Properly trained versions derived from biased or non-evaluated data may lead to skewed or undesired predictions. Biased versions may cause detrimental outcomes, therefore furthering the negative impacts on society or goals. Algorithmic bias is a possible results of data not being entirely geared up for training. Machine learning ethics is starting … Read More