• Corpus ID: 53961090

Algorithmic Bias : A Counterfactual Perspective

@inproceedings{Cowgill2017AlgorithmicB,
  title={Algorithmic Bias : A Counterfactual Perspective},
  author={Bo Cowgill and Catherine Tucker},
  year={2017}
}
We discuss an alternative approach to measuring bias and fairness in machine learning: Counterfactual evaluation. In many practical settings, the alternative to a biased algorithm is not an unbiased one, but another decision method such as another algorithm or human discretion. We discuss statistical techniques necessary for counterfactual comparisons, which enable researchers to quantify relative biases without access to the underlying algorithm or its training data. We close by discussing the… 
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References

SHOWING 1-8 OF 8 REFERENCES
False Positives, False Negatives, and False Analyses: A Rejoinder to "Machine Bias: There's Software Used across the Country to Predict Future Criminals. and It's Biased against Blacks"
PROPUBLICA RECENTLY RELEASED a much-heralded investigative report claim­ ing that a risk assessment tool (known as the COMPAS) used in criminal justice is biased against black defendants.12 The
Fairer and more accurate, but for whom?
TLDR
A model comparison framework for automatically identifying subgroups in which the differences between models are most pronounced, with a primary focus on identifying sub groups where the models differ in terms of fairness-related quantities such as racial or gender disparities is introduced.
Human Decisions and Machine Predictions
TLDR
While machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.
Algorithmic bias? An empirical study into apparent gender-based discrimination in the display of STEM career ads
We explore data from a field test of how an algorithm delivered ads promoting job opportunities in the Science, Technology, Engineering and Math (STEM) fields. This ad was explicitly intended to be
Estimating causal effects of treatments in randomized and nonrandomized studies.
A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented. The objective is to specify the benefits of randomization in estimating
Automating Judgement and Decisionmaking : Theory and Evidence from Résumé Screening
What types of decisionmaking tasks are better automated? And which are better left to judgement? I develop a formal model of the comparative advantages of human judgement and machines in
Towards A Rigorous Science of Interpretable Machine Learning
TLDR
This position paper defines interpretability and describes when interpretability is needed (and when it is not), and suggests a taxonomy for rigorous evaluation and exposes open questions towards a more rigorous science of interpretable machine learning.
How algorithms impact judicial decisions
  • 2017