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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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How to teach machines common sense? Solutions for the ambiguity problem of AI

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 […]

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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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Suomeksi

10 suositusta tekoälyn hyödyntämiseen

Tekoälyyn perustuvat digitaaliset apulaiset, chatbotit ja monenlaiset algoritmit sekä koneellinen päätöksenteko koskettavat ihmisten arkea entistä enemmän – oltiin siitä tietoisia tai ei. Kehitysvauhti on kovaa, mutta standardointi ja vaikuttavuuden arviointi laahaavat perässä. Sen vuoksi on syytä pohtia ja herättää keskustelua siitä, mihin suuntaan tekoälyn kehitystä tulevaisuudessa ohjataan. Yksi tärkeistä kysymyksistä on: Millaisia suosituksia tekoälyn parissa […]

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A Brief Report on AI and Machine Learning Fairness Initiatives

This report was created by Joni Salminen and Catherine R. Sloan. Publication date: December 10, 2017. Artificial intelligence (AI) and machine learning are becoming more influential in society, as more decision-making power is being shifted to algorithms either directly or indirectly. Because of this, several research organizations and initiatives studying fairness of AI and machine […]

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Suomeksi

9 eettistä ongelmaa keinoälyssä

Keinoälyssä on kyse muustakin kuin teknologisista edistysaskeleista. Nykyisin tunnustetaan laajalti, että keinoälyjärjestelmiin liittyy keskeisesti inhimillisten arvojen ylläpitäminen ja riskienhallinta. Teknologiayritykset, kuten Googlen emoyhtiö Alphabet, Amazon, Facebook, IBM, Nokia ja Microsoft, sekä useat tieteen ja teknologian maailmassa tunnetut mielipidevaikuttajat, kuten Stephen Hawking, Elon Musk ja Bill Gates uskovat, että nyt on paras aika keskustella paitsi keinoälyn mahdollisuuksista, […]

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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 […]

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Ethics and Governance of Artificial Intelligence Initiative

Read about this amazing initiative at Harvard’s website and thought of sharing it: About the Ethics and Governance of Artificial Intelligence Initiative Artificial intelligence and complex algorithms, fueled by the collection of big data and deep learning systems, are quickly changing how we live and work, from the news stories we see, to the loans […]

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Feature analysis for detecting algorithmic bias

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): […]