Bias in word embeddings, Papakyriakopoulos et al., FAT*’20 There are no (stochastic) parrots in this paper, but it does examine bias in word embeddings, and how that bias carries forward into models that are trained using them. There are definitely some dangers to be aware of here, but also some cause for hope as we ... Continue Reading

Cloudy with a high chance of DBMS: a 10-year prediction for enterprise-grade ML, Agrawal et al., CIDR'20 "Cloudy with a high chance of DBMS" is a fascinating vision paper from a group of experts at Microsoft, looking at the transition of machine learning from being primarily the domain of large-scale, high-volume consumer applications to being ... Continue Reading

POTS: Protective optimization technologies, Kulynych, Overdorf et al., arXiv 2019 With thanks to @TedOnPrivacy for recommending this paper via Twitter. Last time out we looked at fairness in the context of machine learning systems, coming to the realisation that you can't define 'fair' solely from the perspective of an algorithm and the data it is ... Continue Reading

The measure and mismeasure of fairness: a critical review of fair machine learning, Corbett-Davies & Goel, arXiv 2018 With many thanks to Ben Fried and the ACM Queue editorial board for the paper recommendation. We've visited the topic of fairness in the context of machine learning several times on The Morning Paper (see e.g. [1]1, ... Continue Reading

Programmatically interpretable reinforcement learning, Verma et al., ICML 2018 Being able to trust (interpret, verify) a controller learned through reinforcement learning (RL) is one of the key challenges for real-world deployments of RL that we looked at earlier this week. It's also an essential requirement for agents in human-machine collaborations (i.e, all deployments at some ... Continue Reading

Challenges of real-world reinforcement learning, Dulac-Arnold et al., ICML'19 Last week we looked at some of the challenges inherent in automation and in building systems where humans and software agents collaborate. When we start talking about agents, policies, and modelling the environment, my thoughts naturally turn to reinforcement learning (RL). Today's paper choice sets out ... Continue Reading

Optimized risk scores Ustun & Rudin, KDD'17 On Monday we looked at the case for interpretable models, and in Wednesday’s edition of The Morning Paper we looked at CORELS which produces provably optimal rule lists for categorical assessments. Today we’ll be looking at RiskSLIM, which produces risk score models together with a proof of optimality. ... Continue Reading

Learning certifiably optimal rule lists for categorical data Angelino et al., JMLR 2018 Today we’re taking a closer look at CORELS, the Certifiably Optimal RulE ListS algorithm that we encountered in Rudin’s arguments for interpretable models earlier this week. We’ve been able to create rule lists (decision trees) for a long time, e.g. using CART, ... Continue Reading