Data plays a crucial role in decision-making, understanding the world, and shaping our future. However, with great power comes great responsibility. As such, ethical considerations surrounding data products are becoming increasingly important. In this blog post, we will explore the fundamentals of data ethics through real-world examples from the fields of data science and data engineering.
Topics in Data Ethics
Data ethics is a discipline that studies the implications of how data is collected, stored, used and shared. It is about making sure data is being managed and used responsibly. At its core, data ethics involves considering the legal and moral implications of data collection, storage, and use, as well as understanding the potential risks associated with data, such as privacy and security concerns.
- Need for recourse and accountability: Mechanisms have to be put in place for individuals to seek recourses if their data is mishandled and for organizations to take accountability for potential data misuse.
- Feedback loops: The latter can amplify and / or perpetuate in the data.
- Bias: Can lead to unfair or discriminatory treatment of individuals or groups.
- Disinformation: A significant issue in the digital age as the proliferation of false or misleading information can have serious consequences for individuals and society as a whole.
Recourse and Accountability
When a complex system is in place, it can be difficult for any one person to take responsibility for the outcomes. This lack of accountability can lead to negative results. For example, a bug in Arkansas healthcare system caused people with cerebral palsy to lose access to necessary care. In this case, the creator of the algorithm blamed government officials, while government officials blamed those who implemented the software. Professor Danah Boyd has referred to this as an extension of bureaucracy, which is often used as a way to evade responsibility.