Building Trust in AI through Transparency and Governance

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.