The majority of AI algorithms are very complex. It is difficult and often impossible to understand how these algorithms are making decisions, as such these algorithms are widely known as a "black box”. Use DeepTrace to learn about the decision-making process performed by AI and discover key factors that influence the predicted outcomes.
DeepTrace helps knowledge workers to trust AI
For businesses developing AI
DeepTrace will ensure the quality of the final product and help to convince consumers of its reliability and ethnicity.
For businesses using AI
DeepTrace will ensure that obtained predictions are completely transparent by providing comprehensive explanations, so you can trust these results.
DeepTrace integrates with your existing AI pipeline and makes it better, so you can set it up and run in just 4 simple steps: Connect, Query, Review and Explore
Explainable AI in Industry
Financial institutions such as Capital One and Bank of America are actively leveraging AI technology by many facets within their organizations. They are looking to bring their customers’ such benefits as financial stability, empower their financial awareness, and help them better manage their spending. All these require more sophisticated AI algorithms which provide fair, unbiased, and explainable outcomes that can be easily understood by customers and service providers. This allows financial institutions not only to ensure compliance with different regulatory requirements but also follow fair and ethical standards in machine learning.
"Traditional machine learning can help us predict events, but as end-users, we can't tell why the machine is predicting something a certain way," said Kamal Jethwani, MD, MPH, Senior Director, Partners Connected Health Innovation. This imposes significant limitations on how and where AI technology can be applied in the healthcare domain. With the help of explainable AI, doctors will be able to tell why a certain patient is at high risk for hospital admission, and what treatment would be the most suitable. This will enable doctors to act based on better information in comparison to what many of state-of-the-art machine learning algorithms can deliver today.
Security and Defense
Explainable AI is one of the current DARPA initiatives to enable “third-wave AI systems”. These systems must not only give accurate predictions but also to understand the context and environment in which they operate. It allows building explanatory models that can characterize some real-world phenomena. Such ability to produce accurate explanations plays a key role in mission-critical applications in security and defense domain. The current challenges in the security and defense sector are mainly in areas of classifying events of interest from multiple multimedia data sources and constructing decision policies to perform a variety of simulated missions.
AUROMIND is a young and exciting AI start-up, located right in Belfast city center (United Kingdom). Our team consists of experienced data scientists and software engineers who are deeply passionate about explainable AI. We are always open to new ideas and collaborations. Just drop us a message to start a conversation.