Open discussion: Research papers on bias and fairness in AI/ML

Given the great interest, confusion and controversy around bias and fairness in AI/machine learning algorithms & models, we've decided to continue this discussion.
In our April meetup we will meet at a local coffee shop (location will be shared with folks who RSVP) and discuss academic research papers that go deeper into this critical topic.
List of proposed papers is below. Please read at least one of these so we can have a meaningful discussion. We'll kick off the meetup with list of definitions and concepts to help frame the discussion.

• On Formalizing Fairness in Prediction with Machine Learning
  http://www.fatml.org/media/documents/formalizing_fairness_in_prediction_with_ml.pdf
• The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making
  https://people.mpi-sws.org/~gummadi/papers/process_fairness.pdf
• Bayesian Modeling of Intersectional Fairness: The Variance of Bias
  https://arxiv.org/pdf/1811.07255.pdf
• Fairness Through Awareness https://arxiv.org/pdf/1104.3913.pdf

Some of the questions we will cover, in addition to anything else folks would like to discuss.
• What is fair/fairness? http://www.fatml.org/media/documents/formalizing_fairness_in_prediction_with_ml.pdf
• What is outcome bias? http://www.fatml.org/media/documents/formalizing_fairness_in_prediction_with_ml.pdf
• What is process fairness vs. outcome fairness? https://people.mpi-sws.org/~gummadi/papers/process_fairness.pdf
• What is the cost of process fairness? https://people.mpi-sws.org/~gummadi/papers/process_fairness.pdf
• What is the trade off between process fairness and accuracy? https://people.mpi-sws.org/~gummadi/papers/process_fairness.pdf
• What is intersectional fairness? https://arxiv.org/pdf/1811.07255.pdf
• How can a classifier trained to predict outcomes based on historical data be potentially unfair? https://arxiv.org/pdf/1811.07255.pdf
• What is group fairness vs. individual fairness? https://arxiv.org/pdf/1104.3913.pdf
• Other key concepts/definitions: Loss function, statistical parity. similarity metric, classifiers, infra-marginality, etc.