Additionally, a summarization of command categories and frequencies showed the most popular category was "command and control" where users adjust device settings, navigate through the results and answer yes/no style of questions. The next popular category was related to entertainment questions, followed by lifestyle and shopping. Furthermore, despite signing into a device, participants made sophisticated use of the spaces around their bodies, for example to represent and refer to people or things that were the topic of their questions. Another observation was the use of a question-mark sign at the beginning of yes or no questions, to call the attention of the device, while typically this sign more often used at the end of such questions. When it came to errors, such as the device not giving the result the users were looking for, most commonly users would simply ignore the error and proceed with a different command. A close second method was to repeat the command with the exact same wording and signing style, followed by rewording the command.

With current research showing that most users do not use mental health apps beyond two weeks, the texting platform offers a more promising alternative. The platform works by offering pushed text messages throughout the day, giving the user the opportunity to engage whenever they're ready. Because it uses SMS, there's no need for data or internet access to interact with the AI bot. After signing up on the MHA website, a text conversation is initiated and the user answers questions. From there, the platform uses machine learning to drive specific engagement interests and learn more about the activities of the user, like the topics they're interested in and the best format for information.

When sourcing for candidates, recruiters have a variety of tools to choose from, including automated screening tools. Sammy encountered bias from people in his education experience and employment search with various companies, so could the use of artificial intelligence (AI) correct that bias or will machine learning also filter out highly qualified candidates with disabilities like Sammy? The Inclusive Design Research Center at OCAD University (IDRC) is searching for that answer. It is estimated that over 50 percent of companies will deploy some form of AI-assisted or AI-automated hiring tools in the next decade. These tools use past hiring data to filter applicants by optimizing the characteristics of previous successes, but does that approach help build a diverse and inclusive workforce?

Through our work in the Microsoft AI for Accessibility program, we have learned there are big gaps in mental health services around the globe. In some countries, there may only be one mental health professional per 100,000 people. When paired with the reality that 1 in 5 people have a mental health condition, we are asking how technology can and should be involved. In February, we shared our call for project proposals that aim to accelerate mental health research, data insights, and innovations using AI, and today we want to highlight the projects we're supporting. Of the 89% of people who screened positive for major depression through Mental Health America's online survey last year, 79% do not want to pursue psychotherapy or medications, yet 50% want access to digital tools.