Abstract:
Bias in ML models can refer to inaccuracies or errors in predictions due to the training data or algorithm, while ethical biases specifically pertain to the potential for discrimination or unfair outcomes based on social or ethical considerations. They are very hard to mitigate because of the contestable nature of ethics. Ethical biases are not just technical glitches, they are societal challenges that necessitate collective attention and thoughtful solutions. To address these concerns, the introduction of a collaborative platform is suggested, fostering unity among general communities, developers, researchers, practitioners, and policymakers. The proposed solution comprises three main implementations.
