STATS 184: Societal Impacts of Data
This course considers the impacts that data collected today have upon individuals and society. The rapid increase in the scale and types of data collected has impacted commerce and society in new ways. This course considers the economic, social, ethical, legal and political impacts of data, especially that collected on human behavior. Topics include privacy and data protection, intellectual property and confidentiality, sample selection and algorithms, equality and anti-discrimination.
A detailed description of the class is available here.
The Bruin Learn course page is here.
Motivation and Synopsis
During the twentieth century, the development of statistical computing played a crucial facilitating role for the growth of the statistics discipline and the adoption of statistical methods. In the twenty-first century digital age, the amounts of data available for statistical analysis has grown tremendously, yielding new opportunities , as well as new challenges.
This course considers the impacts that the data collected today have upon individuals and society. The rapid increase in the scale and types of data collected has impacted commerce and society in new ways. In this course we consider the economic, social and ethical, legal and political impacts of data, especially that collected on human behavior. Particular topics will be privacy and data protection, intellectual property and confidentiality, sample selection and algorithms, equality and anti-discrimination.
This course is intended to provide students sociological, psychological and economic lenses to explore how our increasingly digital lifestyle changes institutions and social relations. These lenses are then used to guide data scientists in their work.
The course has various parts:
- We’ll consider what data is and the growth of Big Data.
- We will consider the role data play in the broader information context and how that context influences the data we collect and the related statistical issues.
- We will consider statistical techniques to improve privacy and data protection, confidentiality, sample selection and algorithms, equality and anti-discrimination.
Syllabus of the Course
| Lecture | Topics |
| 1 | Introduction: What, really, is Data? |
| 2 | Contextualizing Data |
| 3 | Small Data and Big Data |
| 4 | Datafication and its Implications |
| 5 | Statistical methods to preserve privacy |
| 6 | Differential Privacy / Gaussian Differential Privacy |
| 7 | Statistical Privacy via Synthetic Data |
| 8 | Understanding Fairness in Representations |
| 9 | Equality and anti-discrimination |
| 10 | Intellectual and Personal Property | 11 | Additional topics |