The Inverse Transparency Toolchain: A Fully Integrated and Quickly
Deployable Data Usage Logging Infrastructure
- URL: http://arxiv.org/abs/2308.04366v1
- Date: Tue, 8 Aug 2023 16:04:48 GMT
- Title: The Inverse Transparency Toolchain: A Fully Integrated and Quickly
Deployable Data Usage Logging Infrastructure
- Authors: Valentin Zieglmeier
- Abstract summary: Inverse transparency is created by making all usages of employee data visible to them.
For research and teaching contexts that integrate inverse transparency, creating this required infrastructure can be challenging.
The Inverse Transparency Toolchain presents a flexible solution for such scenarios.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Inverse transparency is created by making all usages of employee data visible
to them. This requires tools that handle the logging and storage of usage
information, and making logged data visible to data owners. For research and
teaching contexts that integrate inverse transparency, creating this required
infrastructure can be challenging. The Inverse Transparency Toolchain presents
a flexible solution for such scenarios. It can be easily deployed and is
tightly integrated. With it, we successfully handled use cases covering
empirical studies with users, prototyping in university courses, and
experimentation with our industry partner.
Related papers
- Open-Vocabulary Camouflaged Object Segmentation [66.94945066779988]
We introduce a new task, open-vocabulary camouflaged object segmentation (OVCOS)
We construct a large-scale complex scene dataset (textbfOVCamo) containing 11,483 hand-selected images with fine annotations and corresponding object classes.
By integrating the guidance of class semantic knowledge and the supplement of visual structure cues from the edge and depth information, the proposed method can efficiently capture camouflaged objects.
arXiv Detail & Related papers (2023-11-19T06:00:39Z) - Transparent Object Tracking with Enhanced Fusion Module [56.403878717170784]
We propose a new tracker architecture that uses our fusion techniques to achieve superior results for transparent object tracking.
Our results and the implementation of code will be made publicly available at https://github.com/kalyan05TOTEM.
arXiv Detail & Related papers (2023-09-13T03:52:09Z) - Hawk: DevOps-driven Transparency and Accountability in Cloud Native
Systems [0.0]
Transparency is one of the most important principles of modern privacy regulations.
Data controllers must provide data subjects with precise information about the collection, processing, storage, and transfer of personal data.
arXiv Detail & Related papers (2023-06-04T22:09:42Z) - Rethinking People Analytics With Inverse Transparency by Design [57.67333075002697]
We propose a new design approach for workforce analytics we refer to as inverse transparency by design.
We find that architectural changes are made without inhibiting core functionality.
We conclude that inverse transparency by design is a promising approach to realize accepted and responsible people analytics.
arXiv Detail & Related papers (2023-05-16T21:37:35Z) - What and How of Machine Learning Transparency: Building Bespoke
Explainability Tools with Interoperable Algorithmic Components [77.87794937143511]
This paper introduces a collection of hands-on training materials for explaining data-driven predictive models.
These resources cover the three core building blocks of this technique: interpretable representation composition, data sampling and explanation generation.
arXiv Detail & Related papers (2022-09-08T13:33:25Z) - SemTUI: a Framework for the Interactive Semantic Enrichment of Tabular
Data [0.0]
SemTUI is a framework to make the enrichment process flexible, complete, and effective through the use of semantics.
A task-driven user evaluation proved SemTUI to be understandable, usable, and capable of achieving table enrichment with little effort and time.
arXiv Detail & Related papers (2022-03-17T17:14:21Z) - TIRA: An OpenAPI Extension and Toolbox for GDPR Transparency in RESTful
Architectures [0.0]
Transparency provides information about what personal data is collected for which purposes, how long it is stored, or to which parties it is transferred.
Technical approaches for implementing transparency in practice are, however, only rarely considered.
We introduce 1) a transparency-focused extension of OpenAPI specifications that allows individual service descriptions to be enriched with transparency-related annotations in a bottom-up fashion and 2) a set of higher-order tools for aggregating respective information across multiple, interdependent services and for coherently integrating our approach into automated CI/CD-pipelines.
arXiv Detail & Related papers (2021-06-10T18:42:50Z) - Trustworthy Transparency by Design [57.67333075002697]
We propose a transparency framework for software design, incorporating research on user trust and experience.
Our framework enables developing software that incorporates transparency in its design.
arXiv Detail & Related papers (2021-03-19T12:34:01Z) - Mining Implicit Entity Preference from User-Item Interaction Data for
Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task.
Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator.
To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
arXiv Detail & Related papers (2020-03-28T05:47:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.