Algorithmic bias is a term used to describe the tendency of algorithms to produce results that reflect or reinforce existing prejudices or disparities. This can happen in a number of ways, including but not limited to: selection bias (the algorithm only considering a subset of data that is not representative of the population as a whole), reinforcement learning (the algorithm continuing to give preferential treatment to certain groups or individuals based on previous successes or failures), and human bias (the algorithm incorporating the personal biases of those who design and operate it). While some amount of bias is inevitable in any decision-making system, it is important to be aware of its potential effects and take steps to minimize it where possible.