Does your organisation use a Responsible AI?
Microsoft Accessible AI Guidelines
Developing a Responsible AI approach presents a difficulty for numerous organizations, prompting Microsoft to establish standardized Responsible AI guidelines accessible for adoption by other companies and machine learning experts.
A Complete AI Lifecycle
These practices cover the entire AI lifecycle, from creation to implementation, and include tools like a Responsible AI impact assessment template that helps users evaluate AI applications, maintain data integrity, and identify potential negative impacts.
AI Accountability Assurance
Microsoft supports AI responsibility by providing tools and research for developers. These include the Responsible AI Dashboard for debugging machine learning models, a feature of Azure Cognitive Services aligned with Responsible AI principles.
Fairness
Ensure that AI systems do not exhibit unjust or biased behavior, treating all individuals and groups equitably.
Inclusiveness
Incorporate diverse perspectives and avoid exclusion to create AI technologies that benefit and serve a wide range of users.
Safety
Implement measures to prevent harm, both physical and psychological, while deploying AI systems and minimizing risks associated with their operation.
Accountability
Build AI systems that consistently perform as intended, delivering accurate and dependable results across various scenarios.
Reliability
Hold individuals and organizations responsible for the design, development, and consequences of AI systems, including addressing any unintended outcomes.
Transparency
Provide clear and understandable explanations of AI system behavior, decisions, and processes to enhance comprehension and promote trust among users and stakeholders.
AI Case studies
Leveraging smart workflow and automation
Leading Professional Service organisation selling SaaS solutions leveraged Azure OpenAI Services, CharGPT, and machine learning to deliver continuous product innovation. They now apply automation at all levels with embedded generative AI assistant.
Read the Case StudyImprovement with Collaborative AI
German power supplier had to go through a long manual process of checking the power line and maintaining it. With new digital solutions, the supplier aims to increase the efficiency and safety of the process.
Read the Case StudyAutomatic translation of documents
A global automotive conglomerate operates internationally and with the help of Azure AI improved translation efficiency, precision, and cost-effectiveness to meet growing demands and evolving communication needs.
Read the Case StudyPre-Owned Vehicle Market with AI
Becoming the largest retailer of pre-owned vehicles in the United States through the transformation of the used car buying process with AI.
Read the Use CaseScaling Retail Optimization with Azure AI
Usage of Microsoft Azure Automated machine learning to enhance image recognition and drive retail success.
Read the Use CaseDeath to the tickets
From time entry automation to game-changing efficiency - the journey of transforming customer support.
Read the Use CaseProven competence
Awards & certifications
