Twitter has started a collaborative effort to improve its Machine Learning algorithms. The company-wide initiative is called Responsible ML. It aims to have responsible, responsive, and community-driven machine learning (ML) systems incorporated into its algorithms. Machine learning is a branch of computer science that makes computers decide on their own. It is a more advanced form of artificial intelligence (AI).

As AI reaches critical momentum across industries and applications, it becomes essential to ensure the safe and responsible use of AI. AI deployments are increasingly impacted by the lack of customer trust in the transparency, accountability, and fairness of these solutions. Microsoft is committed to the advancement of AI and machine learning (ML), driven by principles that put people first, and tools to enable this in practice. In collaboration with the Aether Committee and its working groups, we are bringing the latest research in responsible AI to Azure. Let's look at how the new responsible ML capabilities in Azure Machine Learning and our open-source toolkits empower data scientists and developers to understand ML models, protect people and their data, and control the end-to-end ML process.

Organizations around the world are gearing up for a future powered by artificial intelligence (AI). From supply chain systems to genomics, and from predictive maintenance to autonomous systems, every aspect of the transformation is making use of AI. This raises a very important question: How are we making sure that the AI systems and models show the right ethical behavior and deliver results that can be explained and backed with data? This week at Spark AI Summit, we talked about Microsoft's commitment to the advancement of AI and machine learning driven by principles that put people first. Over the past several years, machine learning has moved out of research labs and into the mainstream and has grown from a niche discipline for data scientists with PhDs to one where all developers are empowered to participate.

As AI reaches critical momentum across industries and applications, it becomes essential to ensure the safe and responsible use of AI. AI deployments are increasingly impacted by the lack of customer trust in the transparency, accountability, and fairness of these solutions. Microsoft is committed to the advancement of AI and machine learning (ML), driven by principles that put people first, and tools to enable this in practice. In collaboration with the Aether Committee and its working groups, we are bringing the latest research in responsible AI to Azure. Let's look at how the new responsible ML capabilities in Azure Machine Learning and our open-source toolkits empower data scientists and developers to understand ML models, protect people and their data, and control the end-to-end ML process.