more examples and solutions, and Relevant ethical issues

Through more reading, I found other examples that fit

A large body of research also suggests solutions, including (Delpierre (2018) claimed some solutions.

Data collection:The data used to train algorithms and make decisions should be diverse and representative of all populations. This includes consideration of factors such as race, gender, socioeconomic status and other relevant variables.

Data cleaning: A data cleaning process should be performed to remove any irrelevant or redundant information and to ensure that the data is of high quality. This helps to minimise the risk of introducing bias into the data.

However, further solutions needed…

(A separate ethical issue, is data surveillance good or bad?)

e.g. (Johnson, 2020) Continuous monitoring: Regular monitoring of the data and algorithms used for decision making is important to identify and address any potential biases.

Overall, big data is a valuable tool for companies and job seekers alike. But reducing bias in big data is an ongoing process that requires constant attention and effort. By following these steps, companies and organisations can help ensure that the data and algorithms they use are as fair and unbiased as possible.

Then i need to Identify gaps or areas that need further research….