The Promise and Perils of Data Science in the Wild Data Science - PowerPoint PPT Presentation

The Promise and Perils of Data Science in the Wild Data Science & Society Seminar | eScience Institute Community Seminar Brittany Fiore-Gartland, Ph.D. Anissa Tanweer, Ph.C. eScience Institute Department of Communication Human Centered Design and Engineering

Overview: Ethics in Data Science ● Where are ethics in Data Science? ● Your ethical concerns in data science ● Ethical Challenges of Doing Data Science (perils) ● Data Science as Ethical Intervention (promise)

Where are ethics in data science? ● Laws and rules ● Policies and procedures ● Cultural norms and practices Adapted from Sandra Braman (2006) Change of State: Information, Policy, and Power

Ethical Cases: Challenges of ● Boston’s StreetBump Pothole App Doing Data ● Uber’s “Rides of Glory” Analysis Science ● Microsoft’s Offensive Twitter Bot The perils!

CASE #1 Boston’s StreetBump Pothole App ● App that citizens download to phone ● Accelerometer and GPS detect when a car hit a pothole ● Automatically reports potholes to city ● City knows where to go to fix potholes

Diversity

Diversity Discrimination

Diversity Discrimination Fallacy of technical solutions

CASE #2 Uber’s “Rides of Glory” Analysis ● Rides from 10 pm-4 am on Fri/Sat, followed by ride from same location 4-6 hrs later ● Implies people “found love that you might immediately regret upon waking up the morning after” ● Calls these “Rides of Glory”

Diversity Discrimination Fallacy of technical solutions

Diversity Discrimination Fallacy of technical solutions Consent

Diversity Discrimination Fallacy of technical solutions Consent Privacy

Diversity Discrimination Fallacy of technical solutions Consent Privacy Interpretation

CASE #3 Microsoft’s Offensive Twitter Bot ● Tay, persona of American teen girl ● Similar MS bots in China and Japan ● Tay’s tweets quickly become racist and misogynistic ● Blamed on internet trolls who manipulated ML algorithm to have Tay say offensive things

Diversity Discrimination Fallacy of technical solutions Consent Privacy Interpretation

Diversity Discrimination Fallacy of technical solutions Consent Privacy Interpretation Opportunities for mischief

Diversity Discrimination Fallacy of technical solutions Consent Privacy Interpretation Opportunities for mischief Algorithmic accountability

Ethical Thinking Skills ● Recognition : What are the issues? ● Reason : What are the implications and ramifications of the issue? ● Responsibility : What are the responsibilities of various parties involved? ● Response : What are the actions you will take?

CASE FOR CLASS DISCUSSION Chicago’s Crime Prediction Algorithm ● “Strategic Subjects List” ● High risk for being perpetrator/victim of violent crime ● Visits by cops and social workers ● Prior record and associations with criminals and victims ● Eval of pilot: not more likely to be victims, more likely to be arrested Image by Dylan Lathrop, The Verge

1. What are the major ethical concerns, questions, or issues? 2. How, if at all, are the following Discussion arenas implicated in these issues? ○ Rules and laws? Questions ○ Policies and procedures? ○ Cultural norms and practices? 3. Who has what responsibilities in this case? 4. How would you respond if you were working as a data scientist on this project? https://tinyurl.com/dsethics

Data Science as Ethical Case: Bloomberg analysis of Amazon Prime same day service (April 2016) Intervention The promise!

Thank you. Questions? Brittany Fiore-Gartland, Ph.D. Anissa Tanweer, Ph.C. eScience Institute Department of Communication Human Centered Design and Engineering tanweer@uw.edu fioreb@uw.edu

The Promise and Perils of Data Science in the Wild Data Science - PowerPoint PPT Presentation

The Promise and Perils of Data Science in the Wild Data Science & Society Seminar | eScience Institute Community Seminar Brittany Fiore-Gartland, Ph.D. Anissa Tanweer, Ph.C. eScience Institute Department of Communication Human Centered

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