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Algorithmic scapegoating revisited (a.k.a., shifting blame to an algorithm)

’Accountability’. There’s a lot of discussion about it, but it seems elusive in many real world situations. In fact, there is an alarming trend of people using automation in systems as an excuse to justify unfair treatment of others. Example: ”Oh, we can’t do anything, it’s in the system”. Or, ”I don’t have the authority […]

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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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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): […]

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How to measure media bias?

Introduction Media bias is under heavy discussion at the moment, especially relating to the past presidential election in the US. However, the quality of discussion is not the way it should be; I mean, there should be objective analysis on the role of the media. Instead, most comments are politically motivated accusations or denials. This […]