Using a fixed target ratio C for defining an unbiased dataset presents several limitations:
Context Dependence: A fixed ratio may not account for the specific context of the dataset or the domain in which it is applied. Different fields may have varying standards for what constitutes fairness, and a one-size-fits-all approach could lead to inappropriate conclusions about bias.
Historical Inequities: Setting a fixed target ratio may inadvertently reinforce existing societal biases. For instance, if historical data reflects systemic discrimination, using a fixed ratio could perpetuate these inequities rather than address them.
Dynamic Populations: Populations are not static; they change over time due to various factors such as migration, policy changes, and social dynamics. A fixed ratio may become outdated, failing to reflect the current demographic landscape.
Lack of Nuance: A single ratio does not capture the complexity of bias, which can manifest in multiple dimensions (e.g., race, gender, age). This simplification may overlook important disparities that require more nuanced approaches.
To generalize this concept further, the definition of an unbiased dataset could incorporate:
Adaptive Ratios: Instead of a fixed C, the model could allow for adaptive ratios that change based on the characteristics of the dataset and the context in which it is used. This could involve using statistical methods to determine optimal ratios based on historical data and current trends.
Multi-dimensional Fairness Metrics: Incorporating multiple fairness metrics that consider various demographic factors simultaneously. This would allow for a more comprehensive understanding of bias and fairness in the dataset.
Stakeholder Input: Engaging with stakeholders, including community representatives and domain experts, to define what constitutes fairness in their specific context. This participatory approach can lead to more relevant and accepted definitions of unbiased datasets.
By moving beyond a fixed target ratio, the bias measurement framework can become more flexible and responsive to the complexities of real-world data and societal dynamics.