnostic tools, and privacy concerns around personal data. The importance of inclusivity is highlighted, stressing that AI should benefit all, irrespective of physical ability, gender, or ethnicity. Transparency ensures users understand AI systems, while accountability makes developers responsible for meeting ethical and legal standards.

10:03

🌐 Exploring Microsoft’s Responsible AI Practices

In this section, users are encouraged to explore Microsoft's Responsible AI site for more details on ethical AI practices. The site offers videos from experts explaining Microsoft's approach. A demo on human-AI interaction guidelines is introduced, accessible via a provided link. The demo features cards representing different stages of interaction with AI systems, from intent to error handling and long-term use. The examples highlight transparency and user control, like Microsoft Office explaining its features upfront or Apple Music making non-intrusive recommendations.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence refers to the development of computer systems that can perform tasks typically requiring human intelligence. In the video, AI is presented as a powerful tool with great potential to benefit the world, but it also carries risks and challenges such as bias and data privacy concerns. The course discusses how AI must be developed responsibly to avoid unintended negative consequences.

💡Bias

Bias in AI occurs when a machine learning model's predictions are influenced by prejudiced data, leading to unfair outcomes. In the video, an example is given where a loan approval model discriminates by gender because of biased training data. The importance of mitigating bias is emphasized as part of responsible AI development.

💡Fairness

Fairness in AI means ensuring that systems treat all people equally, without discrimination based on gender, ethnicity, or other factors. The video explains how fairness is one of Microsoft’s six principles for responsible AI, with tools in Azure Machine Learning designed to identify and reduce bias in models to ensure fair decision-making.

💡Reliability and Safety

Reliability and safety in AI refer to ensuring that systems perform consistently and do not pose risks to human life. The video gives the example of an autonomous vehicle or medical diagnostic model that could endanger lives if it fails. Rigorous testing and quality control are essential to make AI systems safe and dependable.

💡Privacy and Security

Privacy and security involve safeguarding sensitive data used in AI systems. Since AI models rely on large volumes of data, including personal information, the video highlights the importance of protecting this data from breaches and ensuring it remains secure even after the model is in production. Privacy is a core principle in responsible AI development.

💡Inclusiveness

Inclusiveness in AI means ensuring that AI systems empower and benefit all people, regardless of physical ability, gender, or other characteristics. The video stresses that AI should be designed to be accessible and useful to all parts of society. An example is given of a home automation system that fails to serve visually impaired users because it lacks audio output.

💡Transparency

Transparency in AI refers to making AI systems understandable to users, including how they work and what their limitations are. In the video, transparency is highlighted as a core principle, ensuring that users know the purpose and functioning of the AI system. Examples include recommendations from Apple Music or Amazon where users are informed why certain content is being suggested.

💡Accountability

Accountability in AI ensures that humans, particularly designers and developers, are responsible for the decisions made by AI systems. The video discusses scenarios where AI makes significant decisions, such as convicting someone of a crime using facial recognition, and raises the question of who is responsible for those decisions. Microsoft emphasizes that developers must adhere to ethical and legal standards.

💡Human-AI Interaction

Human-AI interaction refers to the design of systems that interact smoothly with human users. The video points to Microsoft's guidelines for human-AI interaction, highlighting the importance of transparency and communication during interactions. Examples include how Microsoft, Apple, and Amazon provide explanations for recommendations or actions taken by AI systems.

💡Responsible AI

Responsible AI involves creating AI systems that adhere to ethical principles and avoid harmful outcomes. The video outlines Microsoft's six principles—fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability—that guide the development of responsible AI. These principles ensure that AI applications address real-world challenges without causing unintended harm.

Highlights

Introduction to AI 900 course covering Microsoft Azure AI Fundamentals certification.

Focus on Responsible AI, discussing the challenges and risks associated with artificial intelligence.

Bias can affect results, with an example of a loan approval model discriminating by gender due to biased training data.

Errors in AI may cause harm, such as autonomous vehicle system failures leading to collisions.

Data exposure risk, demonstrated by insecurely stored sensitive patient data in medical diagnostic bots.

AI solutions may not work for everyone, with an example of a home automation system lacking audio for visually impaired users.

AI systems require user trust, like financial tools making opaque investment recommendations.

Accountability in AI-driven decisions, exemplified by wrongful criminal convictions based on flawed facial recognition.

Microsoft's six guiding principles for AI development: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.

Fairness principle: AI models should treat all people fairly, avoiding bias based on factors like gender or ethnicity.

Reliability and safety principle: AI systems must undergo rigorous testing to ensure they perform safely and as expected.

Privacy and security principle: AI systems should respect user privacy and protect personal data throughout their lifecycle.

Inclusiveness principle: AI should benefit all parts of society, regardless of physical ability, gender, or other factors.

Transparency principle: Users should understand how AI systems work, their purposes, and their limitations.

Accountability principle: AI designers and developers should be held accountable for ensuring solutions meet ethical and legal standards.

Transcripts

hey welcome back my name is sushant

sutish

and i'm your trainer for this ai 900

which is

microsoft azure ai fundamentals

certification examination course we are

still at module 1

module one is all about introduction to

artificial intelligence

and in this lesson we're gonna learn

about responsible ai

so without wasting any more time let's

get into it

let us understand the challenges and

risks associated with artificial

intelligence

artificial intelligence is a powerful

tool

that can be used to greatly benefit the

world however

like any tool it must be used

responsibly

so let me show you some of the potential

challenges or risk

based by an ai developer

the first one is bias can affect

results let's look at the first example

on this

an example could be a loan approval

model

discriminates by gender due to bias in

the data

with which it was trained

then the challenge or risk is errors may

cause

harm an example of this is an autonomous

vehicle

experiences a system failure and causes

a collision

and the third challenge or risk would be

data could be exposed

an example of this risk is a medical

diagnostic bot

is trained using sensitive patient data

which is stored insecurely

let's look at the next challenge

solutions may not

work for everyone an example of this

risk is

a home automation assistant provides no

audio output

for visually impaired users

another risk is user must trust a

complex system

an example of this risk is an ai based

financial tool makes investment

recommendation

and what are they based on

let's look at one more challenge who is

liable for ai driven decisions

an example of this challenge would be an

innocent person

is convinced of a crime based on

evidence

from facial recognition so who is

responsible for that

so these are some of the things we need

to keep in mind certain challenges and

the risk which is faced with ai

at microsoft ai software development

is guided by a set of six principles

designed to ensure that ai applications

provide amazing solution

to difficult problems without any

unintended negative consequences

so these six principles are fairness

reliability and safety privacy and

security

inclusiveness transparency and

accountability

you can know more about it by going into

this website which i mentioned over here

let me take you through each one by one

so what is fairness ai system should

treat

all people fairly for example suppose

you create a machine learning model

to support a loan approval application

for a bank

the model should make predictions of

whether or not the loan should be

approved without incorporating any bias

based on gender

ethnicity or other factors that might

result in an unfair advantage

or disadvantage to specific group of

applicants

azure machine learning includes the

capability to interpret models

and quantify the extent to which each

feature of the data influence the model

prediction

this capability helps data scientists

and developers

identify and mitigate bias in the model

let's understand reliability and safety

ai system should perform reliably and

safely

for example consider an ai ai-based

software system

for an autonomous vehicle or on a

machine learning model

that diagnoses patient symptoms and

recommends prescriptions

unreliability in these kind of system

can

result in substantial risk to human life

aib software application development

must be subjected to rigorous testing

and deployment management process to

ensure that they work as expected

before release let's

understand privacy and security

ai system should be secure and respect

privacy

the machine learning models on which ai

systems are based

rely on large volumes of data which may

contain

personal details that must be kept

private

even after the models are trained and

the system is in production

it uses new data to make predictions or

take action

that may be subject to privacy or

security concerns

let's understand about inclusiveness

ai system should empower everyone and

engage people ai should bring benefits

to

all part of the society regardless of

physical ability

gender sexual orientation ethnicity

or other factors

and what about transparency artificial

intelligence system

should be understandable users should be

made fully aware of the purpose of the

system

how it works and what limitations may be

expected

and let's understand about

accountability

people should be accountable for

artificial intelligence systems

designers and developers of aib solution

should work within a framework of

governance

and organizational principles that

ensure the solution meets ethical

and legal standards that are clearly

defined

so let me take you to microsoft

responsible ai site this is a one-stop

place where you can understand

the responsible ai features this is

where you can understand

what are the responsible ai practices

microsoft is following

you can go under each topic and play

this video to understand more

and listen to these experts what they

talk about these responsible ai topics

next i want to take you through the

guidelines for

human ai interaction demo for that

go to aka dot ms slash

hci dash demo this is where you would be

able to learn

more about the guidelines of human ai

interaction

there are different cards on each deck

and you can click on

each to review the example scenarios

there are four decks available so first

one is

initially when you make an intent the

second one

is during an interaction and the third

deck is all about

when and when an ai system is wrong

and the final one is over time how would

you notify

or give you information about these

scenarios so let's pick the first

example

this is an example of microsoft letting

everyone know about their office new

companion experiences

and by upfront microsoft tells what it

can do and

how it can do it so it is very

transparent from the very beginning

another example where apple music is

letting users know that

we think you will like it's not that

they are pushing this

idea into your mind it's a

recommendation

based on what the system thinks and last

example

is all about the outlook web email

explain what are the filtering they do

based on the focused email mailbox etc

so it is letting you the intent

at the very beginning let's pick another

card

from the next deck which is during

interaction

my analytics let you know that how it is

using your data

to help you work smarter so during the

interaction itself

it is giving you more insight into how

this can help you

change your life in terms of your

work-life balance etc

and within bing when you search about a

doctor it brings you details like

it brings you ceos or doctors which

shows images of diverse people

not just not just focusing on a

particular gender or ethnicity

let's pick another card from the third

deck which is went wrong

this is where the system make clear why

the system did what it did

so the first example shows microsoft

online

recommending the documents based on your

history and activity

so it is showing that you are seeing

this recommendation based on what you

have done in the past

let's look at the next example

this is where amazon recommend you a

different product

and when you want to know why amazon

recommended this product you can click

on why recommended to understand what

are the patterns it used to

came up with this conclusion and let's

look at the third example

and in facebook you might see some ads

and facebook

enable you to access these explanation

about why you are seeing each ad

in the news feed by clicking on the

information

and the last one is over time what are

the information

learning the first example show you how

the system is notifying users about

the change in this example the system

clearly showed that

what's new is going to show you all the

latest features and update which

included in the ai features

so this is the place all of you can come

and have a look at

all these cards and look at the

different examples to show you

to show you different guidelines for

human ai interactions

so i hope the information provided was

useful

in the next video we are entering a

brand new module

the module 2 is all about machine

learning

and the first lesson we are going to

learn on module 2 is about introduction

to

machine learning so i will see you on

the next one till then

take care

you