Responsible AI
Transparency Report
How we build, support
our customers, and growMay 2024

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Contents
Foreword

3

Key takeaways

4

How we build generative applications responsibly

5

Govern: Policies, practices, and processes

6

Map: Identifying risks

9

Measure: Assessing risks and mitigations
Manage: Mitigating AI risks

How we learn, evolve, and grow

26

Governance of responsible AI at Microsoft:
Growing our responsible AI community

27

Building safe and responsible frontier models
through partnerships and stakeholder input

30

10Using consensus-based safety frameworks

32

11

Supporting AI research initiatives

33

16

Investing in research to advance the state of the art in responsible AI

34

Deployment safety for generative AI applications

17

Tuning in to global perspectives

35

Sensitive Uses program in the age of generative AI

20

Looking ahead

36

How we support our customers in building responsibly

22

Sources and resources37

How we make decisions about releasing generative applications

AI Customer Commitments

23

Tools to support responsible development

24

Transparency to support responsible development and use by our customers

25

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FOREWORDM
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Foreword

In 2016, our Chairman and CEO,Satya Nadella, set us on a clear
course to adopt a principled andhuman-centered approach to ourinvestments in Artificial Intelligence(AI). Since then, we have been hard
at work to build products that alignwith our values. As we design, build,and release AI products, six values––transparency, accountability, fairness,
inclusiveness, reliability and safety,
and privacy and security––remain
our foundation and guide our work
every day.

To advance our transparency practices, in July
2023, we committed to publishing an annual
report on our responsible AI program, taking
a step that reached beyond the White House
Voluntary Commitments that we and otherleading AI companies agreed to. This is ourinaugural report delivering on that commitment,
and we are pleased to publish it on the heels of
our first year of bringing generative AI products
and experiences to creators, non-profits,
governments, and enterprises around the world.
As a company at the forefront of AI researchand technology, we are committed to sharing
our practices with the public as they evolve. This
report enables us to share our maturing practices,reflect on what we have learned, chart our goals,
hold ourselves accountable, and earn the public’s
trust. We’ve been innovating in responsible AI foreight years, and as we evolve our program, welearn from our past to continually improve. We
take very seriously our responsibility to not only
secure our own knowledge but to also contributeto the growing corpus of public knowledge,
to expand access to resources, and promotetransparency in AI across the public, private, andnon-profit sectors.
In this inaugural annual report, we provide
insight into how we build applications that use
generative AI; make decisions and oversee thedeployment of those applications; support ourcustomers as they build their own generative
applications; and learn, evolve, and grow asa responsible AI community. First, we provide
insights into our development process, exploring
how we map, measure, and manage generative
AI risks. Next, we offer case studies to illustrate
how we apply our policies and processes togenerative AI releases. We also share details
about how we empower our customers as theybuild their own AI applications responsibly. Lastly,
we highlight how the growth of our responsible
AI community, our efforts to democratize thebenefits of AI, and our work to facilitate AI
research benefit society at large.

There is no finish line for responsible AI. And
while this report doesn’t have all the answers,
we are committed to sharing our learnings early
and often and engaging in a robust dialogue
around responsible AI practices. We invite thepublic, private organizations, non-profits, andgoverning bodies to use this first transparency
report to accelerate the incredible momentum
in responsible AI we’re already seeing aroundthe world.

Brad Smith
Vice Chair & President

Natasha Crampton
Chief Responsible AI Officer

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Key takeaways
In this report, we share how we build
generative applications responsibly,
how we make decisions about
releasing our generative applications,
how we support our customers as
they build their own AI applications,and how we learn and evolve our
responsible AI program.
These investments, internal and external,
continue to move us toward our goal—
developing and deploying safe, secure, andtrustworthy AI applications that empower people.

We created a new approach for governing
generative AI releases, which builds on our
Responsible AI Standard and the National
Institute of Standards and Technology’s AI
Risk Management Framework. This approachrequires teams to map, measure, and manage
risks for generative applications throughout
their development cycle.

33
We’ve published 33 Transparency Notes
since 2019 to provide customers with detailed
information about our platform services like
Azure OpenAI Service.

30

We continue to participate in and learn froma variety of multi-stakeholder engagementsin the broader responsible AI ecosystem
including the Frontier Model Forum, thePartnership on AI, MITRE, and the National
Institute of Standards and Technology.

We’ve launched 30 responsible AI tools that
include more than 100 features to support
customers’ responsible AI development.

We support AI research initiatives such as
the National AI Research Resource and fundour own Accelerating Foundation Models
Research and AI & Society Fellows programs.
Our 24 Microsoft Research AI & Society Fellows
represent countries in North America, Eastern
Africa, Australia, Asia, and Europe.

16.6%
In the second half of 2023, we grew
our responsible AI community from
350 members to over 400 members,
a 16.6 percent increase.

99%
We’ve invested in mandatory training for
all employees to increase the adoption of
responsible AI practices. As of December 31,
2023, 99 percent of employees completed
the responsible AI module in our annual
Standards of Business Conduct training.

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1.

Section 1.

How we build generative
applications responsibly

AI is poised to shape the future.
Generative AI—artificial intelligencemodels and applications capable ofcreating original content, including
text, image, and audio—has
accelerated this transformation.

At Microsoft, we recognize our role in shaping
this technology. We have released generative
AI technology with appropriate safeguards at ascale and pace that few others have matched.
This has enabled us to experiment, learn, andhone cutting-edge best practices for developing
generative AI technologies responsibly. As always,
we are committed to sharing our learnings as
quickly as possible, and generative AI is no
exception.

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In 2023, we regularly published resources to share
best practices for developing generative applications
responsibly. These include an overview of
responsible AI practices for OpenAI models available
through Azure OpenAI Service,1 a Transparency
Note2 describing how to deploy Azure OpenAI
models responsibly, examples relevant to generative
AI in the HAX toolkit,3 best practices4 and a casestudy5 for red teaming large language model (LLM)
applications, and system message—or metaprompt—guidance.6 In March 2024, we released additional
tools our customers can use to develop generative
applications more responsibly. This includes prompt
shield to detect and block prompt injection attacks,7safety evaluation in Azure AI Studio to evaluate
AI-generated outputs for content risks,8 and risks &safety monitoring in Azure OpenAI Service to detectmisuse of generative applications.9

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Govern: Policies, practices,and processes
Govern, map, measure, manage: An iterative cycle

In the following sections, we outline some of ourrecent innovations to map, measure, and manage
risks associated with generative AI.

3 Then, we discuss the role of systematic
measurement and how it provides metrics that
inform decision making.
4 Finally, we describe some of our approaches
to managing generative AI risks. This includes
using technology to reinforce trust in democratic
processes and manage generative AI’s impact
on the information ecosystem by implementing
provenance tools to label AI-generated content.

Putting responsible AI into practicebegins with our Responsible AI
Standard. The Standard details
how to integrate responsible AI
into engineering teams, the AI
development lifecycle, and tooling.

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2 Next, we discuss how AI red teaming plays animportant role in mapping generative AI risks at
the model and application layers.

Identify andprioritize AI risks

P

1 First, we cover specific requirements forgenerative applications, based on our
Responsible AI Standard.

Map

GovernIn 2023, we used our Responsible AI Standard
to formalize a set of generative AI requirements,
which follow a responsible AI development
cycle. Our generative AI requirements alignwith the core functions of the National Institutefor Standards and Technology (NIST) AI Risk
Management Framework—govern, map,
measure, and manage—with the aim of reducing
generative AI risks and their associated harms.

ManageManage or
mitigate
identified risks

Align roles and responsibilities
and establish requirements for
safe, secure, and trustworthy
AI deployment

MeasureSystematically measureprioritized risks toassess prevalence andthe effectiveness of
mitigations

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GovernGovernance contextualizes the map, measure,and manage processes. We’ve implemented
policies and practices to encourage a culture ofrisk management across the development cycle.
Policies and principles: Our generative
applications are designed to adhere tocompany policies, including our responsible
AI, security, privacy, and data protection
policies. We update these policies as needed,
informed by regulatory developments
and feedback from internal and external
stakeholders.
Procedures for pre-trained models:
For the use of pre-trained generative
AI models, teams must review available
information about the model, its capabilities,
and its limitations, then map, measure,and manage relevant risks.
Stakeholder coordination: Our policies,
programs, and best practices include input
from a diverse group of internal and external
stakeholders. Cross-functional teams work
together to map, measure, and manage risks
related to generative applications.
Documentation: We provide transparencymaterials to customers and users that explain
the capabilities and limitations of generative
applications, as well as guidelines to help
them use generative applications responsibly.

1.

Pre-deployment reviews: We require teams
to map, measure, and manage generative AI
risks pre-deployment and throughout theirdevelopment cycle. This includes identifying
high-impact uses of generative AI for
additional review by experts within
the company.

Map
Mapping risks is a critical first—and iterative—
step toward measuring and managing risks
associated with AI, including generative AI.
Mapping informs decisions about planning,
mitigations, and the appropriateness of agenerative application for a given context.
Responsible AI Impact Assessments:
The development of generative applications
begins with an impact assessment as requiredby the Responsible AI Standard. The impact
assessment identifies potential risks and theirassociated harms as well as mitigations toaddress them.
Privacy and security reviews: Processes for
identifying and analyzing privacy and security
risks, like security threat modeling, inform aholistic understanding of risks and mitigations
for generative applications.
Red teaming: We conduct red teaming ofgenerative AI models and applications todevelop a deeper understanding of how
the identified risks manifest and to identify
previously unknown risks.

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MeasureWe’ve implemented procedures to measure
AI risks and related impacts to inform how wemanage these considerations when developing
and using generative applications.
Metrics for identified risks: We haveestablished metrics to measure identified
risks for generative applications.
Mitigations performance testing:
We measure how effective mitigations
are in addressing identified risks.

ManageWe manage or mitigate identified risks at theplatform and application levels. We also work
to safeguard against previously unknown risks
by building ongoing performance monitoring,
feedback channels, processes for incident
response, and technical mechanisms for rolling
applications back. Finally, we release andoperate the application. We’ve learned that acontrolled release to a limited number of users,
followed by additional phased releases, helps us
map, measure, and manage risks that emerge
during use. As a result, we can be confident theapplication is behaving in the intended way
before a wider audience accesses it.

User agency: We design our generative
applications to promote user agency andresponsible use, such as through userinterfaces that encourage users to edit andverify AI-generated outputs.

Transparency: We disclose the role ofgenerative AI in interactions with users
and label AI-generated visual content.
Human review and oversight: We design
generative applications so that users canreview outputs prior to use. Additionally,
we notify users that the AI-generated
outputs may contain inaccuracies and thatthey should take steps to verify information
generated by the tool.
Managing content risks: We build generative
applications to address potential content
risks, such as by incorporating content filtersand processes to block problematic prompts
and AI-generated outputs.
Ongoing monitoring: Our teams also
implement processes to monitor performanceand collect user feedback to respond when
our applications don’t perform as expected.
Defense in depth: We use an approach torisk management that puts controls at every
layer of the process, including platform- andapplication-level mitigations.

We map, measure, andmanage generative AI
risks throughout thedevelopment cycle toreduce the risk of harm.

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Because there is no finish line for responsible
AI, our framework is iterative. Teams repeat
processes to govern, map, measure, andmanage AI-related risks throughout the productdevelopment and deployment cycle.
As we expand and evolve our responsible AI
program, each new improvement builds onthe foundation of the Responsible AI Standard.
For example, we recently updated our Security
Development Lifecycle (SDL) to integrate
Responsible AI Standard governance steps. We
also enhanced internal guidance for our SDL
threat modeling requirement, which integrates
ongoing learnings about unique threats specificto AI and machine learning (ML).10 Incorporating
responsible AI requirements into existing security
guidance embodies our unified approach todeveloping and deploying AI responsibly.

Threat modeling is key
to mapping potential
vulnerabilities, enabling
us to measure andmanage risks, andclosely evaluate theimpacts of mitigations.

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Evolving our cybersecurity development cycle in the new age of AI
We’ve developed and deployed technology
using our state-of-the-art cybersecurity
practices for decades. In our efforts to develop
robust and secure AI infrastructure, we build
on our extensive cybersecurity experience andwork closely with cybersecurity teams fromacross the company. Our holistic approachis based on thorough governance to shieldAI applications from potential cyberattacks
across multiple vectors. Defense strategies
include governance of AI security policies
and practices; identification of potential risks
in AI applications, data, and supply chains;protection of applications and information;
detection of AI threats; and response andrecovery from discovered AI issues andvulnerabilities, including through rapid
containment. We take valuable learnings
from these strategies, customer feedback,and external researcher engagement tocontinuously improve our AI security bestpractices.
All Microsoft products are subject to Security
Development Lifecycle (SDL) practices andrequirements.11 Teams must execute threat
modeling to map potential vulnerabilities,
measure and manage risks, and closely
evaluate the impacts of mitigations. Central
engineering teams and our Digital Security
and Resilience team facilitate and monitor
SDL implementation to verify complianceand secure our products. Additional layers of
security include centralized and distributed
security engineering, physical security, and threat
intelligence. Security operations teams drive
implementation and enforcement.
We synthesize and organize learnings about AI
threats into security frameworks, such as:
The Adversarial Machine Learning Threat
Matrix, which we developed with MITRE
and others.12
Our Aether13 Security Engineering
Guidance, which added AI-specific threat
enumeration and mitigation guidance toexisting SDL threat modeling practices.14
Our AI bug bar, which provides a severity
classification for vulnerabilities that
commonly impact AI and ML applications.15
Further, we apply SDL protection, detection,and response requirements to AI technology.
Specifically for our products that leverage pretrained models, model weights are encryptedat-rest and encrypted-in-transit to mitigate
the potential risk of model theft. We apply
more stringent security controls for highrisk technology, such as for protecting highly
capable models. For example, in our AI productenvironments where highly capable proprietary
AI models are deployed, we employ strong

identity and access control. We also use
holistic security monitoring (for both external
and internal threats) with rapid incident
response and continuous security validation(such as simulated attack path analysis).

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Map: Identifying risks
As part of our overall approachto responsible development
and deployment, we identify AI
risks through threat modeling,16
responsible AI impact assessments,17
customer feedback, incident response
and learning programs, external
research, and AI red teaming. Here,
we discuss our evolving practice of AI
red teaming.

Red teaming, originally defined as simulating
real-world attacks and exercising techniquesthat persistent threat actors might use, haslong been a foundational security practice at
Microsoft.18 In 2018, we established our AI Red
Team. This group of interdisciplinary experts
dedicated to thinking like attackers and probing
AI applications for failures19 was the first
dedicated AI red team in industry.20 Recently,
we expanded our red teaming practices to map
risks outside of traditional security risks, including
those associated with non-adversarial users andthose associated with responsible AI, like thegeneration of stereotyping content. Today, the AI
Red Team maps responsible AI and security risks
at the model and application layers:

Red teaming models. Red teaming themodel helps to identify how a model canbe misused, scope its capabilities, andunderstand its limitations. These insights not
only guide the development of platform-levelevaluations and mitigations for use of themodel in applications but can also be used to
inform future versions of the model.
Red teaming applications. Applicationlevel AI red teaming takes a system view,of which the base model is one part. This
helps to identify failures beyond just themodel, by including the application specificmitigations and safety system. Red teaming
throughout AI product development cansurface previously unknown risks, confirm
whether potential risks materialize in an
application, and inform measurement and risk
management. The practice also helps clarify
the scope of an AI application’s capabilities
and limitations, identify potential for misuse,and surface areas to investigate further.
For generative applications we characterize as
high-risk, we implement processes to ensure
consistent and holistic AI red teaming by experts
independent from the product team developing
the application. We are also building external
red teaming capacity to enable third-party

testing before releasing highly capable models,consistent with our White House Voluntary
Commitments.21 Externally led red teaming forhighly capable models will cover particularly
sensitive capabilities, including those related tobiosecurity and cybersecurity.

In 2018

We established the first dedicated AI red
team in industry.

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Measure: Assessing risks
and mitigations

After mapping risks, we use
systematic measurement
to evaluate application andmitigation performance against
defined metrics. For example, we
can measure the likelihood of ourapplications to generate identified
content risks, the prevalence of
those risks, and the efficacy of our
mitigations in preventing thoserisks. We regularly broaden our
measurement capabilities. Some
examples include:22

Groundedness, to measure how well an
application’s generated answers align withinformation from input sources.
Relevance, to measure how directly pertinent
a generated answer is to input prompts.

Safety evaluations in Azure
AI Studio

We also share capabilities and tools that support
measurement of responsible AI concepts anddevelopment of new metrics. We share some ofthese tools as open source on GitHub and withour customers via Azure AI, which includes Azure
Machine Learning and Azure AI Studio.

Similarity, to measure the equivalence
between information from input sources anda sentence generated by an application.
Content risks, multiple metrics through which
we measure an application’s likelihood to
produce hateful and unfair, violent, sexual,
and self-harm related content.
Jailbreak success rate, to measure anapplication’s resiliency against direct andindirect prompt injection attacks that may
lead to jailbreaks.

Azure AI
Content Safety
uses advanced language and visionmodels to help detect content risks suchas hateful, sexual, violent, or self-harm
related content.

Many generative applications are built
on top of large language models, which
can make mistakes, generate content
risks, or expose applications to othertypes of attacks. While risk management
approaches such as safety systemmessages and content filters are agreat start, it’s also crucial to evaluate
applications to understand if the
mitigations are performing as intended.
With Azure AI Studio safety evaluations,customers can evaluate the outputsof generative applications for content
risks such as hateful, sexual, violent, or
self-harm related content. Additionally,
developers can evaluate their applications
for security risks like jailbreaks. Since
evaluations rely on a robust test dataset,
Azure AI Studio can use prompt templates
and an AI-assisted simulator to create
adversarial AI-generated datasetsto evaluate generative applications.
This capacity harnesses learning andinnovation from Microsoft Research,developed and honed to support thelaunch of our own first-party Copilots, andis now available to customers in Azure aspart of our commitment to responsible
innovation.

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Manage: Mitigating AI risks
Once risks have been mapped andmeasured, they need to be managed.
We evaluate and improve ourgenerative AI products across twolayers of the technology to provide
a defense in depth approach to
mitigating risks.
1 Platform: Based on the product’s intended
use, model-level mitigations can guide theapplication to avoid potential risks identified
in the mapping phase. For example, teams
can experiment with and fine-tune different
versions of many generative AI models to
see how potential risks surface differently
in their intended use. This experimentation
allows teams to choose the right model for
their application. In addition, platform-levelsafety measures such as content classifiers
reduce risks by blocking potentially harmfuluser inputs and AI-generated content. For
example, Azure AI Content Safety provides
API-level filters for content risks. Harmful user
input or content risks generated by the AI
model will be blocked when flagged by Azure
AI Content Safety.

2 Application: A number of mitigations
implemented in a specific application canalso further manage risks. For example,
grounding a model’s outputs with input data
alongside safety system messages to limit
the model within certain parameters helps
the application align with our responsible AI
Standard and user expectations. For example,
a safety system message guides Microsoft
Copilot in Bing to respond in a helpful tone
and cite its sources.23 Additionally, usercentered design is an essential aspect of ourapproach to responsible AI. Communicating
what the technology is and is not intended
to do shows the application’s potential,communicates its limitations, and helps
prevent misuse. For example, we include inproduct disclosures of AI-generated content
in our Copilots, FAQs on responsible AI for
our applications like GitHub Copilot,24 andTransparency Notes for our platform products
such as Azure OpenAI Service.25
As part of our commitment to build responsibly
and help our customers do so as well, we
integrate content filtering across Azure OpenAI
Service.26 We regularly assess our content filteringsystems to improve accuracy and to ensure

they’re detecting as much relevant content as
possible. Over the past year, we expanded our
detection and filtering capabilities to include
additional risk categories, such as jailbreaks,
and improved the performance of our text,
image, multimodal, and jailbreak models. These
improvements rely on expert human annotatorsand linguists who evaluate offline evaluation sets.
We also anonymously sample online traffic tomonitor for regressions while leveraging the atscale annotation capabilities of OpenAI’s GPT-4.
Importantly, we’ve made these detection andevaluation tools available to our customers with
the October 2023 general release of Azure AI
Content Safety. Customers can choose to use
our advanced language and vision models to
help detect hate, violent, sexual, and self-harm
related content, plus added jailbreak protections.
When problematic content is detected, themodels assign estimated severity scores to help
customers efficiently tackle prioritized items
and take action to reduce potential harm.27 The
models are offered in Azure AI Content Safety as
standalone APIs, and customers can configure
the filters to detect content with defined severity
scores to fit their specific goals and policies.

The application of AI in our safety systems
empowers organizations to monitor andsupport product safety at a scale that would be
impossible for humans alone. These same tools
are also offered in Azure AI Studio, Azure OpenAI, and Azure AI Content safety where customers
can discover, customize, and operationalize large
foundation models at scale.

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Limited Access forcustomized service
safety settings

A new jailbreak risk detection model
Because generative AI models haveadvanced capabilities, they can besusceptible to adversarial inputs thatcan result in safety system bypass. These
could provoke restricted behaviors anddeviations from built-in safety instructions
and system messages. This kind ofadversarial technique is called a “jailbreakattack,” also known as a user prompt
injection attack (UPIA). In October 2023,
to increase the safety of large language
model deployments, we released a new
jailbreak risk detection model, now
called prompt shield. Prompt shield was
integrated with existing comprehensive
content safety filtering systems across
Azure OpenAI Service and made available
in Azure AI Content Safety as an API. Whena jailbreak attempt is detected, customers
can choose to take a variety of steps best
suited for their application, such as further
investigations or banning users.

Types of jailbreak attacks
Prompt shield recognizes four different
classes of UPIA.

Category

Description

Attempt to change
application rules

This category includes requests to use a newunrestricted application without rules, principles,or limitations, or requests instructing theapplication to ignore, forget, or disregard rules,
instructions, or previous turns.

Embedding a
conversation mockup to
confuse the modelThis attack takes user-crafted conversationalturns embedded in a single user query to
instruct the application to disregard rules andlimitations.

Role-play

This attack instructs the application to act as
another persona without application limitations
or assigns anthropomorphic human qualities
to the application, such as emotions, thoughts,
and opinions.

Encoding attacks

This attack attempts to use encoding, such as acharacter transformation method, generationstyles, ciphers, or other natural language
variations, to circumvent the system rules.

In March 2024, prompt shield was expanded to include protections against indirect prompt injection
attacks, where a generative application processes malicious information not directly authored by theapplication developer or the user, which can result in safety system bypass.28

Because safety is a priority for us, ourAzure OpenAI Service is offered withdefault content safety settings andsafeguards. Customers must complete
registration under our Limited Accesspolicy framework29 and attest to approved
use cases to gain access to Azure
OpenAI Service. Customized settingsfor content filters and abuse monitoring
are only allowed for approved use cases,
and access to the broadest range ofconfigurability is limited to managed
customers. Managed customers arethose who are working directly in trusted
partnerships with Microsoft accountteams. Managed customers must also
attest their use cases for customized
content filtering and abuse monitoring. All
customers must follow the Azure OpenAI
Service Code of Conduct,30 which outlines
mitigations and content requirements that
apply to all customer uses to support safe
deployment.
In the next section, we use a specificexample of an identified risk—information
integrity risks in the age of generative
AI—to illustrate how we manage risks bycombining technological advancementswith policies and programs.

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Managing information integrity risks
in the age of generative AI
Amid growing concern that AI can make it easierto create and share disinformation, we recognizethat it is imperative to give users a trusted
experience. As generative AI technologies
become more advanced and prevalent, it isincreasingly difficult to identify AI-generated
content. An image, video, or audio clip
generated by AI can be indistinguishable fromreal-world capture of scenes by cameras andother human-created media. As more creators
use generative AI technologies to assist theirwork, the line between synthetic content createdby AI tools and human-created content will
increasingly blur.
Labeling AI-generated content and disclosing
when and how it was made (otherwise known
as provenance) is one way to address thisissue. In May 2023, we announced our intent
to build new media provenance capabilities
that use cryptographic methods to mark andsign AI-generated content with metadata
about its source and history. Since then, we’ve
made significant progress on our commitment
to deploy new state-of-the-art tools to help
the public identify AI-generated audio andvisual content. By the end of 2023, we wereautomatically attaching provenance metadata
to images generated with OpenAI’s DALL-E 3
model in our Azure OpenAI Service, Microsoft
Designer, and Microsoft Paint. This provenancemetadata, referred to as Content Credentials,

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includes important information such as when
the content was created and which organization
certified the credentials.
To apply Content Credentials to our products’
AI-generated images, we use an open technicalstandard developed by the Coalition forContent Provenance and Authenticity (C2PA),
which we co-founded in 2021. The industry has
increasingly adopted the C2PA standard, which
requires cryptographic methods to sign, seal,
and attach metadata to the file with a trusted
identity certificate. This means C2PA Content
Credentials can deliver a high level of trust withinformation that is tamper-evident while alsopreserving privacy. Certification authorities issue
identity certificates to vetted organizations, andindividual sources within those organizations canbe anonymized. The C2PA coalition and standard
body builds on our early efforts to prototype
and develop provenance technologies and ourcollaboration with the provenance initiative
Project Origin,31 which we founded alongside the
British Broadcasting Corporation, the Canadian
Broadcasting Corporation, and the New YorkTimes to secure trust in digital media.
Beyond Microsoft, we continue to advocate forincreased industry adoption of the C2PA standard.
There are now more than 100 industry membersof C2PA. In February 2024, OpenAI announced
that they would implement the C2PA standard forimages generated by their DALL-E 3 image model.
This is in addition to completing pre-deployment
risk mapping and leveraging red-teaming

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practices to reduce potential for harm—anapproach similar to ours.
While the industry is moving quickly to rally
around the C2PA standard, relying on metadatabased provenance or even watermarking
approaches alone will be insufficient. It is
important to combine multiple methods, suchas embedding invisible watermarks, alongside
C2PA Content Credentials and fingerprinting,to help people recover provenance information
when it becomes decoupled from its content.
Additionally, authentication, verification, andother forensic technologies allow people
to evaluate digital content for generative
AI contributions. No disclosure method is
foolproof, making a stacked mitigation approachespecially important.
We continue to test and evaluate combinationsof techniques in addition to new methods
altogether to find effective provenance solutions
for all media formats. For example, text caneasily be edited, copied, and transferred
between file formats, which interferes withcurrent technical capabilities that attach Content
Credentials to a file’s metadata. We remaincommitted to investing in our own research,
sharing our learnings, and collaborating withindustry peers to address ongoing provenanceconcerns.

In May 2023

we announced our intent to build new
media provenance capabilities that use
cryptographic methods to mark and sign
AI-generated content with metadata about
its source and history.

End of 2023

we began to automatically apply Content
Credentials to AI-generated images fromMicrosoft Designer, Microsoft Paint, andDALL-E 3 in our Azure OpenAI Service.

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This work is especially important in 2024, a year
in which more people will vote for their electedleaders than any year in human history. A recordbreaking elections year combined with the fast
pace of AI innovation may offer bad actors newopportunities to create deceptive AI content (alsoknown as “deepfakes”) designed to mislead the
public. To address this risk, we worked with 19
other companies, including OpenAI, to announce
the new Tech Accord to Combat Deceptive
Use of AI in 2024 Elections at the Munich
Security Conference in February 2024.32 These
commitments include advancing provenancetechnologies, innovating robust disclosure
solutions, detecting and responding to deepfakes
in elections, and fostering public awareness andresilience.
Since signing the Tech Accord, we continue
to make progress on our commitments. We
recently launched a portal for candidates toreport deepfakes on our services.33 And in March,
we launched Microsoft Content Integrity tools
in private preview, to help political candidates,
campaigns, and elections organizations
maintain greater control over their content andlikeness. The Content Integrity tools include twocomponents: first, a tool to certify digital content
by adding Content Credentials, and second, tools
to allow the public to check if a piece of digitalcontent has Content Credentials.34
In addition to engaging in external researchcollaborations35 and building technical mitigations,it’s equally important to consider policies,
programs, and investments in the broader
ecosystem that can further manage information
integrity risks associated with generative AI.

1.

We know that false or misleading information
is more likely to spread in areas where there islimited or no local journalism. A healthy media
ecosystem acts as a virtual town square where
people gather reliable information and engage
on the most pressing issues facing society.
We support independent journalism to advancefree, open coverage of important issues on a
local and national scale. Our Democracy Forward
Journalism Initiative provides journalists andH O W W E B U I L D G E N E R AT I V E A P P L I C AT I O N S R E S P O N S I B LY

newsrooms with tools and technology to help
build capacity, expand their reach and efficiency,
distribute trustworthy content, and ultimately
provide the information needed to sustain healthy
democracies.36
In addition to the commitments made in the
Tech Accord, we continue to build on our
existing programs to protect elections andadvance democratic values around the world.

We support the rights of voters, candidates,
political campaigns, and election authorities
through a variety of programs and investments.
These include our partnership with the National
Association of State Election Directors, ourendorsement of the Protect Elections from
Deceptive AI Act in the United States, and ourElections Communications Hub.37

Case Study: Content Credentials for Microsoft Designer
Microsoft Designer allows users to input
text prompts to generate images, such as
the one below which was generated using
the prompt: “people using an AI system forfarming in a corn field.”

Each image, like this one generated by Designer,is automatically marked and signed with
Content Credentials, displayed in the rightpane of the product interface. The Content
Credentials indicate the date and time ofcreation using AI. Because Content Credentials
are cryptographically signed and sealed as partof the image file’s metadata, this information istamper-evident and can be examined with tools
such as Content Authenticity Initiative’s open
source Verify tool38 and our Content Integrity
Check tool.39 Both tools are available as websites
where users can upload files to check Content
Credentials. For example, the image to the rightshows that when examined with Verify or Check,Content Credentials for images generated by
Designer indicate that they were created by a
Microsoft product and the date of generation.
We continue to build provenance capabilities
into our products, including most recently in

the DALL-E 3 series models hosted in Azure
OpenAI Service. AI-generated images from
Azure OpenAI Service using DALL-E 3 now
include provenance information that attachsource and generation date through Content
Credentials.40

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1.

H O W W E B U I L D G E N E R AT I V E A P P L I C AT I O N S R E S P O N S I B LY

Third-party evaluation of Microsoft Designer
The work of AI risk management cannot bedone by companies alone. This is why we arecommitted to learning from stakeholders inacademia, civil society, and government whose
perspectives, evaluations, and concerns we
consider as we build. Below is an example of
how we’ve exercised this commitment throughan external assessment of Microsoft Designer.

Designer is a general use text-to-image
generative AI tool. Its many uses can make itvulnerable to adversarial use, including thegeneration of images that can be used for
information operations. While we can’t control
what happens to images generated by ourapplications once they leave our platform, we
can mitigate known risks at the user input andimage output stages. This is why we’ve put inplace safeguards to restrict what the application
will generate, including deceptive images that
could further information operations.
To better understand the risk of misleading
images, reduce potential harms, and promote
information integrity, we partnered with
NewsGuard to evaluate Designer. NewsGuard
is an organization of trained journalists
that scores news sources for adherence tojournalistic principles. As part of their analysis,
NewsGuard prompted Designer to create
visuals that reinforced or portrayed prominent
false narratives related to politics, international
affairs, and elections. Of the 275 images
created:
Mitigations worked in 88 percent of theattempts and the output images contained
no problematic content.
12 percent of the output images contained
problematic content.
To enhance performance related to
information integrity, we regularly improve
prompt blocking, content filters, and safetysystem message mitigations. Following the
mitigation improvements, we input the same
prompts developed by NewsGuard which had
previously resulted in problematic content andreevaluated the images generated by Designer.

96.4%
of test prompts successfully mitigatedfollowing improvements to the
Designer safety system.
We found that only 3.6 percent of the output
images contained problematic content.
NewsGuard’s analysis and our mitigation
improvements were steps in the right direction,
but there is more work to be done. Evaluating
and managing risks of our generative
applications is an ongoing process andinherently iterative in nature. As new issues
surface—identified by our internal responsible
AI practices, external evaluations, andfeedback submitted by users—we take action
to address them. In addition, the questionof how to build scaled measurement forevaluating information integrity in images isstill an open research question. To address thischallenge, we are excited to announce a newcollaboration with researchers from Princeton'sEmpirical Studies and Conflict Project toadvance this research.

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Section 2.

How we make decisions
about releasing
generative applications

2.

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2.

Deployment safety for
generative AI applications

At each stage of the map, measure,and manage process for generative
AI releases, we’ve built best practices,
guidelines, and tools that reflect
our learnings from the last year of
releasing generative applications.
For example, when teams are asked to evaluate
the potential for generative applications to
produce ungrounded content, they are provided
with centralized tools to measure that risk
alongside patterns and best practices to guidetheir design of specific mitigations for theirgenerative application.
After teams complete their initial analysis, senior
experts review the evaluations and mitigations
and make any further recommendations or
requirements before products are launched.
These reviews ensure that we apply a consistentand high bar to the design, build, and launch of
our generative AI applications. When gaps are
identified, these experts dive deep with productteams and leaders to assess the problems andagree on further mitigations and next steps.
This oversight by senior leadership provides
important touch points throughout a product’s
development cycle to manage risks across thecompany. This process improves each AI productand generates important lessons to apply to ourmap, measure, and manage approach.
As we learn more about how generative AI isused, we continue to iterate on our requirements,
review processes, and best practices.
In this section, we share what we’ve learned
through two examples. These short case studies
demonstrate how we’ve improved our products
through our work to map, measure, and manage
risks associated with generative AI.

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Our collaboration
with OpenAI
While we often build and deploy stateof-the-art AI technology, we also partnerwith other companies building advanced
AI models, including OpenAI.
Our most recent agreement with OpenAI41
extends our ongoing collaborations in
AI supercomputing and research. The
agreement also allows both OpenAI andMicrosoft to independently commercialize
any advanced technologies developed
by OpenAI. We deploy OpenAI’s models
across several consumer and enterpriseproducts, including Azure OpenAI Service,
which enables developers to build
cutting-edge AI applications through
OpenAI models with the benefit of Azure’s
enterprise-grade capabilities.
Microsoft and OpenAI share acommitment to building AI systems
and products that are trustworthy
and safe. OpenAI’s leading research
on AI Alignment,42 their preparedness
framework,43 and our Responsible AI
Standard establish the foundation forthe safe deployment of our respective AI
technologies and help guide the industry
toward more responsible outcomes.

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2.

Case Study: Safely deploying Copilot Studio
In 2023, we released Copilot Studio, which
harnesses generative AI to enable customers
without programming or AI skills to build theirown copilots.44 Natural language processing
enables the cloud-based platform to interpret
customer prompts and create interactive
solutions that customers can then deploy totheir users. It also enables customers to test,
publish, and track the performance of copilots
within the platform so they remain in controlof the experience. As with all generative
applications, the Copilot Studio engineering
team mapped, measured, and managed risks
according to our governance framework prior
to deployment.
Map. As part of their process, the engineering
team mapped key risks associated withthe product in their Responsible AI Impact
Assessment as well as security and privacy
reviews, including the potential for copilots
to provide ungrounded responses touser prompts.
Measure and Manage. The Copilot Studioteam worked with subject matter experts tomeasure and manage key risks iteratively
throughout the development and deployment
process. To mitigate AI-generated content
risks, the Copilot Studio team included safetysystem message mitigations and leveraged
Azure OpenAI Service’s content filtering
capabilities to direct copilots to generate only

acceptable content. One of the key risks forthis product is groundedness, or potential for
AI-generated output to contain informationthat is not present in the input sources. By
improving groundedness mitigations throughmetaprompt adjustments, the Copilot Studioteam significantly enhanced in-domain query
responses, increasing the in-domain pass ratefrom 88.6 percent to 95.7 percent. This meansthat when a user submits a question that is
in-domain—or topically appropriate—copilots
built with Copilot Studio are able to respond
more accurately. This change also resulted ina notable 6 percent increase in answer rate
within just one week of implementation. In otherwords, the improved groundedness filtering
also reduced the number of queries that
copilots declined to respond to, improving theoverall user experience.
The team also introduced citations, so copilot
users have more context on the source of
information included in AI-generated outputs.
By amending the safety system message andutilizing content filters, the Copilot Studio team
improved citation accuracy from 85 percent to
90 percent.
Following the map, measure, and manage
framework and supported by robust
governance processes, the Copilot Studio teamlaunched an experience where customers canbuild safer and more trustworthy copilots.

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Case Study: Safely deploying GitHub Copilot
GitHub Copilot is an AI-powered tool designed
to increase developer productivity through avariety of features, including code suggestions
and chat experience to ask questions aboutcode.45 Code completion is a feature that runs in
the integrated development environment (IDE),
providing suggested lines of code as developers
work on projects. GitHub Copilot Chat canbe used in different environments, including
the IDE and on GitHub.com, and provides aconversational interface for developers to ask
coding questions. GitHub Copilot runs on avariety of advanced Microsoft and OpenAI
technologies, including OpenAI’s GPT models.
In developing the features for GitHub Copilot,
the team worked with their Responsible AI
Champions—responsible AI experts within theirorganization—to map, measure, and manage
risks associated with using generative AI in thecontext of coding.
Map. The team completed their Responsible AI
Impact Assessment as well as security and privacy
reviews to map different risks associated with the
product. These risks included 1) the generationof code that may appear valid but may not
be semantically or syntactically correct; 2) thegeneration of code that may not reflect the intent
of the developer; and 3) more fundamentally,
whether GitHub Copilot was actually increasing

developer productivity. The last category,generally referred to as fitness for purpose,is an important concept for establishing thatan AI application effectively addresses the
problem it’s meant to solve.
Measure. In addition to assessing performance,
like the quality of responses, and runningmeasurement sets to evaluate risks like insecurecode or content risks, the GitHub Copilot team
set out to understand if Copilot improved
developer productivity. Research on how 450
developers at Accenture used the GitHub
Copilot code completion feature over six
months found that:46
94 percent of developers reported thatusing GitHub Copilot helped them remainin the flow and spend less effort on
repetitive tasks.
90 percent of developers spent less time
searching for information.
90 percent of developers reported writing
better code with GitHub Copilot.
95 percent of developers learned from
Copilot suggestions.

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Case Study: Safely deploying GitHub Copilot, cont.

In a follow-on study of GitHub Copilot
Chat, the team saw similar improvements
in productivity.47 In this study, researchers
recruited 36 participants that had between
five and ten years of coding experience.
Participants were asked to a) write code,
being randomly assigned to use or not use
GitHub Copilot Chat and b) review code,
being randomly assigned to review code that
was authored with assistance from GitHub
Copilot Chat or not. The researchers createda framework to evaluate code quality, asking
participants to assess if code was readable,
reusable, concise, maintainable, and resilient.
In analyzing the data, researchers found that:85 percent of developers felt more
confident in their code quality whenauthoring code with GitHub Copilot and
GitHub Copilot Chat.
Code reviews were more actionable andcompleted 15 percent faster than without
GitHub Copilot Chat.
88 percent of developers reported
maintaining flow state with GitHub Copilot
Chat because they felt more focused, lessfrustrated, and enjoyed coding more.

This research indicates that not only is GitHub
Copilot making developers more productive,it also increases developer satisfaction.
Manage. As we’ve shared throughout the report,risks often need to be mitigated at multiple
levels, and mitigations often work to manage
multiple risks.
Human oversight. Responsible AI is ashared responsibility, and our goal is toempower users to use GitHub Copilot ina safe, trustworthy, and reliable way. To
support developer oversight of AI-generated
code, GitHub Copilot was designed to offer
suggested lines of code, which a developer
reviews and accepts. In GitHub Copilot
Chat, developers can review and copy codesuggestions generated in the chat window
into their coding environment. Importantly,
the developer remains in control of the code
they’re writing.48
Staying on topic. While the models behind
GitHub Copilot can generate a wide rangeof content, one key approach to mitigate
AI-generated content risks is to keep
conversations limited to coding. The GitHub
Copilot team built a classifier to reduce thenumber of off-topic conversations on theplatform to keep conversations on topic

and to protect users. In addition to the offtopic classifier, GitHub Copilot runs a varietyof content filters, including to block content
related to self-harm, violence, and hate speech.
Transparency. The transparency
documentation we provide on our GitHub
Copilot features provides developers with

important information about how best to
use the features responsibly.49,50 We also
bake transparency right into the experience.
GitHub Copilot discloses that codesuggestions are AI-generated and may
contain mistakes, empowering developers
to make informed decisions about how best
to use GitHub Copilot features.

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2.

H O W W E M A K E D E C I S I O N S A B O U T R E L E A S I N G G E N E R AT I V E A P P L I C AT I O N S

Sensitive Uses program in
the age of generative AI

The generative AI release processintegrates with existing responsible AI
programs and processes, such as ourSensitive Uses program, established in2017 to provide ongoing review andoversight of high-impact and higherrisk uses of AI.
Employees across the company must report
AI uses to our Sensitive Uses program for indepth review and oversight if the reasonably
foreseeable use or misuse of AI could have aconsequential impact on an individual’s legal
status or life opportunities, present the risk ofsignificant physical or psychological injury, or
restrict, infringe upon, or undermine the ability torealize an individual’s human rights. Particularly
high-impact use cases are also brought before
our Sensitive Uses Panel. Professionals fromacross our research, policy, and engineeringorganizations with expertise in human rights,
social science, privacy, and security lend theirexpertise to the Sensitive Uses team and theSensitive Uses Panel to help address complex
sociotechnical issues and questions.
After review and consultation, the Sensitive Uses
team delivers directed, concrete guidance andmitigation requirements tailored to the project.
Since 2019, the Sensitive Uses team has received
over 900 submissions, including 300 in 2023
alone. In 2023, nearly 70 percent of cases wererelated to generative AI.
The increase in generative AI cases led to new
insights about emerging risks, such as thecapability of generative applications to makeungrounded inferences about a person. In somescenarios, our teams observed that a chatbot
could provide realistic sounding but incorrect
responses to questions that were outside thescope of their grounding data. Dependingon the context, these ungrounded responses
could misattribute actions or information about
individuals or groups.

For example, a chatbot designed to answer
questions about workplace benefits shouldn’t
answer questions about employee performance
when that information is not included in the
grounding data. Some of the mitigations thatcan prevent ungrounded inferences include
safety system message provisions to guide whichquestions chatbots should respond to, ensuringthat application responses are grounded in theright source data, and isolating private data.
Once risks are assessed by the Sensitive Uses
team, guidance is given to product teams on the
mitigations for the use case.

900
submissions have been received by theSensitive Uses team since 2019, including
300 in 2023 alone.

Nearly 70%
of cases in 2023 were related togenerative AI.

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2.

Sensitive Uses in action: Microsoft Copilot for Security
One example of a product that underwent
Sensitive Uses review is Copilot for Security,51an AI-powered tool that helps security
professionals respond to threats and assess
risk exposure faster and more accurately.
Copilot for Security uses generative AI toinvestigate analysts’ digital environments,
flag suspicious activity or content, andimprove analysts’ response to incidents.
It generates natural language insights andrecommendations from complex data,
which helps analysts catch threats they
may have otherwise missed and helps
organizations potentially prevent anddisrupt attacks at machine speed.
Through a Responsible AI Impact Assessment
and with the support of their Responsible
AI Champion, the Copilot for Security team
identified that the project could meet thethreshold for Sensitive Uses. They submitted
the project to the Sensitive Uses programas part of early product development. The
Sensitive Uses team confirmed that Microsoft
Copilot for Security met the criteria for a
Sensitive Uses review. They then worked
with the product team and Responsible AI
Champion to map key risks associated withthe product, including that analysts could be
exposed to potential content risks as part oftheir work.

The team landed on an innovative approachto address risks, which, due to the nature ofroutine security work, are different than thosefor consumer solutions. For example, security
professionals may encounter offensive content
or malicious code in source information. To allow
analysts to stay in control of when they encounter
potentially harmful content, the team made surethat Microsoft Copilot for Security surfaces theserisks when requested by the security professionals.
While Microsoft Copilot for Security suggests next
steps, analysts ultimately decide what to do basedon their organization’s unique needs.
The Copilot for Security team worked closelywith subject matter experts to validate theirapproach and specific mitigations. They improved
in-product messaging to avoid overreliance on
AI-generated outputs. They also refined metrics
for grounding to improve the product’s generated
content. Through required ongoing monitoring of
the product over the course of its phased releases,
the team triaged and addressed responsible AI
issues weekly.
This process led to a more secure, transparent,
and trustworthy generative AI product thatempowers security professionals to protect
their organizations and customers, furthering
our pursuit of the next generation of
cybersecurity protection.52

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R E S P O N S I B L E A I T R A N S PA R E N C Y R E P O R T

Section 3.

How we support ourcustomers in building
AI responsibly
In addition to building our own AI
applications responsibly, we empower
our customers with responsible AI
tools and features. We invest in ourcustomers’ responsible AI goals inthree ways:

1

We stand behind our customers’ deployment
and use of AI through our AI Customer
Commitments.

2

We build responsible AI tools for ourcustomers to use in developing their own AI
applications responsibly.

3

We provide transparency documentation tocustomers to provide important information
about our AI platforms and applications.

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R E S P O N S I B L E A I T R A N S PA R E N C Y R E P O R T

AI Customer
Commitments
In June 2023, we announced our AI
Customer Commitments,53 outlining
steps to support our customers on
their responsible AI journey.
We recognize that ensuring the right guardrails
for the responsible use of AI will not be limited
to technology companies and governments.
Every organization that creates or uses
AI applications will need to develop andimplement governance systems. We made thefollowing promises to our customers:
We created an AI Assurance Program
to help customers ensure that the AI
applications they deploy on our platforms
meet the legal and regulatory requirements
for responsible AI. This program includes
regulator engagement support, alongwith our promise to attest to how we are
implementing the NIST AI Risk Management
Framework.
We continue to engage with customer
councils, listening to their views on how we
can deliver the most relevant and compliant
AI technology and tools.

We created a Responsible AI Partner
program for our partner ecosystem and 11
partners have joined the program so far.
These partners have created comprehensive
practices to help customers evaluate, test,
adopt, and commercialize AI solutions.54
We announced, and later expanded, theCustomer Copyright Commitment55 in which
Microsoft will defend commercial customers
who are sued by a third party for copyright
infringement for using Azure OpenAI Service,
our Copilots, or the outputs they generate
and pay any resulting adverse judgments
or settlements, as long as the customer met
basic conditions such as not attempting to
generate infringing content and using ourrequired guardrails and content filters.56
Ultimately, we know that these commitments are
an important start, and we will build on them asboth the technology and regulatory conditions
evolve. We are excited by this opportunity topartner more closely with our customers as we
continue on our responsible AI journey together.

11
partners have joined since we created the Responsible AI Partner program.

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R E S P O N S I B L E A I T R A N S PA R E N C Y R E P O R T

Tools to support responsible
development
To empower our customers, we’vereleased 30 responsible AI toolsthat include more than 100 features
to support customers’ responsible
AI development. These tools work
to map and measure AI risks andmanage identified risks with novel
mitigations, real-time detection andfiltering, and ongoing monitoring.
Tools to map and measure risks
We are committed to developing tools andresources that enable every organization tomap, measure, and manage AI risks in theirown applications. We’ve also prioritized making
responsible AI tools open access. For example,
in February 2024, we released a red teaming
accelerator, Python Risk Identification Tool for
generative AI (PyRIT).57 PyRIT enables security
professionals and machine learning engineers
to proactively find risks in their generative
applications. PyRIT accelerates a developer’s
work by expanding on their initial red teaming
prompts, dynamically responding to AIgenerated outputs to continue probing forcontent risks, and automatically scoring outputs
using content filters. Since its release on GitHub,
PyRIT has received 1,100 stars and been copied
more than 200 times by developers for use in
their own repositories where it can be modified to
fit their use cases.
After identifying risks with a tool like PyRIT,
customers can use safety evaluations in Azure AI
Studio to conduct pre-deployment assessmentsof their generative application’s susceptibility togenerate low-quality or unsafe content, as well as
to monitor trends post-deployment. For example,
in November 2023 we released a limited set ofgenerative AI evaluation tools in Azure AI Studioto allow customers to assess the quality andsafety of their generative applications.58
The first pre-built metrics offered customers aneasy way to evaluate their applications for basic
generation quality metrics such as groundedness,
which measures how well the model’s generated
answers align with information from the input
sources. In March 2024, we expanded ourofferings in Azure AI Studio to include AI-assisted
evaluations for safety risks across multiple
content risk categories such as hate, violence,
sexual, and self-harm, as well as content that
may cause fairness harms and susceptibility to
jailbreak attacks.59

Recognizing that evaluations are most effective
when iterative and contextual, we’ve continued
to invest in the Responsible AI Toolbox (RAI
Toolbox).60 This open-source tool, which isalso integrated with Azure Machine Learning,offers support for computer vision and natural
language processing (NLP) scenarios. The RAI
Toolbox brings together a variety of model
understanding and assessment tools such as
fairness analysis, model interpretability, error
analysis, what-if exploration, data explorations,and causal analysis. This enables ML professionals
to easily flow through different stages of model
debugging and decision-making. As an entirely
customizable experience, the RAI Toolbox
can be deployed for various functions, suchas holistic model or data analysis, comparing
datasets, or explaining individual instances ofmodel predictions. On GitHub, the RAI Toolbox
has received 1,200 stars with more than 4,700
downloads per month.

Tools to manage risks
Just as we measure and manage AI risks acrossthe platform and application layers of ourgenerative products, we empower our customers
to do the same. For example, Azure AI Content
Safety helps customers detect and filter harmfuluser inputs and AI-generated content in theirapplications. Importantly, Azure AI Content
Safety provides options to detect content risks
along multiple categories and severity levels
to enable customers to configure settings to fit
specific needs. Another example is our systemmessage framework and templates, which
support customers as they write effective systemmessages—sometimes called metaprompts—
which can improve performance, align generative
application behavior with customer expectations,and help mitigate risks in our customers'
applications.61
In October 2023, we made Azure AI Content
Safety generally available. Since then, we’ve
continued to expand its integration across ourcustomer offerings, including its availability in
Azure AI Studio, a developer platform designed
to simplify generative application development,
and across our Copilot builder platforms, such as
Microsoft Copilot Studio. We continue to expand
customer access to additional risk management
tools that detect risks unique to generative
AI models and applications, such as prompt
shield and groundedness detection. Prompt
shield detects and blocks prompt injection
attacks, which bad actors use to insert harmful
instructions into the data processed by large
language models.62 Groundedness detection
finds ungrounded statements in AI-generated
outputs and allows the customer to implement
mitigations such as triggering rewrites of
ungrounded statements.63
In March 2024, we released risks & safety
monitoring in Azure OpenAI Service, which
provides tools for real-time harmful content
detection and mitigation, offering insights into
content filter performance on actual customer
traffic and identifying users who may be abusing
a generative application.64 Customers can use
these insights to fine-tune content filters toalign with their safety goals. Additionally, thepotentially abusive user detection feature

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R E S P O N S I B L E A I T R A N S PA R E N C Y R E P O R T

analyzes trends in user behavior and flagged
content to generate reports for our customers todecide whether to take further action in Azure AI
Studio. The report includes a user ranking and an
abuse report, enabling customers to take action
when abuse is suspected.

As we continue to improve our tools to map,
measure, and manage generative AI risks, we
make those tools available to our customers
to enable an ecosystem of responsible AI
development and deployment.

Transparency to support responsible
development and use by our customers
Beginning in 2019, we’ve regularly
released Transparency Notes—
documentation covering responsible
AI topics—for our platform services
which customers use to build theirown AI applications.
Since then, we’ve published 33 Transparency
Notes. Required for our platform services, thesefollow a specific template to provide customers
with detailed information about capabilities,
limitations, and intended uses to enable
responsible integration and use. Some examples
include Transparency Notes for Azure AI Vision
Face API,65 Azure OpenAI Service,66 and Azure
Document Intelligence.67
In 2023, we expanded our transparency
documentation beyond Transparency Notes.
We now require our non-platform services,such as our Copilots, to publish Responsible AI
Frequently Asked Questions (FAQs) and includeuser-friendly notices in product experiences toprovide important disclosures. For example,
Copilot in Bing provides users with responsible
AI documentation68 and FAQs69 that detail ourrisk mapping, measurement, and management
methods. In addition, when users interact with
Copilot in Bing, we provide in-product disclosure

to inform users that they are interacting with an
AI application and citations to source material tohelp users verify information in the responses andlearn more. Other important notices may include
disclaimers about the potential for AI to makeerrors or produce unexpected content. Theseuser-friendly transparency documents and productintegrated notices are especially important in our
Copilot experiences, where users are less likely to
be developers.

Transparency documentation and in-producttransparency work together to enable ourcustomers to build and use AI applications
responsibly. And as with our other responsible
AI programs, we anticipate that the ways weprovide transparency for specific products will
evolve as we learn.

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Section 4.

How we learn,
evolve, and growAs we’ve prioritized our company-wide
investments in responsible AI over the lasteight years, people remain at the center ofour progress. From our growing internal
community to the global responsible AI
ecosystem, the individuals and communities
involved continue to push forward what’s
possible in developing AI applications
responsibly. In this section, we share ourapproach to learning, evolving, and growing
by bringing outside perspectives in, sharing
learnings outwards, and investing in
our community.

4.

H O W W E L E A R N , E V O LV E , A N D G R O W

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4.

Governance of responsible AI
at Microsoft: Growing our
responsible AI community
At Microsoft, no one team ororganization can be solely responsible
for embracing and enforcing the
adoption of responsible AI practices.
Rather, everyone across every level of thecompany must adhere to these commitments
in order for them to be effective. We developed
our Responsible AI Standard to communicate
requirements and guidance so all teams canuphold our AI principles as they develop AI
applications.
Specialists in research, policy, and engineering
combine their expertise and collaborate on
cutting-edge responsible AI practices. These
practices ensure we meet our own commitments
while also supporting our customers and partners
as they work to build their own AI applications
responsibly.
Research: Researchers in Aether,70 Microsoft
Research,71 and our engineering teams keep
the responsible AI program on the leadingedge of issues through thought leadership.
They conduct rigorous AI research, including
on transparency, fairness, human-AI
collaboration, privacy, security, safety, andthe impact of AI on people and society. Our

researchers actively participate in broader
discussions and debates to ensure that our
responsible AI program integrates big-picture
perspectives and input.
Policy: The Office of Responsible AI (ORA)
collaborates with stakeholders and policy
teams across the company to develop policies
and practices to uphold our AI principles whenbuilding AI applications. ORA defines roles
and responsibilities, establishes governancesystems, and leads Sensitive Use reviews to
help ensure our AI principles are upheld inour development and deployment work. ORA
also helps to shape the new laws, norms, andstandards needed to ensure that the promiseof AI technology is realized for the benefit ofsociety at large.
Engineering: Engineering teams create
AI platforms, applications, and tools. They
provide feedback to ensure policies andpractices are technically feasible, innovate
novel practices and new technologies, andscale responsible AI practices throughout thecompany. Our engineering teams draw oninteractions with customers and user research
to address stakeholder concerns in thedevelopment of our AI applications.

H O W W E L E A R N , E V O LV E , A N D G R O W

Our responsible AI journey

Aether Committee
established

Satya Nadella’s
Slate article

Sensitive Usesof AI definedand program
established2016

Launched Global
Perspectives

AI Red TeamestablishedFacial Recognition
Principles adopted
AI Principles
adopted

Office of
Responsible
AI established

2017

2018

2019

Responsible AI
Standard v2

Responsible
AI Dashboard
releasedError Analysis
Open Source
tool released

2022

2021

2020

Copyright
Commitments

Co-founded theFrontier Model
Forum

Launched Azure AI
Content Safety

Published
Governing AI
Blueprint

White House
Voluntary
Commitments

2023

Responsible AI
Standard v1

PyRIT, prompt
shield, risks &abuse monitoringand more released

2024

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4.

Applying lessons from previous efforts to addressprivacy, security, and accessibility, we’ve built
a dedicated responsible AI program to guideour company-wide efforts.72 We combine afederated, bottom-up approach with strong
top-down support and oversight by company
leadership to fuel our policies, governance, andprocesses. From a governance perspective,
the Environmental, Social, and Public Policy
Committee of the Board of Directors provides
oversight and guidance on responsible AI
policies and programs. Our management ofresponsible AI starts with CEO Satya Nadella andcascades across the senior leadership team andall of Microsoft. At the senior leadership level,
the Responsible AI Council provides a forum
for business leaders and representatives fromresearch, policy, and engineering. The council,
co-led by Vice Chair and President Brad Smith
and Chief Technology Officer Kevin Scott, meetsregularly to grapple with the biggest challenges
surrounding AI and to drive progress in our
responsible AI policies and processes. Executive
leadership and accountability are key drivers
to ensure that responsible AI remains a priority
across the company.

Microsoft Board

Responsible AI Council

Office of Responsible AI

Research

PolicyEngineering

At the community level, we’ve nurtured aunique Responsible AI Champion program thatengages our engineering and global field teams
in our responsible AI work. The Responsible
AI Champion program is guided by a definedstructure, with clear roles and responsibilities
that empower our Champions and enable aculture of responsible AI across the company.
Senior leaders accountable for responsible AI
identify members of their organization to serve as

H O W W E L E A R N , E V O LV E , A N D G R O W

Responsible AI Champions. These Champions
enable their organizations to carry out our AI
commitments by working together and learningfrom one another’s expertise. This companywide network troubleshoots problems, offers
guidance, and advises on how to implement the
Responsible AI Standard.
Our combined bottom-up and top-down
approach empowers individuals, teams, andorganizations and facilitates a culture of
responsible AI by design. The collaborative andmultidisciplinary structure embedded in our
responsible AI program leverages the incredible
diversity of the company73 and amplifies what
we can achieve. Our engineering, policy, andresearch teams bring a wealth of passion,
experience, and expertise, which enables
us to develop and deploy safe, secure, andtrustworthy AI.

A dedicatedresponsible AI
programOur combined bottom-up and top-down
approach empowers individuals, teams,
and organizations and facilitates a cultureof responsible AI by design.

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4.

H O W W E L E A R N , E V O LV E , A N D G R O W

Growing a responsible AI community

Supporting our responsible AI community through training

We strongly believe that AI has the potential
to create ripples of positive impact acrossthe globe. Over the years, we have matchedthat belief with significant investments in
new engineering systems, research-led
incubations, and, of course, people. We
continue to grow and now have over 400
people working on responsible AI, morethan half of whom focus on responsible AI
full-time. In the second half of 2023,
we grew our responsible AI community 16.6
percent across the company. We increased
the number of Responsible AI Champions
across our engineering groups and grew thenumber of full-time employees who work on
centralized responsible AI infrastructure, AI
red teaming, and assessing launch readinessof our products.

We continue to grow a diverse community
to fulfill our commitments to responsible AI
and to positively impact the products andtools that millions of people use every day.
Some responsible AI community members
provide direct subject matter expertise, whileothers build out responsible AI practices
and compliance motions. Our community
members hold positions in research, policy,
engineering, sales, and other core functions,
touching all aspects of our business.
They bring varied perspectives rooted intheir diverse professional and academic
backgrounds, including liberal arts, computerscience, international relations, linguistics,
cognitive neuroscience, physics, and more.

We have an enormous opportunity tointegrate AI throughout the products andservices we offer—and are dedicated todoing so responsibly. This journey begins
with educating all of our employees.

11,00030,000attendees welcomed at SkillUp AI events.

employees reached through more than 40
events by our AI/ML connected community.

The 2023 version of our Standards of BusinessConduct training, a business ethics course
required companywide, covers the resources
our employees use to develop and deploy AI
safely. As of December 31, 2023, 99 percent
of all employees completed this course,
including the responsible AI module.
For our responsible AI community members,
our training goes even deeper. We provide
extensive training for our over 140 Responsible
AI Champions, more than 50 of whom joined
the program in 2023. In turn, Responsible AI
Champions help scale the implementationof responsible AI practices by training othermembers of the responsible AI community
in their respective Microsoft organizations.
This cascade strengthens peer relationships,builds trust among employees, and enables
Champions to customize instruction to their
specific organizations or divisions.
We continue to refine our training tokeep pace with rapid developments in AI,
particularly for responsible AI-focused
professionals. We’ve developed learning

sessions and resources for educating
employees on responsible AI skills, ourinternal processes, and our approach toresponsible AI. Ongoing education helps tokeep our responsible AI subject matter experts
current so they can disseminate up-to-datebest practices throughout the company.
We also provide training on general AI
topics so our employees can improve theirknowledge and abilities as AI becomes more
important for both our society and ourbusiness.
Throughout 2023, more than 100,000
employees attended conferences andevents such as the AI/ML Learning Series
and Hackathon, which has incubated morethan 11,000 AI-focused projects. At these
events, they learn the latest technologies
and ways to apply responsible AI principles
through company communities and channels.
Our employees also lead by sharing theirexperiences and expertise. For example,
our AI/ML connected community. reachednearly 30,000 employees through more than
40 events in 2023, and our SkillUp AI events
welcomed more than 11,000 attendees.

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H O W W E L E A R N , E V O LV E , A N D G R O W

Building safe and responsible
frontier models throughpartnerships and stakeholder input

AI sits at the exciting intersectionof technological breakthroughand real-world application. We arecontinually discovering new ways topush the limits with AI, innovating
solutions to address society’s biggest
problems. Frontier models, highly
capable AI models that go beyond
today’s state-of-the-art technologies,
offer significant opportunities tohelp people be more productive
and creative as well as hold major
potential to address global challenges.

Alongside these benefits, they also present risksof harm. That is why we are engaging in a numberof partnerships across industry, civil society,and academia to share our learnings and learn
from others.
An important example of how we can leadthrough partnership is our co-founding of the
Frontier Model Forum alongside Anthropic,
Google, and OpenAI. The Frontier Model Forumis an industry non-profit dedicated to the safe
and secure development of frontier AI models by
sharing information, developing best practices,
and advancing research in frontier AI safety.
By leveraging the expertise of the founding
members, as well as other organizationscommitted to developing and deploying frontiermodels safely, the Frontier Model Forum works
toward four priorities:

Advance AI safety research. We mustquestion, investigate, and collaborate on
how to responsibly develop and deploy
frontier models to address the challengesof frontier AI. We will collaborate todevelop standardized approaches toenable independent evaluations of models’capabilities and safety, where appropriate.

3 Share information and seek input. We
work with policymakers, academics, civilsociety organizations, and the private sector
to share knowledge about safety risks andcontinuously earn trust. We strongly believethat AI will touch virtually every aspect of life,
so frontier models will need input from all
corners of society to operate responsibly.

2 Contribute to the development andrefinement of best practices. We areworking with partners to identify bestpractices for the responsible development
and deployment of frontier models andhow to improve our practices as we
continuously learn.

4 Support efforts to develop applications
that can help meet society’s greatest
challenges. Innovation must play a central
role in tackling complex and persistent issues,
from human-caused climate change toglobal health.

1

We continue to support the work of the Frontier
Model Forum as it advances understanding of
how to address frontier AI risks in a way that
benefits organizations and communities
around the world.

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We are also a founding member of the multistakeholder organization Partnership on AI (PAI).
We consistently contribute to workstreams acrossits areas of focus, including safety-critical AI; fair,transparent, and accountable AI; AI, labor, andthe economy; and AI and media integrity. In June
2023, we joined PAI’s Framework for Collective
Action on Synthetic Media.74 This set of practices
guides the responsible development, creation,and sharing of media created with generative
AI. We also participated in PAI’s process todevelop Guidance for Safe Foundation Model
Deployment.75 We shared insights from ourresearch on mapping, measuring, and mitigating
foundation model risks and benefited from amulti-stakeholder exchange on this topic.
We continually look for ways to engage withstakeholders who represent specific concerns.
Since early 2023, we have been actively
engaging with news publishers and content
creators globally, including in the Americas,
Europe, and Australia. We listen to feedback
from creators and publishers to learn how
creative industries are using generative AI tools
and to understand concerns from creative
communities. We have engaged in public andprivate consultations, roundtables, and events.
For example, we participated in Creative
Commons’ community meetings on generative
AI and creator empowerment. We also sponsored
the Creative Commons Global Summit on AI
and the Commons held in Mexico City.76 Thissummit brought together a diverse set of artists,
civil society leaders, technology companies,

4.

and academics to address issues related to AI
and creative communities. We participatedin the inaugural Centre for News Technology
and Innovation roundtable on Defining AI in
News. Attendees included news organizations,
technology companies, and academics from theUnited States, the United Kingdom, Brazil, andNigeria. A report from the event highlights areasof opportunity and further multi-stakeholder
collaboration.77 In Australia, we also participatedin government-led roundtables engaging with
content and creative industries on addressing theissue of copyright and AI.
We support creators by actively engaging in
consultations with sector-specific groups to
obtain feedback on our tools and incorporate
their feedback into product improvements.
For example, news publishers expressed
hesitation around their content being used totrain generative AI models. However, they did
not want any exclusion from training datasets
to affect how their content appeared in search
results. In response to that feedback, we launchedgranular controls to allow web publishers to
exercise greater control over how content fromtheir websites is accessed and used.78
We are committed to responsibly scaling AI
to empower every person on the planet toachieve more. Our engagements with thebroader community of concerned artists, civilsociety organizations, and academics reflect ourinvestment in learning as we evolve our approachto responsible AI.

H O W W E L E A R N , E V O LV E , A N D G R O W

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4.

H O W W E L E A R N , E V O LV E , A N D G R O W

Using consensus-based
safety frameworks

Technology sector-led initiatives
comprise one important force toadvance responsible AI. Industry andothers stand to significantly benefit
from the key role that governments
can also play.
From within the U.S. Department of Commerce,
the National Institute for Standards andTechnology (NIST) built and published avoluntary framework to develop AI applications
and mitigate related risks. Extensive consultation
with industry, civil society organizations,and academic stakeholders helped NIST refine
this AI Risk Management Framework (AI RMF).
We contributed to NIST consultations and haveapplied learnings from NIST’s work ourselves,
including our application of the NIST RMF in our
generative AI requirements.

To implement its tasks in Executive Order (EO)
14110 (on the Safe, Secure, and Trustworthy
Development and Use of AI), NIST will consult
with stakeholders to develop additional guidance,such as a generative AI-specific version of the AI
RMF. Federal agencies and their AI providers canleverage the NIST AI RMF and NIST’s additionalreference materials to meet obligations requiredby the implementation of EO 14110. The NISTled AI Safety Institute Consortium (AISIC), which
we have joined, has launched five working
groups.79 These working groups will contribute
further guidance, datasets, frameworks, and test
environments to advance the field of AI safety.
Governments, industry, and other stakeholders
can also partner to develop standards, including
in international forums. Within the International
Standards Organization (ISO), there are ongoing
efforts to develop standards to support AI risk

management, including the recent publicationof ISO/IEC 42001, AI Management System(AIMS). Companion standards will also define
controls and support assessments of theirimplementation. International standards help
bring together global expertise in defining widely
applicable practices that can serve as the basis forrequirements in an interoperable ecosystem.
We have also partnered with the national security
and innovation nonprofit MITRE to incorporate
security guidance for generative applications into
its ATLAS framework.80 A recent update of the
ATLAS framework includes the vulnerabilities ofand adversarial attack tactics targeting generative
AI and LLMs so organizations can better protect
their applications.81 The framework also highlights
case studies of real-world incidents, including
how AI red teams and security professionals
mitigated identified issues. Finally, the ATLAS

update integrates feedback and best practices
from the wider community of government,
industry, academia, and security experts. This
resource provides an actionable frameworkso security professionals, AI developers, andAI operators can advance safety in generative
applications.
We welcome these and other multi-stakeholder
initiatives to advance responsible AI, knowingthat these efforts produce results that addressa broad range of concerns from a variety of
stakeholders.

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4.

Supporting AI
research initiatives

In addition to governmental
and private sector investment inresponsible AI, academic researchand development can help realize
the potential of this technology. Yet
academic institutions do not always
have the resources needed toresearch and train AI models.
The National AI Research Resource (NAIRR) seeks
to address this challenge.82 It intends to provide
high-quality data, computational resources,and educational support to make cutting-edge
AI research possible for more U.S. academic
institutions. We would also welcome and support
an extension of the NAIRR to provide access toacademic institutions among partners globally.
We believe that this comprehensive resource
would enable the United States and like-mindednations to continue to lead in AI innovation andrisk mitigation.

As currently proposed, a U.S.-focused NAIRR
will support a national network of users intraining the most resource-intensive models
on a combination of supercomputer andcommercial cloud-based infrastructure. This
centralized resource would enable academics topursue new lines of research and development
without individual institutions needing to heavily
invest in computing. Democratizing AI researchand development is an essential step toward
diversifying the field, leading to a greater breadthin background, viewpoints, and experience
necessary to build AI applications that serve
society as fully as possible. In short, NAIRR will
enable the country to innovate at scale.
In 2023, we announced our support of the NAIRR
pilot led by the National Science Foundation(NSF).83 For this pilot, we committed $20 million
worth of Azure compute credits and access toleading-edge models including those available in
Azure OpenAI Service.

In the spirit of advancing AI research, we havedeveloped the Accelerating Foundation
Models Research (AFMR) program.84 The AFMR
program assembles an interdisciplinary researchcommunity to engage with some of the greatest
technical and societal challenges of our time.
Through the AFMR, we make leading foundation
models hosted by Microsoft Azure moreaccessible to the academic research community.
So far, we have extended access to Azure OpenAI
Service to 212 AFMR principal investigators from
117 institutions across 17 countries. These projects
focus on three goals:
Aligning AI with shared human goals, values,
and preferences via research on models.
Projects will enhance safety, robustness,sustainability, responsibility, and transparency,
while exploring new evaluation methods tomeasure the rapidly growing capabilities ofnovel models.
Improving human interactions via
sociotechnical research. Projects will enable
AI to extend human ingenuity, creativity, andproductivity; reduce inequities of access;and create positive benefits for people andsocieties worldwide.
Accelerating scientific discovery in naturalsciences through proactive knowledge
discovery, hypothesis generation, andmultiscale multimodal data generation.
In the next call for proposals, we will seek projects
in the areas of AI cognition and the economy,
AI for creativity, evaluation and measurement,
and AI data engagement for natural and life

H O W W E L E A R N , E V O LV E , A N D G R O W$20 million

worth of Azure compute credits
committed to the NAIRR pilot, in addition
to access to leading-edge models.

sciences. We also launched an AFMR grantfor AI projects advanced by Minority Serving
Institutions, focused on Historically Black Collegesand Universities (HBCUs) and Hispanic-Serving
Institutions (HSIs), with 10 inaugural grantrecipients.85
In 2023, we announced the Microsoft Research
AI & Society Fellows program to foster researchcollaboration between Microsoft Research andscholars at the intersection of AI and societal
impact.86 We recognize the value of bridging
academic, industry, policy, and regulatory worlds
and seek to ignite interdisciplinary collaborationthat drives real-world impact. In the fall of 2023,
Microsoft Research ran a global call for proposals
to seek collaborators for a diverse set of thirteen
research challenges. The 24 AI & Society Fellows
were announced in early 2024. These fellows will
join our researchers for a one-year collaboration
with the goal of catalyzing research andcontributing publications that advance scholarly
discourse and benefit society more broadly.

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4.

Investing in research toadvance the state of theart in responsible AI
Microsoft researchers are also
advancing the state of the art
in generative AI, frequently inpartnership with experts outside of
the company.
Researchers affiliated with Microsoft Research87
and Aether88 published extensive research in 2023
to advance our practices for mapping, measuring,and managing AI risks,89 some of which wesummarize here.

Our researchers also contributed greatly toadvancing red teaming knowledge through the
production of tools, like AdaTest++,91
that augment existing red teaming practices.
Our researchers uncovered and shared novel
privacy and security vulnerabilities, such asprivacy-inferencing techniques,92 or attack
vectors when integrating LLMs for AI-assisted
coding.93 These researchers play a key role inshaping the emerging practice of responsible AIfocused red teaming and in producing resources
to share this practice more broadly.94

Identifying risks in LLMs and theirapplications

Research to advance our practices formeasuring risks

One of our approaches for identifying risks is
the Responsible AI Impact Assessment, which
includes envisioning the benefits and harms forstakeholders of an AI application. To address thechallenge of identifying potential risks before
AI application development or deployment,
researchers introduced AHA! (anticipating harmsof AI),90 a human-AI collaboration for systematic
impact assessment.

After we’ve identified potential risks, we canmeasure how often risks occur and how
effectively they’re mitigated.
For scaling measurement practices, ourresearchers developed a framework that uses two
LLMs.95 One LLM simulates a user’s interaction
with a generative application, and one LLM

evaluates the application’s outputs against agrading scheme developed by experts. Anotherarea explored by our researchers is measurement
validity. Thinking beyond measuring model
accuracy, researchers are advancing metrics thatalign more appropriately with user needs—for
example, when capturing productivity gains.96
Our researchers have also made advancementsin the emerging field of synthetic data fortraining and evaluating generative AI models.
These include an English-language dataset forevaluating stereotyping and demeaning harms
related to gender and sexuality97 and a framework
for increasing diversity in LLM-generated
evaluation data.98

Managing AI risks through transparency
Responsible use of AI applications is a shared
responsibility between application developers
and application users. As application developers,
it's important that we design mitigations thatenable appropriate use of our generative
applications—in other words, to minimize users’risk of overreliance 99 on AI-generated outputs.100
Our researchers have developed a number oftools and prototypes to assess AI-generated
outputs and improve our products. These include
an Excel add-in prototype that helps users
assess AI-generated code,101 a case study of how
enterprise end users interact with explanationsof AI-generated outputs,102 and research on
when code suggestions are most helpful for
programmers.103

H O W W E L E A R N , E V O LV E , A N D G R O W

In setting out a roadmap for transparency
in the age of LLMs,104 our researchers argue
that human-centered transparency is key tocreating better mitigations and controls for AI
applications. Their contributions to further ahuman-centered approach include research on
how users interact with AI transparency features,such as explanations of AI-generated outputs,105
and how to communicate model uncertainty
when interacting with AI-generated code
completions.106
Here, we’ve just scratched the surface of thecontributions our researchers are making toadvance our understanding and practice of
responsible AI.

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4.

H O W W E L E A R N , E V O LV E , A N D G R O W

Tuning in to global perspectives
Like many emerging technologies,
if not managed deliberately, AI
may either widen or narrow socialand economic divides between
communities at both a local and global
scale. Currently, the development ofAI applications is primarily influenced
by the values of a small subset of the
global population located in advanced
economies. Meanwhile, the farreaching impact of AI in developing
economies is not well understood.
When AI applications conceived in advanced
economies are used in developing ones, there
is considerable risk that these applications
either will not work or will cause harm. This isparticularly the case if their development does notcarefully consider the nuanced social, economic,
and environmental contexts in which they are
deployed. Both real and perceived AI-related
harms are primary drivers behind increasing
calls for AI regulation. Yet developing countries
are often left out of multistakeholder regulatory
discussions related to AI, even though they are
pursuing AI regulation themselves.

We affirm that to be responsible by design,
AI must represent, include, and benefit
everyone. We are committed to advancing
responsible AI norms globally and adapting
to the latest regulations. As our AI footprint
continues to grow in developing countries,
we must ensure our AI products and governanceprocesses reflect diverse perspectives fromunderrepresented regions. In 2023, we worked
with more than 50 internal and external groups
to better understand how AI innovation may
impact regulators and individuals in developing
countries. Groups included the United Nations
Conference on Trade and Development(UNCTAD), the U.S. Agency for International
Development (USAID), the U.S. Department ofState, and the Microsoft Africa Research Institute.
What we learned informed two goals:
1

Promote globally inclusive policy-making
and regulation. AI regulation is still in its
infancy, especially in developing countries.
We must recruit and welcome diverse
perspectives, such as representatives fromthe Global South, in the global AI policymaking process.

2

Develop globally relevant technology.
We must work to ensure the responsible
AI by design approach works for all theworld’s citizens and communities byactively collaborating with stakeholders
in developing countries.

As our AI presence expands globally, we willcontinue to make our AI services, products,
and responsible AI program more inclusive
and relevant to all. We are pursuing thiscommitment via several avenues.
UNESCO AI Business Council: Microsoft
and Telefonica co-chair the UNESCOAI Business Council. This public-private
partnership promotes the implementationof UNESCO’s Recommendation on the
Ethics of AI, which has been adopted by 193
countries so far. For example, we showcase
resources and processes to align withresponsible AI standards in webinars and
UNESCO events. We expect this effort tobring more companies and countries undera cooperative, globally relevant regulatory
framework for responsible AI.

Global Perspectives Responsible AI
Fellowships: The Strategic Foresight Hub at
Stimson Center and the Office of Responsible
AI established a fellowship to investigate
the impacts of AI on developing countries.107
The fellowship convenes experts fromAfrica, Asia, Latin America, and Eastern
Europe working to advance AI responsibly.
They represent views across academia,
civil society, and private and public sectors,
offering insights on the responsible use anddevelopment of AI in the Global South.
We recognize that we do not have all the answers
to responsible AI. We have prioritized collaboration
by partnering with a diverse range of privatecompanies, governmental groups, civil society
organizations, regulators, and international bodies.
This dynamic mix of perspectives, lived experiences,
technical expertise, and concerns pushes us tocontinue to do better.

50+

groups engaged to better understandhow AI innovation may impact regulatorsand individuals in developing countries.

36

LOOKING AHE AD

R E S P O N S I B L E A I T R A N S PA R E N C Y R E P O R T

Looking ahead

The progress we’ve sharedin this report would not be
possible without the passion andcommitment of our employees
across the company and aroundthe world. Everyone at Microsoft
has a role to play in developing AI
applications responsibly. Throughinnovation, collaboration, and awillingness to learn and evolve ourapproach, we will continue to drive
progress on our goals.
We'll continue to invest in four key areas toenable the scaling of responsible AI acrossthe industry:1

Innovating new approaches to
responsible AI development inour own products.

2

Creating tools for our customers
to responsibly develop theirown AI applications.

3

Sharing our learnings and bestpractices with the responsible
AI ecosystem at large.

4

Supporting the developmentof laws, norms, and standards
via broad and inclusive
multistakeholder processes.

We continue to develop policies, tools, andsolutions to mitigate risks in our own AI
products. For example, as we identify new
potential risks in generative AI, we improve ourexisting mitigations or build new mitigations
to try and stay ahead of emerging threats. In
addition to using innovative tools to protect ourusers, we also make them available to customers,
through tools like Azure AI Content Safety
and Azure AI Studio. This approach supports
customers’ responsible deployment of their own
AI applications.
To expand and improve our collective playbookof responsible AI best practices, we’ll continue
to share our learnings in deploying AI in asafe, secure, and trustworthy manner. We’ll
provide updates through our own channels
like the Microsoft On the Issues blog. We’ll also
participate in multistakeholder organizations
where we can both share our learnings andlearn from experts outside the company.

Finally, we recognize that we cannot build AI
applications that empower everyone on theplanet to achieve more if we do not include
everyone’s voices in the way these technologies
are built and governed. As we move into a newera of global AI governance, we’ll do our part toensure that laws, norms, and standards—and thepolicies we apply internally—are developed via
broad and inclusive multistakeholder processes.

2024
promises to be another pivotal year in
responsible AI as we work to harness
recent momentum to further accelerate
our progress toward safe, secure, andtrustworthy AI—in our own products, thework of our customers, and throughout
the global technology landscape.

37

SOURCES AND RESOURCESR E S P O N S I B L E A I T R A N S PA R E N C Y R E P O R T

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32

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36

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Sources and resources
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38

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48
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60
Responsible AI Toolbox. https://github.com/microsoft/
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68

61
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69
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