There are some ethical considerations in data analysis that one might have to uphold the integrity and social responsibility of the analysis process. Here are some of them:
1. Privacy and confidentiality - As data analysts, we must ensure that company data, especially those that are personal, are kept confidential and used in compliance with data protection laws like GDPR. Analysts must also consider ethics when using data that has been stolen and data from individuals from whom consent has not been given.
- Bias and Fairness - Analysts must also be aware of biases in data, which can result from the way data is collected, processed, or interpreted. It is important that we provide context to data in order to prevent the perpetuation or exacerbation of social inequalities.
- Ethical Use of Data - Analysts have consider how their work will be used, asking questions like how does their analyses impact on individuals, groups of people, and society as a whole. There may be negative ramifications that stem from their work when there is misuse of data and their analyses.
- Conflicts of Interest - Analysts must remain vigilant about conflicts of interests that might influence their analysis. It is best practice to disclose any potential conflicts of interests before any work has begun.
- Societal Impact - Analysts should consider the societal implications of their work, especially those that are harmful to groups of people and society at large. Data analysis should work towards improving the human condition, as well as the planet and other living inhabitants.
- Intellectual Property - Analysts should always cite data sources and give proper attribution for them.
- Laws and Regulations - All laws and regulations must be adhered to in the region and geographic locations of where the company and analysts reside, which may or may not be the same.
- Accuracy and Reliability - Analysts should work to ensure that the data used is accurate and reliable. Their methods of data cleaning and analysis must not lead to incorrect conclusions that can be potentially harmful, socially as well as monetarily.
- Informed Consent - Analysts should always obtain informed consent from data subjects when personal data is being used. Data subjects should also be informed of how their data will be used and what types of methods will be employed to protect privacy and confidentiality.