Fairness and biases in predictive algorithms

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  • Kathy O’Neill: Weapons of Math Destruction
  • ProPublica mandatory sentencing reporting (On Machine Bias)
    • COMPASS: for-profit proprietary software sold to court systems (mostly states) for scoring risk of recidivism
    • Used for decisions in
      • Parole
      • Bail
      • Sentencing
    • ProPublica
    • When considering an algorithm, one has to choose between 3 types of fairness (pick 2; can’t have all 3)
  • Miriad interesting types of social uses for algorithms
    • Police brutality

Kathy O’Neill’s concept of WMDs (Weapons of Math Destruction):

  • things that are
    • Large-scale
    • Detrimental to society
  • Often, the source of algorithmic bias is not the algorithm itself, but the data that is fed to it. For example, there can be positive feedback loops (crime)
  • The human using the algorithm often adds an extra layer of human bias. E.g. a sentencing judge can use a risk score as one input. But that’s also subject to human bias
    • And, over time, as society becomes more comfortable with algorithms, it’s possible that humans will put increasing weight on algorithms over time
  • To get at algorithms that are proprietary, there are examples of ppl getting proprietary algorithm inputs and outputs through data transparency requests, then reverse engineering how the algorithm works
  • Recidivism algorithms can be based on quizzes to prisoners
  • There’s also a question of measuring the “success” of algorithms
  • Algorithms are notoriously bad at context; they can only know what they’ve been fed
  • Humans haven’t agreed on what we ourselves want to optimize for:
    • Fairness?
    • Redistribution
    • Safety
    • Making money
  • Secret algorithms: even if they’re open, that might not be enough. The training data and inputs is an absolutely essential part of the equation
  • The drivers for producing and releasing algorithms are often money, not desire to make the world better.
  • And, sometimes even when the motive is benevolent or benign, there are failures of imagination in terms of how people will use it for evil.
  • China: “Life score” – maybe Heibo?
  • For things like credit scores and predictive algorithms for recidivism, it’s important to consider whether or not the person affected has a method of redress
  • Data can be used for good too:
    • HRDAG – Human Rights Data Analysis Group
    • Social media analysis for predictions of violence
  • Solutions:
    • Transparency
    • Avenues of recourse
    • Human training
    • Can’t outlaw variables, but you could potentially regulate against known biases
    • Any kind of pushback will require tons of statisticians and data scientists pushing for social justice