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

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the promise and perils of data science in the wild
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The Promise and Perils of Data Science in the Wild Data Science - - PowerPoint PPT Presentation

Jun 04, 2023 24 likes •340 views

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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The Promise and Perils of Data Science in the Wild

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

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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)
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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

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Ethical Challenges of Doing Data Science

Cases:

  • Boston’s StreetBump Pothole

App

  • Uber’s “Rides of Glory”

Analysis

  • Microsoft’s Offensive Twitter

Bot

The perils!

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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
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Diversity

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Diversity Discrimination

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Diversity Discrimination Fallacy of technical solutions

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Diversity Discrimination Fallacy of technical solutions

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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”
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Diversity Discrimination Fallacy of technical solutions

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Diversity Discrimination Fallacy of technical solutions Consent

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Diversity Discrimination Fallacy of technical solutions Consent Privacy

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Diversity Discrimination Fallacy of technical solutions Consent Privacy Interpretation

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Diversity Discrimination Fallacy of technical solutions Consent Privacy Interpretation

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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

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Diversity Discrimination Fallacy of technical solutions Consent Privacy Interpretation

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

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

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

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

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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?
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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

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Discussion Questions

1. What are the major ethical concerns, questions, or issues? 2. How, if at all, are the following arenas implicated in these issues?

○ Rules and laws? ○ 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

  • n this project?

https://tinyurl.com/dsethics

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Data Science as Ethical Intervention

The promise!

Case: Bloomberg analysis of Amazon Prime same day service (April 2016)

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Thank you. Questions?

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