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“Some of the Microsoft tools around responsible AI are really good and show where those biases are,” Green says. “Those dashboards are fantastic.”
Reed agrees and adds that having “explainable AI” is critical for a healthcare organisation.
He also says that even after many decades of experience in orthopedics, he was surprised by some findings that the Responsible AI dashboard helped him spot.
“I was looking at what the AI model looks for to predict a risk of a ‘moderately severe’ complication. The dominant one was age, which was pretty obvious, followed by high blood pressure, which also made sense. The third one was the number of platelets.” These are cells in the blood that help clotting.
Reed was surprised to see that platelets carry such a significant weight in determining the outcome from surgery when compared to the other factors, and it may lead to new areas of research. That finding would have to be validated with different approaches, but it shows how technology is helping medical professionals to think differently about care.
NHS teams building their own AI models – as Green and Reed have done – are becoming increasingly common, as the healthcare sector tries to manage increasing workloads and provide cutting-edge care to millions of people.
Earlier this year, Health Education England, which supports the delivery of healthcare to the public, published its first roadmap to the use of AI in the NHS, which showed that the healthcare sector “recognizes the power and potential for AI to increase resilience, productivity, growth, and innovation.”
A total of 60 technologies are expected to be ready for large-scale deployment in England’s healthcare sector within a year. There are plans to roll out these and other digital tools across 67 clinical areas, including radiology, cardiology and general practice.
Patients might not notice the changes when they visit a hospital or their GP, but they could soon be benefitting from a more personalized and informative care experience.
Top image: Orthopedic surgeons Justin Green and Mike Reed from the Northumbria Healthcare NHS Foundation Trust look at Microsoft’s Responsible AI Dashboard (Photo credit: Jonathan Banks)
Ever since Ada Lovelace, a polymath often considered the first computer programmer, proposed in 1843 using holes punched into cards to solve mathematical equations on a never-built mechanical computer, software developers have been translating their solutions to problems into step-by-step instructions that computers can understand.
That’s now changing, according to Kevin Scott, Microsoft’s chief technology officer.
Today, AI-powered software development tools are allowing people to build software solutions using the same language that they use when they talk to other people. These AI-powered tools translate natural language into the programming languages that computers understand.
“That allows you, as a developer, to have an intent to accomplish something in your head that you can express in natural language and this technology translates it into code that achieves the intent you have,” Scott said. “That’s a fundamentally different way of thinking about development than we’ve had since the beginning of software.”
This paradigm shift is driven by Codex, a machine learning model from AI research and development company OpenAI that can translate natural language commands into code in more than a dozen programming languages.
Codex descended from GPT-3, OpenAI’s natural language model that was trained on petabytes of language data from the internet. Codex was trained on this language data as well as code from GitHub software repositories and other public sources.
“It makes coding more productive in terms of removing not-so-fun work and also helping you remember things you might have forgotten and helping you with the approach to solve problems,” Peter Welinder, vice president of products and partnerships for OpenAI, said of Codex.
The increase in productivity that Codex brings to software development is a game changer, according to Scott. It allows developers to accomplish many tasks in two minutes that previously took two hours.
“And oftentimes, the things that the tools are doing is they are helping you to very quickly go through the least interesting parts of your job so that you can get to the most interesting parts of your job, which makes the qualitative experience of creating much more pleasant and stimulating and fun,” he said.
Microsoft and OpenAI formed a partnership in 2019 to accelerate breakthroughs in AI – including jointly developing some of the world’s most powerful AI supercomputers – and deliver them to developers to build the next generation of AI applications through Azure OpenAI Service.
Microsoft subsidiary GitHub also worked with OpenAI to integrate Codex into GitHub Copilot, a downloadable extension for software development programs such as Visual Studio Code. The tool uses Codex to draw context from a developer’s existing code to suggest additional lines of code and functions. Developers can also describe what they want to accomplish in natural language, and Copilot will draw on its knowledge base and current context to surface an approach or solution.
GitHub Copilot, released in a technical preview in June 2021, today suggests about 35% of the code in popular languages like Java and Python generated by the tens of thousands of developers in the technical preview who regularly use GitHub Copilot. GitHub Copilot will move to general availability this summer, bringing this AI-assisted coding capability to millions of professional developers, Microsoft announced today at its Microsoft Build developer’s conference.
“A lot of software has common frameworks and pieces of scaffolding. Copilot does such an awesome job of doing all that for you so you can focus your energy and your creativity on the things that you’re trying to solve uniquely,” said Julia Liuson, president of the developer division at Microsoft, which includes GitHub.
As more developers experiment with Codex and GitHub Copilot, more clues to the potential of AI-assisted development are emerging, according to Welinder. For example, natural language documentation inside most software programs is sparse. Users of GitHub Copilot create this documentation by default as they use the tool.
“You get a bunch of comments in the code just from the nature of telling Copilot what to do,” he said. “You’re documenting the code as you go, which is mind-blowing.”
These comments, in turn, serve as a teaching tool for other developers, who often study other programs to learn how to solve specific problems in their own programs. The ability of Codex to translate from code to natural language is another way developers can learn as they program, which will lower the barrier of entry to coding, Welinder added.
Meanwhile, AI-powered low code and no code tools, such as those available through Microsoft Power Platform, aim to enable billions of people to develop the software applications that they need to solve their unique problems, from an audiologist digitizing simple paper forms to transform hearing loss prevention in Australia to a tool that relieves the burden of manual data-entry work from employees of a family owned business and an enterprise grade solution that processes billions of dollars of COVID-19 loan forgiveness claims for small businesses.
Today, the hundreds of millions of people who are comfortable working with formulas in Microsoft Excel, a spreadsheet program, could easily bring these skills into Power Platform where they can build these types of software applications, according to Charles Lamanna, Microsoft corporate vice president of business applications and platform.
“One of the big pushes we’ve been doing is to go to the next level, to go from hundreds of millions of people that can use these tools to billions of people that can use these tools,” he said. “And the only way we think we can actually do that is to go from low code to no code by using AI-powered development.”
To do this, Lamanna’s team first integrated GPT-3 with Microsoft Power Apps for a feature called Power App Ideas, which allows people to create applications using conversational language in Power Fx, an open-source programming language for low code development with its origins in Microsoft Excel. The next step, announced at Build, is a feature called Power Apps express design, which leverages AI models from Azure Cognitive Services to turn drawings, images, PDFs and Figma design files into software applications.
“We’ve made it so that we can do image recognition and map it to the constructs that exist within an application. We understand what’s a button, what’s a grouping, what’s a text box and generate an application automatically based on those drawings without you having to understand and wire up all these different components,” Lamanna said.
A new AI-powered feature called Power Apps express design helps turn sketches and other images into the bones of an app, helping people with little or no coding experience develop software.
This transition from low code to no code on the back of AI follows a general trend of computing becoming more accessible over time, he added. Personal computers were rare 40 years ago, spreadsheets were uncommon 30 years ago, internet access was limited 20 years ago, for example. Until recently, video and photo editing were reserved for experts.
Software development should also become more accessible, Lamanna said.
“If we want everybody to be a developer, we can’t plan on teaching everyone how to write Python code or JavaScript. That’s not possible. But it is possible if we create the right experiences and get them in front of enough people who can click and drag and drop and use concepts that are familiar to create amazing solutions,” he said.
GitHub Copilot as well as the low code and no code offerings available via the Power Platform are the first phase of AI-powered development, according to Liuson. She envisions AI-powered models and tools that will help developers of all ability levels clean data, check code for errors, debug programs and explain what blocks of code mean in natural language.
These features are part of a larger vision of AI-powered tools that could serve as assistants that help developers more quickly find solutions to their problems and help anyone who wants to build an application go from an idea in their head to a piece of software that works.
“As a developer, we all have days that we have pulled out our hair, saying, ‘Why is this thing not working?’ And we consult with a more senior developer who points us in the right direction,” Liuson said. “When Copilot can go, ‘Hey here are the four different things that are common with this pattern of problem,’ that will be huge.”
This new era of AI-assisted software development can lead to greater developer productivity, satisfaction and efficiency and make software development more natural and accessible to more people, according to Scott.
For example, a gamer could use natural language to program non-player characters in Minecraft to accomplish tasks such as build structures, freeing the gamer to attend to other, more pressing tasks. Graphic designers can use natural language to build 3D scenes in the graphics rendering engine Babylon.js. Teachers can use 3D creation and collaboration tools like FrameVR to speak into existence a metaverse world such as a moonscape with rovers and an American flag.
“You can describe to the AI system what you want to accomplish,” Scott said. “It can try to figure out what it is you meant and show you part of the solution and then you can refine what the model is showing you. It’s this iterative cycle that’s free flowing and natural.”
These tools, Scott added, will also swell the ranks of developers in a world that will be increasingly powered by software.
“Because the future is so dependent on software, we want a broad and inclusive set of people participating in its creation,” he said. “We want people from all sorts of backgrounds and points of view to be able to use the most powerful technology they can lay their hands on to solve the problems that they have, to help them build their businesses and create prosperity for their families and their communities.”
Related
Top photo: Kevin Scott, Microsoft chief technology officer, said AI-powered tools help developers get from thoughts in their heads to code. Photo courtesy of Microsoft.
John Roach writes about Microsoft research and innovation. Follow him on Twitter.
It’s a well-known challenge that large language models (LLMs)—growing in popularity thanks to their adaptability across a variety of applications—carry risks. Because they’re trained on large amounts of data from across the internet, they’re capable of generating inappropriate and harmful language based on similar language encountered during training.
Content moderation tools can be deployed to flag or filter such language in some contexts, but unfortunately, datasets available to train these tools often fail to capture the complexities of potentially inappropriate and toxic language, especially hate speech. Specifically, the toxic examples in many existing hate speech datasets tend either to be too hard or too easy for tools to learn from—the too-easy examples contain slurs, profanity, and explicit mentions of minority identity groups; the too-hard examples involve obscure references or inside jokes within the hate speech community. Additionally, the neutral examples in these datasets tend not to contain group mentions. As a result, tools may flag any language that references a minority identity group as hate speech, even when that language is neutral. Alternatively, tools trained on this data fail to detect harmful language when it lacks known or explicit slurs, profanity, or explicit mentions of minority identity groups.
Generating the kind of data needed to strengthen content moderation tools against the above failures and harms is challenging for numerous reasons. In particular, toxic text that is more implicit and that existing machine learning architectures can still learn from or neutral text with group mentions is difficult to collect at scale. Additionally, asking people to write such examples—particularly the toxic ones—can have a negative impact mentally on those assigned the task.
Inspired by the ability of large language models to mimic the tone, style, and vocabulary of prompts they receive—whether toxic or neutral—we set out to create a dataset for training content moderation tools that can be used to better flag implicitly harmful language. In our paper “ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection,” we collected initial examples of neutral statements with group mentions and examples of implicit hate speech across 13 minority identity groups and used a large-scale language model to scale up and guide the generation process. The outcome is the largest implicit hate speech dataset to date that is publicly available: 274,000 examples comprising both neutral and toxic statements. We conducted a human study on the generated dataset to better understand different aspects of harm beyond binary labels of toxic and neutral assigned by content moderation tools. To stress test existing content moderation tools across minority identity groups studied in this work, we also propose an adversarial classifier-in-the-loop decoding approach. The dataset, two content moderation tools trained on the dataset, prompts used as seed data, and the source codes for our proposed adversarial decoding approach are available in the ToxiGen GitHub repo (please see footnote).
We’re presenting this work at the 2022 Meeting of the Association for Computational Linguistics (ACL), where our colleagues will also be presenting work that leverages the generative power of large language models and human expertise.
Large Transformer-based language models don’t explicitly encode semantic information; nevertheless, these models can distinguish the statistical interactions of words in different contexts. Through experimentation with the generation of language via one of these large language models, we learned how to utilize careful prompt engineering strategies to create the ToxiGen implicit hate speech dataset.
Our first experiments were to generate examples of hate speech and neutral speech related to the 13 minority identity groups in our work. We started by collecting implicit hate speech prompts from existing datasets and neutral prompts drawn from news articles, opinion pieces, podcast transcripts, and other similar public sources and feeding them into the LLM to create a broader, deeper set of prompts. What we found was that the LLM could generate examples that were qualitatively different depending on the source material. When prompted with bits from different writers on the above topics, in each case, the LLM produced linguistically diverse outputs that were nonetheless similar in style and tone.
Furthermore, we found that through careful cultivation of prompt sets, we could generate a wide variety of text reflecting diverse opinions and thoughts on these topics that weren’t found in our original source materials. We could generate neutral statements about sensitive topics that mentioned the relevant minority identity groups, and we could consistently generate hate speech statements about these minority identity groups that didn’t contain slurs or profanity. And the more we experimented with the source material, the more interesting our dataset became. This is particularly exciting because we hope that other individuals and groups can use these tools to extend our dataset; different disciplinary experts could utilize the same strategies and collect even better prompt sets, resulting in even more subtle and rich examples of neutral speech and hate speech.
We also found that the model often generated examples of speech that we ourselves had trouble labeling. In essence, we were using the LLM as a probe to explore the delicate boundaries between acceptable and offensive speech. As a result, our own understanding of the problem definition itself grew through our interactions with the model.
The first 260,000 examples from our dataset were drawn from this experimental approach.
While demonstration-based prompting can facilitate large-scale data generation, it doesn’t generate data targeted specifically to challenge a given content moderation tool, or content classifier. This is important because every content moderation tool has unique vulnerabilities depending on the type of data it has been trained on. To address this, we developed (De)ToxiGen (referred to as ALICE in the paper), an algorithmic mechanism that creates an adversarial set-up between an LLM and a given content moderation tool in which the content classifier is in the loop during decoding.
The proposed approach can increase or decrease the likelihood that a generated statement is classified as hate speech while maintaining the coherence of the generated language. It can generate both false negatives and false positives for a given content moderation tool. For false negatives, toxic prompts are used to elicit toxic responses, and then the tool’s probability of the neutral class is maximized during decoding. Similarly, to generate false positives, neutral prompts are used to generate neutral responses, and then the probability of the toxic class is maximized during decoding. With this approach, we’re essentially trying to reveal weaknesses in a specific content moderation tool by guiding the LLM to produce statements that we know the tool will misidentify. The generated data can then be used to improve the performance and coverage of the targeted content moderation tool. Our ToxiGen dataset includes data generated by both demonstration-based prompting and our proposed adversarial decoding approach. Through empirical study on three existing human-written datasets, we found that starting with an existing content moderation tool and fine-tuning it on ToxiGen can improve the tool’s performance significantly, demonstrating the quality of the machine-generated data in ToxiGen.
Human language is complex, particularly when it comes to harmful statements. To better understand different aspects of the data in ToxiGen—its perceived harmfulness and intent and whether it presents as fact or opinion, for example—we conducted human evaluations on the data generated by both regular decoding (top-k), used in the demonstration-based prompting, and the proposed adversarial decoding. The human evaluation also allowed us to test the quality of the output of these methods and gauge how effective these methods were in guiding the generation of the data we sought.
For the human evaluation, three annotators were used for each statement from a pool of 156 prequalified annotators with prior experience annotating toxic language. About 4,500 samples were randomly selected for each of the decoding methods with coverage across all 13 minority identity groups for each split. We found the following:
As advances continue to be made in large language models, we remain vigilant in our pursuit of AI systems that align with our commitment to technology that benefits society as a whole and empowers everyone to achieve more. We’re beginning to ask better questions to more deeply understand the risks associated with LLMs and build processes and methods for addressing them. Existing content moderation tools tend to be only good at flagging overt inappropriate or harmful language. Our work aims to create data that can better target the challenge. While our work here specifically explores hate speech, our proposed methods could be applied to a variety of content moderation challenges, such as flagging potential misinformation content. By releasing the source codes and prompt seeds for this work, we hope to encourage the research community to contribute to it by, for example, adding prompt seeds and generating data for minority identity groups that aren’t covered in our dataset.
As with many technologies, the solutions we develop to make them stronger, more secure, and less vulnerable also have the potential to be used in unintended ways. While the methods described here may be used to generate inappropriate or harmful language, we believe that they provide far greater value in helping to combat such language, resulting in content moderation tools that can be used alongside human guidance to support fairer, safer, more reliable, and more inclusive AI systems.
There is still a lot that this dataset is not capturing about what constitutes problematic language, and before utilizing the dataset, its limitations should be acknowledged. Our annotations might not capture the full complexity of these issues, given problematic language is context-dependent, dynamic, and can manifest in different forms and different severities. Content moderation tools aren’t a silver bullet to address harmful online content. Problematic language is fundamentally a human-centric problem. It should be studied in conjunction with human experience, and tools to address this problem should be developed and deployed with human expertise and well-informed regulatory processes and policy. Multidisciplinary work is needed to better understand the aspects of this challenge.
Also, this dataset only captures implicit toxicity (more precisely hate speech) for 13 minority identity groups and due to its large scale can naturally have imperfections. Our goal in this project is to provide the community with means to improve hate speech detection on implicit toxic language for the identified minority identity groups, and there exist limitations to this dataset and models trained on it that can potentially be the subject of future research, for example, including more minority identity groups, a combination of them, and so on that are not covered in our work. Stronger content moderation tools and systems can contribute to mitigating fairness-related harms in AI systems. For example, systems that don’t over-flag neutral statements with minority identity group mentions can help ensure better representation of diverse perspectives and experiences, while systems that can better flag implicit hate speech can support more inclusive technology.
Acknowledgment
This work was conducted by PhD students Thomas Hartvigsen and Saadia Gabriel during their internships at Microsoft Azure and Microsoft Research. Hamid Palangi, Dipankar Ray, Maarten Sap, and Ece Kamar served as advisors on the work. A special thanks to Misha Bilenko from Azure ML for making the compute resources available and to Microsoft Research for supporting our large-scale human study.
Please note: This research, the GitHub repository, and examples from our work included in this blog contain and discuss content that is offensive or upsetting. All materials are intended to support research that improves hate speech detection methods. Included examples of hate speech don’t represent how the authors or sponsors feel about any minority identity groups. Hate speech applies to a range of minority identity groups; for the purposes of this research, we focus on 13 of them (as shown in Figure 1). Content moderation tools are part of larger content moderation systems. These systems also include human expertise and thoughtful policy and regulatory development. Even the most robust content moderation tools and datasets require systems with human supervision.
As air travel and tourism returns to pre-pandemic levels, the commercial aviation industry is ready to welcome travelers back into the air. And digital technology has the potential to help airlines create a smoother travel experience, especially for those who may not have traveled in the past few years.
American Airlines, the world’s largest airline, is one of the first global airlines to recognize and embrace this opportunity. To minimize disruptions in the airport, on the tarmac and throughout the system, American and Microsoft are partnering to streamline operations, better empower team members and enhance customer experiences using the Microsoft Cloud.
Through this partnership, the airline is equipping its frontline workers with access to information and insights that streamline ground operations and make travel a more pleasant experience for customers, as well as applying data and other technologies to enhance business processes.
“Reliably operating thousands of flights around the world to take customers to hundreds of destinations is critical to American, which is why the airline has chosen Microsoft’s technology to support our applications,” says American Airlines’ Chief Information Officer Maya Leibman.
Improving costs and increasing efficiency
For airlines and customers who are trying to make a connecting flight, minutes count. Together, American and Microsoft are applying the power of AI, machine learning and data analytics to reduce the taxi time for flights, giving connecting customers extra time to make their next flight while also saving thousands of gallons of jet fuel and decreasing CO2 emissions for the American Airlines fleet. Built on Azure, American’s intelligent gating program provides real-time analysis of data points, including routing and runway information to automatically assign the nearest available gate to arriving aircraft.
Gating decisions for American’s 136 gates at Dallas/Fort-Worth International Airport (DFW), for example, have traditionally required more manual involvement from gate planners. Now, the program can look at multiple data points simultaneously for the hundreds of daily arrivals, saving more than a minute of taxi time per flight. That can not only eliminate up to 10 hours of taxi time per day but also 870,000 gallons of jet fuel each year at DFW – equating to a CO2 emissions reduction of more than 2,600 metric tons annually.
Empowering frontline teams
Prior to the pandemic slowdown, technology investments across all industries tended to focus on simplifying customer experiences. In the travel industry, high-visibility customer-facing systems and smartphone apps received significant funding. Meanwhile, frontline systems received less attention, prompting mobile employees without regular access to desktop or laptop computers to rely instead on texting and consumer apps.
In fact, according to a Microsoft Work Trends Index Special Report, one-third of all frontline workers say they do not have the right technological tools to do their job effectively; that number rises to 41 percent for those in non-management positions.
American Airlines is addressing its frontline workers’ technology needs, equipping them with solutions like ConnectMe, a Microsoft Teams-based solution leveraging PowerApps and Azure, which the airline developed in partnership with Microsoft. Using the app, team members can access real-time data from any mobile device. With key arrival, boarding, baggage and gate information now at their fingertips, American’s frontline teams have accelerated aircraft turn times at dozens of airports in the United States.
By empowering its team members with modern technology to streamline communication and coordination, the airline is driving operational efficiency while also creating a more connected, inclusive worker-friendly culture.
Driving innovation
Running the world’s largest airline is no small feat. Through its partnership with Microsoft, American is on track to migrate and centralize its entire portfolio of strategic operational workloads in the cloud. Operations Hub on Azure will connect American’s data warehouse, several legacy applications and other tools in one place, making the airline one of the first to embrace a comprehensive cloud strategy for all areas of its business. The move will allow American to save costs, increase efficiency and scalability, and make progress toward its ambitious sustainability goals.
“With the power of Microsoft Azure, American can innovate and accelerate its technology transformation, giving our team members augmented tools to provide our customers with an enhanced travel experience,” says Leibman.
It’s been rewarding to see how the world’s largest airline has embraced technology to propel innovation, and we’re excited to reach new heights together for years to come.
(Photos courtesy of American Airlines)
The power and utilities sector plays a critical role in building a sustainable energy future while providing safe, reliable, affordable, clean energy to homes and businesses. As a technology innovator and partner of the industry, the Microsoft Energy team looks forward to participating in DISTRIBUTECH International in Dallas, Texas, from May 23 to 25, 2022, the leading annual energy transmission and distribution event for North America. We’re energized to connect with the industry, customers, and partners to share innovations and technologies that transform energy, improve operational efficiencies, and advance sustainability goals.
Our team will be on stage and at the Microsoft booth together with our partners and customers to highlight success stories in digital transformation, grid flexibility, renewable energy integration, advanced metering, enhancing system operations and reliability, cybersecurity, carbon tracking, and more. Program highlights featuring the Microsoft Energy team include:
As we continue the journey to a net-zero future, Microsoft is proud to announce an expanded partnership with a key partner, Itron. With the enormous energy challenges the world is facing, collaboration has become more important than ever to get to net-zero. Through strategic partnerships, Microsoft continues to extend our global energy partner ecosystem that fosters innovative technology development and enables digital transformation.
Through the expanded partnership, Microsoft and Itron will help accelerate cloud adoption and the next generation of consumer and grid edge solutions for the utility industry. The collaboration uniquely brings together Itron’s leading energy management solutions and Microsoft’s leading cloud solutions to transform how end users view and manage their energy, and how utilities meet the demands of a rapidly changing industry. The collaboration between Itron and Microsoft will enable utilities to take advantage of cloud computing technology—in particular, edge computing—to accelerate advanced metering infrastructure (AMI) 2.0 capabilities including cloud-native analytics, distribution automation, carbon reporting, and an overall more flexible, scalable system to support customer and utility outcomes for a distributed, resilient energy grid.
Together, Microsoft and Itron will partner to develop solutions that deliver insights and benefits for utilities to reach their goals, including grid resilience, decarbonization, consumer engagement, and operational efficiency.
Another key relationship we’re pleased to highlight is with Schneider Electric, which represents a partnership spanning more than 30 years fueled by a shared vision for energy efficiency and sustainability. At the heart of this partnership, Schneider’s solutions are powered by the most advanced evolution of Microsoft Azure and integrate process and energy technologies to deliver the full efficiency and sustainability potential for utility grids, buildings, data centers, industry, and infrastructure. Working together, and with the unique expertise Microsoft and Schneider can bring, we have the ability to accelerate progress and our continued partnership will enable us to get there. Learn more during Schneider’s presentation at the Microsoft booth on Monday, May 23, 2022.
Technology will play a larger, more important role in helping the energy industry decarbonize and achieve a net-zero economy by 2050. No matter where companies are on their sustainability journey, Microsoft provides technology solutions that enable an easier, faster, automated way for organizations to collect and record environmental data, report actionable insights, and reduce carbon emissions. To support companies on this critical journey to net-zero, we are pleased to introduce Microsoft Sustainability Manager, available on June 1, 2022.
Our Sustainability Manager, a component of Microsoft Cloud for Sustainability, integrates powerful solutions delivered by Microsoft and our partners to help organizations manage sustainability progress end to end. These solutions offer a more automated and comprehensive view into the emissions impact of their entire operations and value chain by enabling organizations to record and more accurately report their emissions, and continually test and optimize reduction strategies to reach their goals. Microsoft Cloud for Sustainability brings together capabilities that help organizations unify data intelligence and identify opportunities to build a more sustainable IT infrastructure. We encourage you to visit our DISTRIBUTECH booth to meet the Microsoft Sustainability team and experience firsthand how the solution can advance your company’s journey to net-zero.
If you are attending DISTRIBUTECH International 2022, we look forward to seeing you at the show and at the Microsoft booth 1023, where you can meet with the Microsoft Energy team, our partners, and customers and learn how we are working together to power a sustainable future.
Every day, we must do our part to protect our planet from the impacts of climate change, and technology has an important role to play.
Every day, we must do our part to protect our planet from the impacts of climate change, and technology has an important role to play.
Editor’s Note: This blog was updated after publication.
Today we honor the 11th annual Global Accessibility Awareness Day (GAAD). GAAD is a day of awareness focused on digital access and inclusion for people with disabilities around the world. The pandemic has grown the disability demographic and widened the disability divide. Yet there are the seeds of positive change. As Caroline Casey, Founder & Creator of The Valuable 500 said at the Microsoft Ability Summit last week, “We’re seeing a move since the pandemic… recognition from businesses that accessibility is not a ‘niche’ issue for people with disabilities, it’s an issue that needs to be completely invested in for communication for consumers and employees.” We couldn’t agree more. We’re proud that accessibility is part of our culture here at Microsoft.
Here’s more on our journey to bridge the disability divide in partnership with disability community and accessibility experts around the world.
Technology has the power to empower. Our responsibility is to raise the bar for what is possible with technology for people with disabilities and deliver on the potential of inclusive design.
This month, Microsoft has introduced several new features and products that deliver on that potential, and many of those ideas came from you. Windows 11 is the most accessible Windows ever. You asked us to make accessibility features easy to find and use, so we flipped the name from ‘Ease of Use’ to ‘Accessibility’ and added a new accessibility pane and human icon to make it easier to use and find accessibility features. Those new features include Live Captions (available online and offline); new Natural voices for screen reader users; Voice Access; and (my new favorite) focus mode, which allows you to turn off notifications.
Accessibility can make content more inclusive, whether or not you know if someone has a disability. The great news is that you’re using those accessibility features more and more. Thanks to better placement and prompts, use of Office Accessibility Checker — our accessibility ‘spellcheck’ — has grown 14-fold in the last year, meaning more content is being checked for accessibility than ever before. And 163,000 people have joined the Xbox Accessibility Insider League (XAIL), an opportunity for anyone who self-identifies as a person with a disability or ally, to provide accessibility feedback directly to Xbox engineering or game development teams. This is powering a new wave of insight to support the more than 400 million players with disabilities across the globe, creating features for deaf gamers such as the inclusion of ASL and BSL on Forza, and a new ASL Twitch channel!
At the Microsoft Ability Summit last week, Microsoft Chief Product Officer Panos Panay revealed our new Adaptive Accessories line — a new mouse and keyboard experience on a PC or phone. Available later this year, the kit also includes a hub to connect these tools to other adaptive devices made by Microsoft or others.
There is far more ahead. Innovating for the future is top of our mind. It’s clearly top of yours. Our Microsoft partner ecosystem is working to build and deliver accessible technology at scale for customers, and partners are innovating solutions with accessibility at the forefront. For example, CityMaaS has developed the Mobility Map platform to provide localized accessibility information around the globe to enable disabled communities to visit businesses and places of interests. (You can find more #BuildFor2030 featured accessibility offerings by Microsoft partners.)
To power that next wave of innovation, we need to empower the talent pipeline and tackle challenges impacting disabled talent. According to the National Council on Disability (NCD), students with disabilities who could not receive necessary services and support during the pandemic have experienced disruption and regression in their behavioral and educational goals. People with disabilities have historically been underrepresented in the workforce; the pandemic exacerbated the long-standing problem.
Youth and adults with mental health disabilities that predate the pandemic have experienced measurable deterioration over its course.
A new study from Microsoft EDU found that 84% of teachers say it’s impossible to achieve equity in education without accessible learning tools. And 87% agree that accessible technology can help not only level the playing field for students with disabilities but also generate insights that help teachers better understand and support all their students. Four in ten (41%) teachers have seen an increase in mental and/or emotional issues among their students.
Helping to empower students with disabilities is a top priority for us at Microsoft. We are working to support teachers with tools, from training through the new Microsoft Learn Educator Center for Resources and Professional Development to technology, including Immersive Reader and Microsoft Reflect, which helps teachers monitor student well-being and encourage feeling identification, and the new Minecraft: Education Edition.
GAAD is a time to double down to open doors to disabled talent into employment. We support the Transformation to Competitive Integrated Employment Act, legislation that addresses subminimum wage, an important step for wage equity for people with disabilities. This includes U.S. Department of Labor grants to states and eligible entities to help transform their business and program models to support people with disabilities.
Employment for people with disabilities takes partnership. With an unemployment/underemployment rate for neurodiverse talent estimated at 80% or higher, it’s crucial that we work together, not compete. Recently, we worked with 30 partners from Neurodiversity @ Work Roundtable, to launch the Neurodiversity Career Connector. This jobs marketplace supports large to small employers to “screen in” neurodivergent job candidates. This is the next chapter in our journey at Microsoft, which started in 2015 with a pilot program to hire autistic talent. We are proud to expand these efforts and partner with 30 companies to open doors to this talent pool and make a dent on those metrics.
Microsoft is also excited to announce the expansion of our relationship with The Valuable 500, an organization working with 500 major companies committed to making workplaces more accessible and inclusive of people with disabilities. This will expand on our partnership with impactful organizations such as Disability:IN and many others.
Microsoft continues to be powered by the insights from the disability community, and is proud to work with some of the best in the industry. This includes our accessibility team, who came together at the new Inclusive Tech Lab at Microsoft HQ this week. It is a journey and I encourage all to explore our Disability Answer Desk and https://blogs.microsoft.com/accessibility/ wherever you are in navigating your work towards a more accessible future for all.
I’m so very proud of Microsoft’s work in this field, working in partnership with so many focused on accelerating accessibility and disability inclusion around the world. Join me in celebrating their work today. And tomorrow, let’s get going on the next chapter. Onwards!
