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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 […]

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21 things that are wrong about ”algorithmic bias”

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 […]

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It’s not only about individuals – Three levels to consider with automation

Short argument: when talking about automation, it’s important to define what level we mean. There are at least three levels: automation within a professional task = this level deals with understanding how automation impacts people’s work, e.g., replacing them or boosting their productivity (both the good and the bad) automation within an organization = this […]

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Random notes from automation workshop

Notes from a CHI2018 workshop: creativity is not an excuse for ignorance software doing part of your work might be more work errors from the developers are cascading in the system never trust the marketing aspect of automation frictions in the promise of automation automation surprises automation was expected to have better consistent behavior automation […]

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On Social Media Sampling

In social media sampling, there are many issues. Two of them are: 1) the silent majority problem and 2) the grouping problem. The former refers to the imbalance between participants and spectators: can we trust that the vocal few represent the views of all? The latter means that people of similar opinions tend to flock […]

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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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Machine decision making and workflow engineering

Did you ever want to climb Mount Everest? If you did, you would have to split such a goal into many tasks: You would first need to find out what resources are needed for it, who could help you, how to prepare mentally and physically, etc. You would come up with a list of tasks […]

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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 […]