  Problem statement: Standardisation of AI fairness rules and benchmarks is
challenging because AI fairness and other ethical requirements depend on
multiple factors such as context, use case, type of the AI system, and so on.
In this paper, we elaborate that the AI system is prone to biases at every
stage of its lifecycle, from inception to its usage, and that all stages
require due attention for mitigating AI bias. We need a standardised approach
to handle AI fairness at every stage. Gap analysis: While AI fairness is a hot
research topic, a holistic strategy for AI fairness is generally missing. Most
researchers focus only on a few facets of AI model-building. Peer review shows
excessive focus on biases in the datasets, fairness metrics, and algorithmic
bias. In the process, other aspects affecting AI fairness get ignored. The
solution proposed: We propose a comprehensive approach in the form of a novel
seven-layer model, inspired by the Open System Interconnection (OSI) model, to
standardise AI fairness handling. Despite the differences in the various
aspects, most AI systems have similar model-building stages. The proposed model
splits the AI system lifecycle into seven abstraction layers, each
corresponding to a well-defined AI model-building or usage stage. We also
provide checklists for each layer and deliberate on potential sources of bias
in each layer and their mitigation methodologies. This work will facilitate
layer-wise standardisation of AI fairness rules and benchmarking parameters.
