Thinking About Algorithmic Transparency

Jennifer A. Stark, Ph.D.

Nicolas Diakopoulos, Ph.D.

Why Investigate?

• • •
  • Black-Box Decisions
  • No Recourse
  • Blind Acceptance, "Not our fault"
  • Ethics???
  • What are Engineers told? What information are they given?

    • Potential Findings

  • Find algorithm/organisation at fault
  • Find algorithm reveals embedded societal disparities

    • Typically the two are tightly related

    Difficult to identify a solution

    "Because with Uber there is no destination discrimination — no refusals based on what you look like or where you live." – Uber Under the Hood, Medium

    Dataset #1:

    – Via Uber's Developer API late 2016

  • Average UberX estimated wait times
    • Overall
    • During surge only
    • During non-surge
    • Just before surge (trigger)
  • Percent time spent surging (Proportion)
  • Average Surge price multiplier per census tract

    • UberX Overall Average Wait Times

    What information can we gather to reproduce wait times across D.C.?

    Information to Explore - Census

  • Population => density
  • Poverty => %
  • Median Household Income
  • Race / Ethnicity => dichotomised to % POC

    • Run multiple regression analysis.

    “Cycles, Systems, & Loops”

    Additional Information:

  • Transient Population (where people go)
    • Proxy: Non-violent Crime => 5yrs, density
    • Proxy: Google Places API => density
  • Unbanked (no credit card = barrier for Uber App)
  • Demand (Proxy: Taxi data)
  • Risk - Proxy: Violent Crime => 5yrs, density
  • Spatial Regression - spatial adjacency

    • Results!

    Interpretation

  • Wait times reflect questionable ethics (bad Uber!?)
  • - Ineffective control of drivers
  • Wait times reveal disadvantaged communities (bad Government!?)

    • Journalistic Outcomes

  • Uber policy? – Change algorithm? Change driver-incentives? Make data available?
  • Government policy? – Ban Uber? Regulate Uber? Invest in those communities so they can participate in the technological future
  • Public attitude? – informed decisions

    • What about us?

    Why?

    • • •
  • Black-Box Decisions
  • No Recourse
  • Blind Acceptance
  • Ethics?

    • And also …

  • Encourage better documentation and code commenting (good for future you and colleagues)
  • Build trust with readers
  • Educational
  • Catalyse new projects

    • Transparency - my open DDJ Guide using GitHub

    Thank you!

    @_JAStark

    jastark@protonmail.com

    Template for DDJ Transparency using GitHub:

    https://github.com/JAStark/cookiecutter-data-driven-journalism