’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 […]
Avainsana: algorithms
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Algorithmic Scapegoating
Algorithm scapegoating = blaming an ”algorithm” for a social problem instead of attributing the blame appropriately to humans. This is often done with some vague and incorrect notion of ”algorithm”, not specifying what algorithm and how exactly the algorithm was at fault at carrying out its task. This misattribution is often associated with not properly […]
Just spent 1.5hrs talking to a journalist about algorithms. Sharing my notes, containing many ”unpopular opinions” that I nonetheless believe should be part of the public discussion about these topics. TL;DR: It’s easy to blame algorithms, hard to take individual responsibility. Here’s what’s wrong with the debate on algorithms: (1) algorithms are used as scapegoats […]
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 […]
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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Questions from ICWSM17
In the ”Studying User Perceptions and Experiences with Algorithms” workshop, there were many interesting questions popping up. Here are some of them: Will increased awareness of algorithm functionality change user behavior? How How can we build better algorithms to diversify information users are exposed to? Do most people care about knowing how Google works? What’s […]
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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, […]
The relationship between users and algorithms is always a mediated one, meaning that there is always a proxy between the algorithm and the user. The proxy can be understood differently based on the particular level we’re interested in. For example, it can be a social media platform (e.g., Facebook, Twitter) where people retrieve their news content […]
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The balanced view algorithm
I recently participated in a meeting of computer scientists where the topic was ”fake news”. The implicit assumption was that ”we will do this tool x that will show people what is false information, and they will become informed.” However, after the meeting I realized this might not be enough, and in fact be naïve […]
1. Introduction Earlier, I had a brief exchange of tweets with @jonathanstray about algorithms. It started from his tweet: Perhaps the biggest technical problem in making fair algorithms is this: if they are designed to learn what humans do, they will. To which I replied: Yes, and that’s why learning is not the way to […]
Algoritmien valta - Neutraalius ja puolueellisuus koneellisessa päätöksenteossa 