Everything, Everywhere All in One Evaluation: Using Multiverse Analysis to Evaluate the Influence of Model Design Decisions on Algorithmic Fairness

A vast number of systems across the world use algorithmic decision making (ADM) to (partially) automate decisions that have previously been made by humans. When designed well, these systems promise more objective decisions while saving large amounts of resources and freeing up human time. However, when ADM systems are not designed well, they can lead to unfair decisions which discriminate against societal groups. The downstream effects of ADMs critically depend on the decisions made during the systems' design and implementation, as biases in data can be mitigated or reinforced along the modeling pipeline. Many of these design decisions are made implicitly, without knowing exactly how they will influence the final system. It is therefore important to make explicit the decisions made during the design of ADM systems and understand how these decisions affect the fairness of the resulting system. To study this issue, we draw on insights from the field of psychology and introduce the method of multiverse analysis for algorithmic fairness. In our proposed method, we turn implicit design decisions into explicit ones and demonstrate their fairness implications. By combining decisions, we create a grid of all possible "universes" of decision combinations. For each of these universes, we compute metrics of fairness and performance. Using the resulting dataset, one can see how and which decisions impact fairness. We demonstrate how multiverse analyses can be used to better understand variability and robustness of algorithmic fairness using an exemplary case study of predicting public health coverage of vulnerable populations for potential interventions. Our results illustrate how decisions during the design of a machine learning system can have surprising effects on its fairness and how to detect these effects using multiverse analysis

Gorenstein homological algebra and universal coefficient theorems

We study criteria for a ring—or more generally, for a small category—to be Gorenstein and for a module over it to be of finite projective dimension. The goal is to unify the universal coefficient theorems found in the literature and to develop machinery for proving new ones. Among the universal coefficient theorems covered by our methods we find, besides all the classic examples, several exotic examples arising from the KK-theory of C*-algebras and also Neeman’s Brown–Adams representability theorem for compactly generated categories

occupationMeasurement/occupationMeasurement: occupationMeasurement 0.3.1

Gracefully handle unavailability of the KldB 2010 classification Disable multithreading in examples and tests to comply with new CRAN polic

Infants relax in response to unfamiliar foreign lullabies

Music is characterized by acoustical forms that are predictive of its behavioral functions. For example, adult listeners accurately identify unfamiliar lullabies as infant-directed on the basis of their musical features alone. This property could reflect a function of listeners’ experiences, the basic design of the human mind, or both. Here, we show that American infants (N = 144) relax in response to 8 unfamiliar foreign lullabies, relative to matched non-lullaby songs from other foreign societies, as indexed by heart rate, pupillometry, and electrodermal activity. They do so consistently throughout the first year of life, suggesting the response is not a function of their musical experiences, which are limited relative to those of adults. The infants’ parents overwhelmingly chose lullabies as the songs that they themselves would use to calm their fussy infant, despite their unfamiliarity. Together, these findings suggest that infants are predisposed to respond to universal features of lullabies

TRIPLE – Ice Data Hub, Model-based Mission Support and Forefield Reconnaissance System

The ocean worlds of our Solar System, like Saturn's moon Enceladus and Jupiter's moon Europa are covered with ice. Recently, these icy moons gained further scientific interest, as they are attributed some potential to sustain or host extraterrestrial life in a subglacial ocean. The investigation of these moons will also help to understand the evolution of the Solar System. The in-situ exploration of these moons requires novel technological solutions as well as intelligent data acquisition and interpretation tools. In 2020, the DLR Space Administration started the TRIPLE project (Technologies for Rapid Ice Penetration and subglacial Lake Exploration) which develops an integrated concept for a melting probe that launches an autonomous underwater vehicle (nanoAUV) into a scientifically interesting water reservoir and an AstroBioLab for in-situ analysis. These three components build up the TRIPLE system. As part of a second project stage, it is envisioned to build the TRIPLE system and test it in Antarctica in 2026. In this contribution, we are going to present the general concept of TRIPLE with a focus on the geophysically most relevant aspects. To navigate the melting probe through the ice, a forefield reconnaissance system (TRIPLE-FRS) based on combined radar and sonar techniques is designed. This will include radar antennas directly integrated into the melting head combined with a pulse amplifier and a piezoelectric acoustic transducer just behind the melting head. In addition, an in-situ permittivity sensor will be implemented to account for the ice structure dependent propagation speed of electromagnetic waves. With this system, obstacles as well as the ice-water interface at the bottom of the icy shell could be detected. To deliver key parameters such as transit time and overall energy requirement, a virtual test bed for strategic mission planning is currently under development. This consists of the Ice Data Hub that combines available data from Earth or any other planetary body – measured or taken from the literature – and allows display, interpretation and export of data, as well as trajectory models for the melting probe. We develop high-fidelity thermal contact models for the phase change as well as macroscopic trajectory models that consider the thermodynamic melting process and the convective loss of heat via the melt-water flow.Bundesministerium für Wirtschaft und Energie (FKZ: 50NA1908, 50RK2050, 50RK2051, 50RK2052, 50RK2053)poste