These 3 weeks of studying, gave me more ideas for my final work. For example, in Inclusive Society, I can consider more about local legal policies, tax policies, etc. The theme of our project is Data activism. The aim is to discuss the significance of data collection for communities and to reduce the possibility of exclusion from datasets, thus reducing inequality and social marginalization. It has many uses, including counter-mapping, data justice, Algorithm Auditing, and more. In addition to the authenticity of the data collection, the modification and utilization of raw data are also very important for the correct bottom line and rules.

By Building Near Future, we also learn about the ethical aspects of big data and ai data, and considering the near future, this is how big data may change in the next 5 years. Since the manipulation of big data algorithms can be very profitable for companies, and soon big data will become more and more popular in every corner of life, for example, if a company finds out that a user wants to own and buy, it will keep pushing ads to make them buy. So the ultimate goal of our project is to show that shortly, more big data will be regulated and many monopolies will be reduced, thus increasing the fairness of the market.

Finally, the class on power, date and inequality in value chains gave me basic information about value chains and the inequalities that exist in them. For example, the example in the class illustrates the attitude of some chocolate companies towards the use of child labour in the cocoa bean-producing regions of Africa and the production areas. Some companies have a very strict policy on child labour and will write about the reasons why child labour occurs and what the consequences will be when it already occurs. But some do not, and at any time there is high visibility in the industry, but since few people pay attention to this, they do not care much. There are many other concepts mentioned, such as the difficulty of small production companies entering the value chain, unequal working conditions, unequal working hours, gender inequality, etc. All of them find good companies to compare with bad ones and are able to have a good understanding and interpretation of the ESG report of the company.

WEEK 1-3 / Knowledge Integration and Project Planning: Data, Inequality and Society (2022-2023) by blogadmin is licensed under a Creative Commons Attribution CC BY 3.0