Gestaltungswille und Algorithm Aversion – Die Auswirkungen der Einflussnahme im Prozess der algorithmischen Entscheidungsfindung auf die Algorithm Aversion

2021 ◽  
Author(s):  
Zulian Gubaydullina ◽  
Jan René Judek ◽  
Marco Lorenz ◽  
Markus Spiwoks
Keyword(s):  
Als Algorithm

Obwohl Algorithmen in vielen Anwendungsgebieten präzisere Prognosen abgeben als Menschen, weigern sich Entscheidungsträger häufig, auf Algorithmen zurückzugreifen. In einem ökonomischen Experiment untersuchen wir, ob das Ausmaß dieses als „Algorithm Aversion“ bekannten Phänomens reduziert werden kann, indem Entscheidungsträgern eine Einflussmöglichkeit auf die Ausgestaltung des Algorithmus eingeräumt wird (Einflussmöglichkeit auf den algorithmischen Input). Zusätzlich replizieren wir die Studie von Dietvorst, Simmons & Massey (2018). Darin zeigt sich, dass die Algorithm Aversion deutlich zurückgeht, sofern die Subjekte am Ende die Ergebnisse des Algorithmus – und sei es nur um wenige Prozent – verändern können (Einflussmöglichkeit auf den algorithmischen Output). In der vorliegenden Studie bestätigt sich, dass die Algorithm Aversion bei einer Einflussmöglichkeit auf den algorithmischen Output signifikant zurückgeht. Eine Einflussmöglichkeit auf den algorithmischen Input scheint allerdings nur bedingt geeignet, die Algorithm Aversion zu reduzieren. Die begrenzte Möglichkeit zur Modifikation des algorithmischen Outputs reduziert die Algorithm Aversion effektiver als die Möglichkeit, Einfluss auf den algorithmischen Input zu nehmen.

The Universal ALS Algorithm

Resuscitation ◽  
1997 ◽  
Vol 34 (2) ◽  
pp. 109-111 ◽  
Author(s):  
W. Kloeck ◽  
R. Cummins ◽  
D. Chamberlain ◽  
L. Bossaert ◽  
V. Callanan ◽  
...  
Keyword(s):  
Als Algorithm

scholarly journals CHALLENGES IN IDENTIFYING PERSONS WITH NEURODEGENERATIVE DISEASES USING HEALTH ADMINISTRATIVE DATABASES

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S16-S16
Author(s):  
Laura C Maclagan ◽  
Serena Soleimani ◽  
Agessandro Abrahao ◽  
Lorne Zinman ◽  
Michael A Campitelli ◽  
...  
Keyword(s):  
Health Service ◽  
Neurodegenerative Diseases ◽  
Medical Records ◽  
Health Service Utilization ◽  
Population Level ◽  
Administrative Databases ◽  
Diagnosis Codes ◽  
Service Patterns ◽  
Als Algorithm ◽  
Lateral Sclerosis

Abstract Health administrative databases can be used to quantify prevalence and incidence of neurodegenerative diseases and their impact on health service utilization outcomes at the population level. Algorithms based on diagnosis codes and health service patterns can be used to identify persons suspected to have a neurodegenerative disease. Previous studies have developed and validated algorithms to identify persons with Alzheimer’s and related dementias using primary care medical records as the reference standard, however, little previous work has focused on developing algorithms for rare neurodegenerative diseases including amyotrophic lateral sclerosis (ALS). This session will discuss challenges in developing algorithms to identify persons with neurodegenerative diseases accurately and opportunities to improve existing definitions using novel data sources including electronic medical record databases. Preliminary findings regarding the development of an ALS algorithm will be presented.

scholarly journals A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1215
Author(s):  
Mazhar Javed Awan ◽  
Rafia Asad Khan ◽  
Haitham Nobanee ◽  
Awais Yasin ◽  
Syed Muhammad Anwar ◽  
...  
Keyword(s):  
Big Data ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Robust Clustering ◽  
Search Data ◽  
Filtering Technique ◽  
Streaming Services ◽  
Movie Recommendation ◽  
Movie Ratings ◽  
Als Algorithm

In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies. Our proposed system uses the last search data of a user regarding movie category and references this to instruct the recommender engine, thereby making a list of predictions for top ratings. The proposed study used a model-based approach of matrix factorization, the ALS algorithm along with a collaborative filtering technique, which solved the cold start, sparse, and scalability problems. In particular, we performed experimental analysis and successfully obtained minimum root mean squared errors (oRMSEs) of 0.8959 to 0.97613, approximately. Moreover, our proposed movie recommendation system showed an accuracy of 97% and predicted the top 1000 ratings for movies.

scholarly journals Channel Estimation for Intelligent Reflecting Surface Assisted mmWave Communications

2021 ◽  
Author(s):  
Dong Chen ◽  
Youhua Fu ◽  
Chen Liu ◽  
Hong Wang
Keyword(s):  
Channel Estimation ◽  
Least Squares ◽  
Millimeter Wave ◽  
Communication Systems ◽  
Regularization Parameter ◽  
Estimation Error ◽  
Error Performance ◽  
Selection Scheme ◽  
Als Algorithm ◽  
Reflecting Surface

Abstract Intelligent reflecting surface (IRS) consists of a large number of low-cost passive reflective elements, which can assist millimeter wave communications to solve the problems of weak penetration and short propagation distance. However, it is challenging to obtain channel state information (CSI) in IRS-aided millimeter wave communication systems. To solve this challenge, this paper proposes a regular alternating least squares (RALS) algorithm based on the canonical/parallel factor (CP) decomposition. Compared with the traditional alternate least squares (ALS) algorithm, the proposed RALS algorithm has better convergence performance, thus solving the problem of divergence or slow convergence of the conventional ALS algorithm. Besides, in order to improve the accuracy of the channel estimation, the convex optimization theory is invoked to devise the regularization parameters, and a regularization parameter selection scheme is developed to ensure that the proposed algorithm obtains the optimal solution. The simulation results verify the theoretical analysis and prove the superiority of the proposed RALS algorithm in terms of estimation error performance.

scholarly journals OCTAVVS: A Graphical Toolbox for High-Throughput Preprocessing and Analysis of Vibrational Spectroscopy Imaging Data

2020 ◽  
Vol 3 (2) ◽  
pp. 34
Author(s):  
Carl Troein ◽  
Syahril Siregar ◽  
Michiel Op De Beeck ◽  
Carsten Peterson ◽  
Anders Tunlid ◽  
...  
Keyword(s):  
Vibrational Spectroscopy ◽  
Hyperspectral Image ◽  
Atmospheric Correction ◽  
Region Of Interest ◽  
Mie Scattering ◽  
Multivariate Curve Resolution ◽  
Imaging Data ◽  
Large Sets ◽  
Heterogeneous Samples ◽  
Als Algorithm

Modern vibrational spectroscopy techniques enable the rapid collection of thousands of spectra in a single hyperspectral image, allowing researchers to study spatially heterogeneous samples at micrometer resolution. A number of algorithms have been developed to correct for effects such as atmospheric absorption, light scattering by cellular structures and varying baseline levels. After preprocessing, spectra are commonly decomposed and clustered to reveal informative patterns and subtle spectral changes. Several of these steps are slow, labor-intensive and require programming skills to make use of published algorithms and code. We here present a free and platform-independent graphical toolbox that allows rapid preprocessing of large sets of spectroscopic images, including atmospheric correction and a new algorithm for resonant Mie scattering with improved speed. The software also includes modules for decomposition into constituent spectra using the popular Multivariate Curve Resolution–Alternating Least Squares (MCR-ALS) algorithm, augmented by region-of-interest selection, as well as clustering and cluster annotation.

Simultaneous determination of aromatic amino acids in different systems using three-way calibration based on the PARAFAC-ALS algorithm coupled with EEM fluorescence: exploration of second-order advantages

2014 ◽  
Vol 6 (16) ◽  
pp. 6358-6368 ◽  
Author(s):  
Chao Kang ◽  
Hai-Long Wu ◽  
Shou-Xia Xiang ◽  
Li-Xia Xie ◽  
Ya-Juan Liu ◽  
...  
Keyword(s):  
Amino Acids ◽  
Simultaneous Determination ◽  
Efficient Method ◽  
Aromatic Amino Acids ◽  
Second Order ◽  
Als Algorithm ◽  
Eem Fluorescence

An efficient method is proposed for determination of aromatic amino acids in different systems simultaneously, even in the presence of three uncalibrated interferents.

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