- Who will be impacted? Who were sampled? Who were over sampled or under sampled? Who were the data scientists (yourself and your collaborators)?
- It is highly useful for common man who is interested in investing with a low financial knowledge. Stock data and the news data are sampled. Since the data taken covers about 725 companies there would be an imbalance in the stock and the news price which is already covered in the article. Coming to the data scientists who worked on this, we are a group of three consisting of Charan Tej Thota, Venkata Sri Rohita Goparaju and Siva Kumar Buddi.
- What are the social and cultural impacts? What are the concerns about data privacy, security, and fairness?
- This highly helps the people who have minimum knowledge in the financial field. The data we have taken is from a reliable source called Kaggle, and did not violate any rules related to privacy or security. When coming to the fairness of the data, we did not take any extra effort on how fair the news data is or whether there is any bias on the data. It can be added as one of the future work improvements to be done on the data.
- When will the social and cultural impacts take place? When should people be concerned about data privacy, security, and fairness?
- Since the data we have chosen to work with is a highly open, the privacy and security policies are not very confined. By saying so, the terms and conditions for the data protection should also be followed when using the data in the application. When we wanted to use this data in our application, we were quite aware that this would lead to a positive impact on the social and economical aspects in the area of financial advancement and lead to the brighter side of development.
- Where will the social and cultural impacts take place? Where will data privacy, security, and fairness issues, like data breach, and evaluative bias, likely to happen?
- The bias or breach issues are most likely to take place for the news archive data. When dealing with the news data, there may be some of the fairness issues which need to be addressed as well. It would be one of the challenging and interesting future advancement work for the current project.
- Why are the social and cultural impacts important or consequential to the people and/or the community? Why should we be concerned about data privacy, security, and fairness issues?
- Since the application provides information regarding all the companies without showing any bias towards any company, there is an importance for the data security and fairness. Also, since the application is not only used by a larger sector of financial investors but also by the beginners on this field, it is highly important to take the social impacts of the application into consideration
- How can we address these societal issues in ML using a community-in-the-loop approach?
- There is a high importance for the involvement of human factors in ML approach. This kind of approach allows the identification of problems and requirements that may not be easily identified by other means of simulation. This kind of interactive simulation includes special kind of human operations and physical involvement.