AStA Wirtschafts- und Sozialstatistisches Archiv最新文献

AStA Wirtschafts- und Sozialstatistisches Archiv Pub Date : 2024-10-18 DOI: 10.1007/s11943-024-00347-z

Jan Pablo Burgard, Markus Zwick, Florian Dumpert, Sebastian Wichert, Thomas Augustin, Nina Storfinger

引用次数: 0

Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production 将算法公平性与官方统计和调查制作中机器学习的质量维度联系起来

AStA Wirtschafts- und Sozialstatistisches Archiv Pub Date : 2024-10-07 DOI: 10.1007/s11943-024-00344-2

Patrick Oliver Schenk, Christoph Kern

{"title":"Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production","authors":"Patrick Oliver Schenk, Christoph Kern","doi":"10.1007/s11943-024-00344-2","DOIUrl":"10.1007/s11943-024-00344-2","url":null,"abstract":"<div><p>National Statistical Organizations (NSOs) increasingly draw on Machine Learning (ML) to improve the timeliness and cost-effectiveness of their products. When introducing ML solutions, NSOs must ensure that high standards with respect to robustness, reproducibility, and accuracy are upheld as codified, e.g., in the Quality Framework for Statistical Algorithms (QF4SA; Yung et al. 2022, <i>Statistical Journal of the IAOS</i>). At the same time, a growing body of research focuses on fairness as a pre-condition of a safe deployment of ML to prevent disparate social impacts in practice. However, fairness has not yet been explicitly discussed as a quality aspect in the context of the application of ML at NSOs. We employ the QF4SA quality framework and present a mapping of its quality dimensions to algorithmic fairness. We thereby extend the QF4SA framework in several ways: First, we investigate the interaction of fairness with each of these quality dimensions. Second, we argue for fairness as its own, additional quality dimension, beyond what is contained in the QF4SA so far. Third, we emphasize and explicitly address data, both on its own and its interaction with applied methodology. In parallel with empirical illustrations, we show how our mapping can contribute to methodology in the domains of official statistics, algorithmic fairness, and trustworthy machine learning.</p><p>Little to no prior knowledge of ML, fairness, and quality dimensions in official statistics is required as we provide introductions to these subjects. These introductions are also targeted to the discussion of quality dimensions and fairness.</p></div>","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 2","pages":"131 - 184"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11943-024-00344-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Bayesian variable selection methods for binary regression models with missing covariate data 针对具有缺失协变量数据的二元回归模型的贝叶斯变量自动选择方法

AStA Wirtschafts- und Sozialstatistisches Archiv Pub Date : 2024-09-13 DOI: 10.1007/s11943-024-00345-1

Michael Bergrab, Christian Aßmann

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引用次数: 0

Fairness als Qualitätskriterium im Maschinellen Lernen – Rekonstruktion des philosophischen Konzepts und Implikationen für die Nutzung außergesetzlicher Merkmale bei qualifizierten Mietspiegeln 作为机器学习质量标准的公平性--哲学概念的重构以及在合格租金指数中使用法外特征的影响

AStA Wirtschafts- und Sozialstatistisches Archiv Pub Date : 2024-08-23 DOI: 10.1007/s11943-024-00346-0

Ludwig Bothmann, Kristina Peters

{"title":"Fairness als Qualitätskriterium im Maschinellen Lernen – Rekonstruktion des philosophischen Konzepts und Implikationen für die Nutzung außergesetzlicher Merkmale bei qualifizierten Mietspiegeln","authors":"Ludwig Bothmann, Kristina Peters","doi":"10.1007/s11943-024-00346-0","DOIUrl":"10.1007/s11943-024-00346-0","url":null,"abstract":"<p>Mit der verstärkten Nutzung von Modellen des Maschinellen Lernens (ML) innerhalb von Systemen der automatisierten Entscheidungsfindung wachsen die Anforderungen an die Qualität von ML-Modellen. Die reine Prognosegüte ist nicht länger das alleinige Qualitätskriterium; insbesondere wird vermehrt gefordert, dass Fairnessaspekte berücksichtigt werden. Dieser Beitrag verfolgt zwei Ziele. Zum einen werden die aktuelle Fairnessdiskussion im Bereich ML (fairML) zusammengefasst und die aktuellsten Entwicklungen, insbesondere in Bezug auf die philosophischen Grundlagen des Fairnessbegriffs innerhalb ML, beschrieben. Zum anderen wird die Frage behandelt, inwiefern sogenannte „außergesetzliche“ Merkmale bei der Erstellung qualifizierter Mietspiegel genutzt werden dürfen. Ein aktueller Vorschlag von Kauermann und Windmann (AStA Wirtschafts- und Sozialstatistisches Archiv, Band 17, 2023) zur Nutzung außergesetzlicher Merkmale in qualifizierten Mietspiegeln beinhaltet eine modellbasierte Imputationsmethode, welche wir den gesetzlichen Anforderungen gegenüberstellen. Schließlich zeigen wir auf, welche Alternativen aus dem Bereich fairML genutzt werden könnten und legen dar, welche unterschiedlichen philosophischen Grundannahmen hinter den verschiedenen Verfahren stehen.</p>","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 2","pages":"185 - 201"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11943-024-00346-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interview mit Walter Krämer 采访 Walter Krämer

AStA Wirtschafts- und Sozialstatistisches Archiv Pub Date : 2024-07-18 DOI: 10.1007/s11943-024-00343-3

Ulrich Rendtel

引用次数: 0

„Mister SOEP et al.“ – ein Nachruf auf Gert G. Wagner "SOEP 先生等人"--格特-G-瓦格纳的讣告

AStA Wirtschafts- und Sozialstatistisches Archiv Pub Date : 2024-07-09 DOI: 10.1007/s11943-024-00342-4

C. Katharina Spieß

引用次数: 0

Data Observer—a guide to data that can help to inform evidence-based policymaking 数据观察员--有助于循证决策的数据指南

AStA Wirtschafts- und Sozialstatistisches Archiv Pub Date : 2024-06-24 DOI: 10.1007/s11943-024-00341-5

Joachim Wagner

引用次数: 0

Flat rent price prediction in Berlin with web scraping 利用网络搜索预测柏林公寓租金价格

AStA Wirtschafts- und Sozialstatistisches Archiv Pub Date : 2024-06-24 DOI: 10.1007/s11943-024-00340-6

Camilo Meyberg, Ulrich Rendtel, Holger Leerhoff

{"title":"Flat rent price prediction in Berlin with web scraping","authors":"Camilo Meyberg, Ulrich Rendtel, Holger Leerhoff","doi":"10.1007/s11943-024-00340-6","DOIUrl":"10.1007/s11943-024-00340-6","url":null,"abstract":"<div><p>Internet data pose a challenge to the traditional system of official statistics, which relies on more conventional sources such as surveys and registers, not readily adaptable to rapid changes. Expanding this system to include internet data is currently at an experimental stage, exploring these sources’ potentials and benefits. This paper describes a project conducted within the ESSnet <i>Trusted Smart Statistics – Web Intelligence Network</i> framework. It investigates the use of online apartment listings to analyze the rental market. We used web scraping to extract information from two online real estate portals for flats in the city of Berlin. Using this data, we developed a model to predict rental prices per square meter based on the accommodation’s features and location within the city. We detected offers which appear in both portals by means of statistical matching and removed duplicate offers. Missing values were treated by multiple imputation. The prediction model is a semi-parametric approach where the postal districts are used to describe the location effect. Comparisons with microcensus results and the local rent index reveal significant differences between the market of online flat offers and the stock of existing flat contracts. Interested readers will find the commented programming code in the internet supplement.</p></div>","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 2","pages":"245 - 278"},"PeriodicalIF":0.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11943-024-00340-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vorwort der Herausgeber 编辑前言

AStA Wirtschafts- und Sozialstatistisches Archiv Pub Date : 2024-04-17 DOI: 10.1007/s11943-024-00339-z

Markus Zwick, Jan Pablo Burgard

引用次数: 0

Interview mit Ralf Münnich 采访拉尔夫-明尼希

AStA Wirtschafts- und Sozialstatistisches Archiv Pub Date : 2024-03-11 DOI: 10.1007/s11943-024-00337-1

Walter Krämer

引用次数: 0