Responsible Data Science
Overview
0.1
Workshop Description
0.2
Introduction
0.3
Learning Objectives
0.4
Expectations
1
Defining Data Science
2
Dilemma
2.1
The Power (and Promises) of Data Science
2.2
The Problem of Data Science
2.3
Good or Evil?
3
History in Context
3.1
Development of Classic Methods
3.2
Emergence of the Computer
3.3
Rise of Big Data
3.4
Illusion of Computational Objectivity
4
Towards Data Ethics, Equity and Justice
4.1
Ethics
4.2
Equity
4.3
Justice
5
Promise And Perils Case Studies
5.1
Automation
5.1.1
Case 1: Robotics in Factories and Mining
5.1.2
Case 2: Automation in Public Safety
5.1.3
Case 3: Self-Driving Cars
5.2
Accessibility
5.3
Discussion
6
The FACT of Data Science
6.1
Case Study: Carbin App for Pothole Identification
6.2
Fairness The data science pipeline (from data collection and management to
6.2.1
“Correcting for Bias”
6.2.2
Case Study: Crowdsourcing City Governance
6.3
Accuracy
6.3.1
Case Study: Gender and Mentorship
6.4
Confidentiality
6.4.1
Privacy and Data Stewardship
6.4.2
De- and Re-Identification of Data
6.4.3
Case Study: New York City Taxi Data
6.5
Transparency
6.5.1
Open Access as Freedom of Information
6.5.2
Research Outcomes Ownership
6.5.3
Case Study: Everyone’s an Epidemiologist
6.6
Applying FACT
7
Assessment
Responsible Data Science
7
Assessment