Introduction The ambiguity problem illustrated: User: ”Siri, call me an ambulance!” Siri: ”Okay, I will call you ’an ambulance’.” You’ll never reach the hospital, and end up bleeding to death. Solutions Two potential solutions come to mind: A. machine builds general knowledge (”common sense”) B. machine identifies ambiguity & asks for clarification from humans (reinforcement […]
Avainsana: machine learning
Introduction. Hal Daumé wrote an interesting blog post about language bias and the black sheep problem. In the post, he defines the problem as follows: The ”black sheep problem” is that if you were to try to guess what color most sheep were by looking and language data, it would be very difficult for you to conclude that […]
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What jobs are safe from AI?
There is enormous concern about machine learning and AI replacing human workers. However, according to several economists, and also according to past experience ranging back all the way to the industrial revolution of the 18th century (which caused major distress at the time), the replacement of human workers is not permanent but there will be […]
Ethics of machine learning algorithms has recently been raised as a major research concern. Earlier this year (2017), a fund of $27M USD was started to support research on the societal challenges of AI. The group responsible for the fund includes e.g. the Knight Foundation, Omidyar Network and the startup founder and investor Reid Hoffman. As […]
Feature analysis could be employed for bias detection when evaluating the procedural fairness of algorithms. (This is an alternative to the ”Google approach” which emphasis evaluation of outcome fairness.) In brief, feature analysis reveals how well each feature (=variable) influenced the model’s decision. For example, see the following quote from Huang et al. (2014, p. 240): […]
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Reading list from ICWSM17
In one of the workshops of the first conference day, ”Studying User Perceptions and Experiences with Algorithms”, the participants recommended papers to each other. Here are, if not all, then most of them, along with their abstracts. Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, […]
Algoritmien valta - Neutraalius ja puolueellisuus koneellisessa päätöksenteossa 