SURV699: Ethical Considerations for Data Science Research

Area: 
Data Output/Access, Research Question
Credit(s)/ECTS: 
1/2
Core/Elective: 
Elective

Networked technologies—including the internet of things (IoT), wearables, ubiquitous sensing, social sharing platforms, and other AI-driven systems—are generating a tremendous amount of data about individuals, companies, and societies. These technologies provide a range of new opportunities for data scientists and researchers to understand human behavior and develop new tools that benefit society.  At the same time, the ease with which data can be collected and analyzed raises a wide range of ethical questions about these technologies, their creators, and their users.

In recent years, we have seen numerous examples of research and technologies that are ethically problematic. For example, Facebook’s Cambridge Analytica scandal revealed researchers using problematic tactics to collect profile data from millions of Facebook users. In addition, algorithms and machine learning techniques have been revealed as systematically biased in how they evaluate resumes[1], recommend parole for prisoners[2], decide where police units should deploy[3], and identify people through facial recognition technology[4], just to name a few.

Therefore, it is critical that data scientists and others who will be working with big data can critically assess the potential risks and benefits of any end products, whether they are developing a search engine or a tool for detecting terrorists. This course will provide an overview of key ethical issues that arise when working with big data, and it will provide opportunities to review and reflect on past mistakes in this space.

 

[1] https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G

[2] https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

[3] https://www.newscientist.com/article/mg23631464-300-biased-policing-is-made-worse-by-errors-in-pre-crime-algorithms/

[4] https://www.nytimes.com/2019/04/03/technology/amazon-facial-recognition-technology.html

Course objectives: 

By the end of the course, students will…

  • Describe the history of research ethics and the goals of institutional review boards
  • Describe the challenges data science and big data raise for protecting individuals’ rights and privacy
  • Identify ethical issues in the study design, data collection, and data analysis process
  • Detail best practices for conducting ethical research
Grading: 

Grading will be based on:

  • Participation in discussion during the weekly online meetings and contributions to weekly discussion forums demonstrating understanding of the required readings and video lectures (10% of grade)
  • Four open-book quizzes assessing comprehension of course material (20% of grade; 5% each)
  • Three online homework assignments reviewing specific aspects of the material covered (45% of grade; 15% each)
  • Final paper covering overarching themes of the class (25%).

A+  100 - 97       C+   79-77    F 59 or below
A     96 – 93       C     76-73
A-    92 – 90       C-    72-70
B+   89 – 87       D+   69-67
B     86 – 83       D     66-63
B-    82 – 80       D-    62-60

The grading scale is a base scale recommended by the IPSDS. Variations for grading on a scale are at the discretion of the instructor.

Dates of when assignment will be due are indicated in the syllabus. Extensions will be granted sparingly and are at the instructor's discretion. If you know you will not be able to meet a deadline, email the professor before the due date to request an extension. If an assignment is submitted late and no extension has been given, a 10% penalty will be applied for each day it is late.

Prerequisites: 

No prerequisites.

 

Course Dates

2020

Fall Semester (September – December)

2021

Fall Semester (September – December)