Design for responsible AI with HAX
Machine learning (ML) is everywhere now. It may not be the flexible, general artificial intelligence promised by science fiction stories, but it’s a powerful alternative to rules engines and brute force image and voice recognition systems. But one big problem remains: Modern AI is composed of black-box systems that are only as good as their training data.The underlying nature of the ML-powered modules we drop into our applications and services raises new questions about how we design our applications. If we don’t know exactly how an application makes decisions, how can we inform its operators and users? We need some way to design that mix of certainty and uncertainty into our code so that we can show users that, while applications make decisions, they need to remain aware of algorithmic bias and other fundamental sources of error.To read this article in full, please click here
Machine learning (ML) is everywhere now. It may not be the flexible, general artificial intelligence promised by science fiction stories, but it’s a powerful alternative to rules engines and brute force image and voice recognition systems. But one big problem remains: Modern AI is composed of black-box systems that are only as good as their training data.
The underlying nature of the ML-powered modules we drop into our applications and services raises new questions about how we design our applications. If we don’t know exactly how an application makes decisions, how can we inform its operators and users? We need some way to design that mix of certainty and uncertainty into our code so that we can show users that, while applications make decisions, they need to remain aware of algorithmic bias and other fundamental sources of error.
