• 
• 27 min
• Cornell Tech
• 2019

Podcast about worker quantification in factors such as hiring, productivity and more. Dives into the discussion on why we should attempt a fair making of algorithms. Warns specifically about how algorithms can find “proxy variables” to approximate for cultural fits like race or gender even when the algorithms is supposedly controlled for these factors.

• Algorithmic Bias
• Technology and Race
• Machine Learning

• 
• 28 min
• Cornell Tech
• 2019

Pre-trial risk assessment is part of an attempted answer to mass incarceration. Data sometimes answers a different question than the ones we’re trying to answer (data based on riskiness before incarceration, not how dangerous they are later). Essentially, technologies and algorithms which end up in contexts of social power differentials can often be abused to further cause injustice against people accused of a crime, for example. Numbers are not neutral and can even be a “moral anesthetic,” especially if the sampled data has confounding variables that collectors ignore. Engineers designing technology do not always envisage ethical questions when making decisions that ought to be political.

• Civil Surveillance
• Algorithmic Bias
• Machine Learning

• 
• 27 min
• Cornell Tech
• 2019

Solon Barocas discusses his relatively new course on ethics in data science, following a larger trend of developing ethical sensibility in this field. He shares ideas of spreading out lessons across courses, promoting dialogue, and making sure we are really analyzing problems while learning to stand up for the right thing. Offers a case study of technological ethical sensibilities through questions raised by predictive policing algorithms.

• Computing Ethics Issues
• Algorithmic Bias
• Machine Learning

• 
• 7 min
• TED
• 2017

Predictive policing software such as PredPol may claim to be objective through mathematical, “colorblind” analyses of geographical crime areas, yet this supposed objectivity is not free of human bias and is in fact used as a justification for the further targeting of oppressed groups, such as poor communities or racial and ethnic minorities. Further, the balance between fairness and efficacy in the justice system must be considered, since algorithms tend more toward the latter than the former.

• Predictive Policing
• Algorithmic Bias
• Machine Learning

• 
• 10 min
• Survival of the Best Fit
• 2018

Explores hiring bias of AI by playing a game in which you are the hiring manager.

• Algorithmic Bias
• Technology and Race
• Machine Learning

• 
• 5 min
• MIT Technology Review
• 2019

In the case of the New Orleans Police Department, along with other cities, data used to train predictive crime algorithms was inconsistent and “dirty” to begin with, making the results disproportionately targeted toward disadvantaged communities.

• Predictive Policing
• Technology and Race
• Machine Learning