In a world where the algorithm can control the lives of society, it is not surprising that specific complications in determining the fairness in the algorithmic decision will arise at some point. Machine learning has been the de facto tool to forecast a problem that humans cannot reliably predict without injecting some amount of subjectivity in it (i.e., eliminating the “irrational” nature of humans). In this paper, we proposed a framework for defining a fair algorithm metric by compiling information and propositions from various papers into a single summarized list of fairness requirements (guideline alike). The researcher can then adopt it as a foundation or reference to aid them in developing their interpretation of algorithmic fairness. Therefore, future work for this domain would have a more straightforward development process. We also found while structuring this framework that to develop a concept of fairness that everyone can accept, it would require collaboration with other domain expertise (e.g., social science, law, etc.) to avoid any misinformation or naivety that might occur from that particular subject. That is because this field of algorithmic fairness is far broader than one would think initially; various problems from the multiple points of view could come by unnoticed to the novice’s eye. In the real world, using active discriminator attributes such as religion, race, nation, tribe, religion, and gender become the problems, but in the algorithm, it becomes the fairness reason.
Age and Gender Detection Using CNN