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Web 3.0: The dark side of social media

Web 2.0 was about all the pretty, shiny things about social media, like user-generated content, blogs, customer participation, ”everyone has a voice,” etc. Now, Web 3.0 is all about the dark side: algorithmic bias, filter bubbles, group polarization, flame wars, cyberbullying, etc. We discovered that maybe everyone should not have a voice, after all. Or at […]

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From polarity to diversity of opinions

The problem with online discussions and communities is that the extreme poles draw people effectively, causing group polarization in which the original opinion of a person becomes more radical due to influence of the group. In Finnish, we have a saying ”In a group, stupidity concentrates” (joukossa tyhmyys tiivistyy). Here, I’m exploring the idea that […]

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The black sheep problem in 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 […]

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Research agenda for ethics and governance of artificial intelligence

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 […]