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Algorithmic fairness in artificial intelligence for medicine and healthcare
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …
Algorithms at work: The new contested terrain of control
The widespread implementation of algorithmic technologies in organizations prompts
questions about how algorithms may reshape organizational control. We use perspective of …
questions about how algorithms may reshape organizational control. We use perspective of …
Wilds: A benchmark of in-the-wild distribution shifts
Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …
Accountable artificial intelligence: Holding algorithms to account
Artificial intelligence (AI) algorithms govern in subtle yet fundamental ways the way we live
and are transforming our societies. The promise of efficient, low‐cost, or “neutral” solutions …
and are transforming our societies. The promise of efficient, low‐cost, or “neutral” solutions …
Measurement and fairness
We propose measurement modeling from the quantitative social sciences as a framework for
understanding fairness in computational systems. Computational systems often involve …
understanding fairness in computational systems. Computational systems often involve …
The measure and mismeasure of fairness
The field of fair machine learning aims to ensure that decisions guided by algorithms are
equitable. Over the last decade, several formal, mathematical definitions of fairness have …
equitable. Over the last decade, several formal, mathematical definitions of fairness have …
[HTML][HTML] Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward
Decision-making on numerous aspects of our daily lives is being outsourced to machine-
learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in …
learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in …
The accuracy, fairness, and limits of predicting recidivism
Algorithms for predicting recidivism are commonly used to assess a criminal defendant's
likelihood of committing a crime. These predictions are used in pretrial, parole, and …
likelihood of committing a crime. These predictions are used in pretrial, parole, and …
The ethnographer and the algorithm: beyond the black box
A common theme in social science studies of algorithms is that they are profoundly opaque
and function as “black boxes.” Scholars have developed several methodological …
and function as “black boxes.” Scholars have developed several methodological …
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the
likelihood that a criminal defendant will reoffend at a future point in time. Although such …
likelihood that a criminal defendant will reoffend at a future point in time. Although such …