tastroder - firstly, our primary aim is to democratise AI and data in general so it is no longer driven by developers. Most non-technical people I've spoken to

tastroder - firstly, our primary aim is to democratise AI and data in general so it is no longer driven by developers. Most non-technical people I've spoken to have no idea what AWS, Azure, etc are and where 'the cloud' is. These platforms rightly so are built for developers. This is an immediate source of bias when the overwhelming majority of developers are male.(here is a really interesting article https://www.linkedin.com/pulse/give-up-gender-gap-science-cs-schuster https://www.linkedin.com/pulse/give-up-gender-gap-science-cs...) Given you understand how cognito works I'm sure you dont mind creating an account with one of the many gmail accounts you most likely control and therefore you wont be exposing any meaningful data. Data is encrypted in transit and at rest. There are also other propriety features we are developing to limit the extent to which data can be copied with out permission. On bias - Data is stored with meta descriptive properties and attributes, which provide the user with a guide to the underlying data composition and therefore can be used to highlight potential bias. Our dev horizon also include similar monitoring capabilities to provide information that can assist users detect bias. The truth here is simply - AI are the products of algorithms, data and developer There is only one source of bias in this statement - developer algorithms are tools; data is information without intended action; developers use these to create intended action. The most obvious way to remove bias from technology is to improve diversity in the pool of technology creators. We are approaching this by increasing the size of the pool by enabling non-technical people to be a part of AI development. As well as increasing the ease at which collaboration occurs in AI development. This begins with the safe sharing of data, which in turn means more data becomes widely distributed for use in ML. It continues with a collaborative approach to data modelling, in which the domain expert leads the process - removing more opportunity for bias introduced by developers who may not have deep domain knowledge and who therefore may not fully grasp the consequences of modelling decisions. And similarly with collaboration on AI development, deployment and monitoring. Imagine a future in which 10 billion people can build their own AI and not have to ask the 100 million developers to do it for them.