Four short links: 15 December 2017
Machine Teaching, Accuracy Trumps Bias, Fairness in ML, and Quantum Game
- Machine Teaching: A New Paradigm for Building Machine Learning Systems — While machine learning focuses on creating new algorithms and improving the accuracy of “learners,” the machine teaching discipline focuses on the efficacy of the “teachers.” Machine teaching as a discipline is a paradigm shift that follows and extends principles of software engineering and programming languages. We put a strong emphasis on the teacher and the teacher’s interaction with data, as well as crucial components such as techniques and design principles of interaction and visualization.
- Accuracy Dominates Bias and Self-Fulfilling Prophecy — three conclusions: (1) Although errors, biases, and self-fulfilling prophecies in person perception are real, reliable, and occasionally quite powerful, on average, they tend to be weak, fragile, and fleeting. (2) Perceptions of individuals and groups tend to be at least moderately, and often highly, accurate. (3) Conclusions based on the research on error, bias, and self-fulfilling prophecies routinely greatly overstate their power and pervasiveness, and consistently ignore evidence of accuracy, agreement, and rationality in social perception.
- Fairness in Machine Learning: Lessons from Political Philosophy — Questions of discrimination, egalitarianism, and justice are of significant interest to moral and political philosophers, who have expended significant efforts in formalizing and defending these central concepts. It is therefore unsurprising that attempts to formalize “fairness” in machine learning contain echoes of these old philosophical debates. This paper draws on existing work in moral and political philosophy in order to elucidate emerging debates about fair machine learning.
- Quantum Game — open source game play with photons, superposition, and entanglement. In your browser! With true quantum mechanics underneath!
