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Mario Zechner

@mariozechner.at

Dann zitieren sie das als Methode:

papers.ssrn.com/sol3/papers....

Sprich, sie reproduzieren einfach eine Studie, die auch schon garbage war.

Vergleiche dazu diesen Artikel in Cell mit dem klingenden Title: "The reanimation of pseudoscience in machine learning and its ethical repercussions"

Assessing the Big Five personality traits using real-life static facial images
A Kachur, E Osin, D Davydov, K Shutilov, A Novokshonov
Scientific Reports, 2020•nature.com
Abstract
There is ample evidence that morphological and social cues in a human face provide signals of human personality and behaviour. Previous studies have discovered associations between the features of artificial composite facial images and attributions of personality traits by human experts. We present new findings demonstrating the statistically significant prediction of a wider set of personality features (all the Big Five personality traits) for both men and women using real-life static facial images. Volunteer participants (N = 12,447) provided their face photographs (31,367 images) and completed a self-report measure of the Big Five traits. We trained a cascade of artificial neural networks (ANNs) on a large labelled dataset to predict self-reported Big Five scores. The highest correlations between observed and predicted personality scores were found for conscientiousness (0.360 for men and 0.335 for women) and the mean effect size was 0.243, exceeding the results obtained in prior studies using ‘selfies’. The findings strongly support the possibility of predicting multidimensional personality profiles from static facial images using ANNs trained on large labelled datasets. Future research could investigate the relative contribution of morphological features of the face and other characteristics of facial images to predicting personality.

Our AI-based methodology for extracting the Photo Big 5 personality scores uses an
updated algorithm originally developed by Kachur et al. (2020, KODSN), who used selfsubmitted images annotated with Big 5 survey responses from a large sample of individuals
to extract facial features and train a cascade of artificial neural networks that learns to
predict personality from facial images. In the KODSN validation sample, the correlation
between self-reported and photo-based personality scores ranges between 0.14 and 0.36,
with most correlations exceeding 0.2. These correlations are comparable to those typically
found between survey-based personality self-assessments and assessments made by individuals’ peers (e.g., co-workers), which range from 0.30 to 0.41, and higher than those between
4
self-reported personality and traits assessed by strangers after watching a short interaction
video (Connolly et al., 2007).

The reanimation of pseudoscience in machine learning and its ethical repercussions
Mel Andrews1,2 ∙ Andrew Smart3 andrewsmart@google.com ∙ Abeba Birhane4,5

Affiliations & Notes

Article Info
Cover Image - Patterns, Volume 5, Issue 9
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The bigger picture
Machine learning has a pseudoscience problem. An abundance of ethical issues arising from the use of machine learning (ML)-based technologies—by now, well documented—is inextricably entwined with the systematic epistemic misuse of these tools. We take a recent resurgence of deep learning-assisted physiognomic research as a case study in the relationship between ML-based pseudoscience and attendant social harms—the standard purview of “AI ethics.” In practice, the epistemic and ethical dimensions of ML misuse often arise from shared underlying reasons and are resolvable by the same pathways. Recent use of ML toward the ends of predicting protected attributes from photographs highlights the need for philosophical, historical, and domain-specific perspectives of particular sciences in the prevention and remediation of misused ML.
Summary
The present perspective outlines how epistemically baseless and ethically pernicious paradigms are recycled back into the scientific literature via machine learning (ML) and explores connections between these two dimensions of failure. We hold up the renewed emergence of physiognomic methods, facilitated by ML, as a case study in the harmful repercussions of ML-laundered junk science. A summary and analysis of several such studies is delivered, with attention to the means by which unsound research lends itself to social harms. We explore some of the many factors contributing to poor practice in applied ML. In conclusion, we offer resources for research best practices to developers and practitioners.

Introduction
The fields of AI/machine learning (ML) ethics and responsible AI have documented an abundance of social harms enabled by the methods of ML, both actual and potential. Although the topic is comparatively more obscure, critics have also sought to draw attention to the epistemic failings of ML-based systems: failures of functionality and scientific legitimacy.1,2 The connection between the ethicality and epistemic soundness of deployed ML, however, has received scant attention.3,4
We urge that if the field of AI ethics is to be efficacious in preventing and remediating the social harms flowing from deployed ML systems, it must first grapple with discrepancies between the presumed epistemic operation of these tools and their in-practice ability to achieve those aims. While such an observation is not novel (see Raji et al.3), we build on prior work, both in offering an analysis of the issue from a philosophical vantage point and in venturing into the intricacies of in-practice epistemic and ethical misuses of ML systems. We argue that philosophical, historical, and scientific perspectives are necessary in confronting these issues and that ethical and epistemic issues cannot, and should not, be confronted independently.
A recent surge of deep learning-based studies have claimed the ability to predict unobservable latent character traits, including homosexuality, political ideology, and criminality, from photographs of human faces or other records of outward appearance, including Alam et al.,5 Chandraprabha et al.,6 Hashemi and Hall,7 Kabir et al.,8 Kachur et al.,9 Kosinski et al.,10 Mindoro et al.,11 Parde et al.,12 Peterson et al.,13 Mujeeb

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