Developing a Benchmark for Reducing Data Bias in Authorship Attribution

Authorship attribution is the task of assigning an unknown document to an author from a set of candidates. In the past, studies in this field use various evaluation datasets to demonstrate the effectiveness of preprocessing steps, features, and models. However, only a small fraction of works use more than one dataset to prove claims. In this paper, we present a collection of highly diverse authorship attribution datasets, which better generalizes evaluation results from authorship attribution research. Furthermore, we implement a wide variety of previously used machine learning models and show that many approaches show vastly different performances when applied to different datasets. We include pre-trained language models, for the first time testing them in this field in a systematic way. Finally, we propose a set of aggregated scores to evaluate different aspects of the dataset collection.

Fine-grained Classification of Political Bias in German News: A Data Set and Initial Experiments

We present a data set consisting of German news articles labeled for political bias on a five-point scale in a semi-supervised way. While earlier work on hyperpartisan news detection uses binary classification (i.e., hyperpartisan or not) and English data, we argue for a more fine-grained classification, covering the full political spectrum (i.e., far-left, left, centre, right, far-right) and for extending research to German data. Understanding political bias helps in accurately detecting hate speech and online abuse. We experiment with different classification methods for political bias detection. Their comparatively low performance (a macro-F1 of 43 for our best setup, compared to a macro-F1 of 79 for the binary classification task) underlines the need for more (balanced) data annotated in a fine-grained way.

How do people interact with biased text prediction models while writing?

Recent studies have shown that a bias in thetext suggestions system can percolate in theuser's writing. In this pilot study, we ask thequestion: How do people interact with text pre-diction models, in an inline next phrase sugges-tion interface and how does introducing senti-ment bias in the text prediction model affecttheir writing? We present a pilot study as afirst step to answer this question.

برامج لكشف تحيز الامتحانات المؤتمتة

يتم في هذا البحث تطبيق مجموعة من البرامج التي قام المؤلف بإنجازها و هي مخصصة لمعالجة مجموعة من المسائل التي تظهر في حالات مختلفة لتطبيق طريقة الامتحانات المؤتمتة. يتم حساب قيم المتحولات العشوائية و توابع التوزيعات الاحتمالية من أجل كل الحالات المتعلقة بشكل معين لتطبيق هذه الطريقة.