AI-900 Exam EP 03: Responsible AI
Summary
TLDRIn 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
- đ The course is AI-900 Microsoft Azure AI Fundamentals, focusing on responsible AI in module 1.
- đ€ AI is a powerful tool but must be used responsibly to avoid risks like bias, errors, data exposure, and trust issues.
- âïž Fairness: AI systems must treat all people fairly and avoid biases, especially in areas like loan approvals.
- đĄïž Reliability and Safety: AI systems, such as those for autonomous vehicles or medical diagnostics, should be thoroughly tested to ensure they perform reliably.
- đ Privacy and Security: AI systems handle large amounts of personal data, which must be protected to maintain privacy.
- đ Inclusiveness: AI should be designed to empower everyone, ensuring no discrimination based on physical ability, gender, or other factors.
- đ Transparency: AI systems should be understandable, with clear explanations of how they work and their limitations.
- đ„ Accountability: Developers must be accountable for their AI systems, ensuring they adhere to legal and ethical standards.
- đ Microsoft follows six principles of responsible AI: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
- đĄ 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
đ 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.
đĄïž Microsoft's Six Responsible AI Principles
This section delves into Microsoftâs 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.
đ Exploring Microsoftâs 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.
Mindmap
Keywords
đĄArtificial Intelligence (AI)
đĄBias
đĄFairness
đĄReliability and Safety
đĄPrivacy and Security
đĄInclusiveness
đĄTransparency
đĄAccountability
đĄHuman-AI Interaction
đĄResponsible AI
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
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