As consumers and employees integrate more of their lives into one intelligence-amplifying human augmentation, organizations will have to address issues of data transparency, privacy and autonomy. Security: Human augmentation technologies must achieve and maintain a known and acceptable state of security-related risk. This risk is across an attack surface that's no longer tied to a specific device or physical location, but may travel with the human subject. Privacy: Human augmentation provides the ability to access intimate knowledge and data about the human it's enhancing. That data must be protected.

There is thus a great need to define inputs, outputs, and their interactive relationships clearly. Inevitably, technologists would code fairness as a narrowly defined modular property of the machine learning system. However, fairness is not a well defined nor universally applicable concept, to begin with as it has to be understood amidst a particular social context. Abstracting away this context is thus an abstraction error. With the presence of this error, AI would have an ineffective, inaccurate and misguided interpretation and thus, quantification of fairness when it is introduced to varying societal systems.