Statistical evidence and algorithmic decision-making

Intelligent Evidence-Based Management for Data Collection and Decision-Making Using Algorithmic Randomness and Active Learning

We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data di...

The Use of Evidence by Decision-Making Committees

Statistical Decision Making and Neural Networks

An algorithmic approach to service and content deployment decision making

companies, interconnect a large number of devices as well as persons. Within such networks, content can be exchanged and services can be utilised. The more complex such networks become, the more opportunities might exist to choose a desired service or content. If multiple of those choices need to be made periodically or in short time, manual selection may not be a suitable procedure. From this ...

On Fairness, Diversity and Randomness in Algorithmic Decision Making

Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning and demonstrate various attractive properties: (i...