although data and computational analysis may make us think that we are receiving objective informationthis is not the casebeing based on data does not mean that machine learning outputs are neutral human bias plays role in how data is collectedorganizedand ultimately in the algorithms that determine how machine learning will interact with that data iffor examplepeople are providing images for "fishas data to train an algorithmand these people overwhelmingly select images of goldfisha computer may not classify shark as fish this would create bias against sharks as fishand sharks would not be counted as fish when using historical photographs of scientists as training dataa computer may not properly classify scientists who are also people of color or women in factrecent peer-reviewed research has indicated that ai and machine learning programs exhibit human-like biases that include race and gender prejudices seefor example "semantics derived automatically from language corpora contain human-like biasesand "men also like shoppingreducing gender bias amplification using corpus-level constraints[pdfas machine learning is increasingly leveraged in businessuncaught biases can perpetuate systemic issues that may prevent people from qualifying for loansfrom being shown ads for high-paying job opportunitiesor from receiving same-day delivery options because human bias can negatively impact othersit is extremely important to be aware of itand to also work towards eliminating it as much as possible one way to work towards achieving this is by ensuring that there are diverse people working on project and that diverse