Quarantining online hate speech: technical and ethical perspectives

Abstract: In this paper we explore quarantining as a more ethical method for delimiting the spread of Hate Speech via online social media platforms. Currently, companies like Facebook, Twitter, and Google generally respond reactively to such material: offensive messages that have already been posted are reviewed by human moderators if complaints from users are received. The offensive posts are only subsequently removed if the complaints are upheld; therefore, they still cause the recipients psychological harm. In addition, this approach has frequently been criticised for delimiting freedom of expression, since it requires the service providers to elaborate and implement censorship regimes. In the last few years, an emerging generation of automatic Hate Speech detection systems has started to offer new strategies for dealing with this particular kind of offensive online material. Anticipating the future efficacy of such systems, the present article advocates an approach to online Hate Speech detection that is analogous to the quarantining of malicious computer software. If a given post is automatically classified as being harmful in a reliable manner, then it can be temporarily quarantined, and the direct recipients can receive an alert, which protects them from the harmful content in the first instance. The quarantining framework is an example of more ethical online safety technology that can be extended to the handling of Hate Speech. Crucially, it provides flexible options for obtaining a more justifiable balance between freedom of expression and appropriate censorship

Das Krankheitsbild der Präeklampsie: Eine retrospektive Analyse klinischer Marker zwischen early- und late-onset schweren Präeklampsien und von Prädiktoren des perinatalen Outcomes & Eine prospektive Studie zur Wertigkeit der sFlt-1/PlGF Ratio im zweiten Trimenon zur Vorhersage des späteren Krankheitsverlaufes in einem Hochrisikokollektiv

Beim Vergleich klinischer Unterschiede zwischen schweren early-onset (EO) und late-onset (LO) Präeklampsien (PEs) zeigen sich signifikante Differenzen. Uterine und umbilikale Dopplerflussmessungen können helfen, das perinatale Risiko besser zu evaluieren. Die Kombination der uterinen Dopplersonographie mit der Bestimmung von sFlt-1 und PlGF verbessert im zweiten Trimenon die Prädiktion des späteren Krankheitsverlaufes. Durch den hohen negativ prädiktiven Faktor ist die sFlt-1/PlGF Ratio besonders zur Ausschlussdiagnostik einer drohenden PE und Entbindung <34+0 SSW gut geeignet

Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering

In recent years, unsupervised analysis of microbiome data, such as microbial network analysis and clustering, has increased in popularity. Many new statistical and computational methods have been proposed for these tasks. This multiplicity of analysis strategies poses a challenge for researchers, who are often unsure which method(s) to use and might be tempted to try different methods on their dataset to look for the “best” ones. However, if only the best results are selectively reported, this may cause over-optimism: the “best” method is overly fitted to the specific dataset, and the results might be non-replicable on validation data. Such effects will ultimately hinder research progress. Yet so far, these topics have been given little attention in the context of unsupervised microbiome analysis. In our illustrative study, we aim to quantify over-optimism effects in this context. We model the approach of a hypothetical microbiome researcher who undertakes four unsupervised research tasks: clustering of bacterial genera, hub detection in microbial networks, differential microbial network analysis, and clustering of samples. While these tasks are unsupervised, the researcher might still have certain expectations as to what constitutes interesting results. We translate these expectations into concrete evaluation criteria that the hypothetical researcher might want to optimize. We then randomly split an exemplary dataset from the American Gut Project into discovery and validation sets multiple times. For each research task, multiple method combinations (e.g., methods for data normalization, network generation, and/or clustering) are tried on the discovery data, and the combination that yields the best result according to the evaluation criterion is chosen. While the hypothetical researcher might only report this result, we also apply the “best” method combination to the validation dataset. The results are then compared between discovery and validation data. In all four research tasks, there are notable over-optimism effects; the results on the validation data set are worse compared to the discovery data, averaged over multiple random splits into discovery/validation data. Our study thus highlights the importance of validation and replication in microbiome analysis to obtain reliable results and demonstrates that the issue of over-optimism goes beyond the context of statistical testing and fishing for significance

Marine temperatures underestimated for past greenhouse climate

AbstractUnderstanding the Earth’s climate system during past periods of high atmospheric CO2 is crucial for forecasting climate change under anthropogenically-elevated CO2. The Mesozoic Era is believed to have coincided with a long-term Greenhouse climate, and many of our temperature reconstructions come from stable isotopes of marine biotic calcite, in particular from belemnites, an extinct group of molluscs with carbonate hard-parts. Yet, temperatures reconstructed from the oxygen isotope composition of belemnites are consistently colder than those derived from other temperature proxies, leading to large uncertainties around Mesozoic sea temperatures. Here we apply clumped isotope palaeothermometry to two distinct carbonate phases from exceptionally well-preserved belemnites in order to constrain their living habitat, and improve temperature reconstructions based on stable oxygen isotopes. We show that belemnites precipitated both aragonite and calcite in warm, open ocean surface waters, and demonstrate how previous low estimates of belemnite calcification temperatures has led to widespread underestimation of Mesozoic sea temperatures by ca. 12 °C, raising estimates of some of the lowest temperature estimates for the Jurassic period to values which approach modern mid-latitude sea surface temperatures. Our findings enable accurate recalculation of global Mesozoic belemnite temperatures, and will thus improve our understanding of Greenhouse climate dynamics.</jats:p

Quarantining online hate speech: technical and ethical perspectives

Abstract: In this paper we explore quarantining as a more ethical method for delimiting the spread of Hate Speech via online social media platforms. Currently, companies like Facebook, Twitter, and Google generally respond reactively to such material: offensive messages that have already been posted are reviewed by human moderators if complaints from users are received. The offensive posts are only subsequently removed if the complaints are upheld; therefore, they still cause the recipients psychological harm. In addition, this approach has frequently been criticised for delimiting freedom of expression, since it requires the service providers to elaborate and implement censorship regimes. In the last few years, an emerging generation of automatic Hate Speech detection systems has started to offer new strategies for dealing with this particular kind of offensive online material. Anticipating the future efficacy of such systems, the present article advocates an approach to online Hate Speech detection that is analogous to the quarantining of malicious computer software. If a given post is automatically classified as being harmful in a reliable manner, then it can be temporarily quarantined, and the direct recipients can receive an alert, which protects them from the harmful content in the first instance. The quarantining framework is an example of more ethical online safety technology that can be extended to the handling of Hate Speech. Crucially, it provides flexible options for obtaining a more justifiable balance between freedom of expression and appropriate censorship

Quarantining online hate speech: technical and ethical perspectives

Abstract: In this paper we explore quarantining as a more ethical method for delimiting the spread of Hate Speech via online social media platforms. Currently, companies like Facebook, Twitter, and Google generally respond reactively to such material: offensive messages that have already been posted are reviewed by human moderators if complaints from users are received. The offensive posts are only subsequently removed if the complaints are upheld; therefore, they still cause the recipients psychological harm. In addition, this approach has frequently been criticised for delimiting freedom of expression, since it requires the service providers to elaborate and implement censorship regimes. In the last few years, an emerging generation of automatic Hate Speech detection systems has started to offer new strategies for dealing with this particular kind of offensive online material. Anticipating the future efficacy of such systems, the present article advocates an approach to online Hate Speech detection that is analogous to the quarantining of malicious computer software. If a given post is automatically classified as being harmful in a reliable manner, then it can be temporarily quarantined, and the direct recipients can receive an alert, which protects them from the harmful content in the first instance. The quarantining framework is an example of more ethical online safety technology that can be extended to the handling of Hate Speech. Crucially, it provides flexible options for obtaining a more justifiable balance between freedom of expression and appropriate censorship

The practical ethics of bias reduction in machine translation: why domain adaptation is better than data debiasing

Funder: This research was funded by the Humanities and Social Change International FoundationAbstractThis article probes the practical ethical implications of AI system design by reconsidering the important topic of bias in the datasets used to train autonomous intelligent systems. The discussion draws on recent work concerning behaviour-guiding technologies, and it adopts a cautious form of technological utopianism by assuming it is potentially beneficial for society at large if AI systems are designed to be comparatively free from the biases that characterise human behaviour. However, the argument presented here critiques the common well-intentioned requirement that, in order to achieve this, all such datasets must be debiased prior to training. By focusing specifically on gender-bias in Neural Machine Translation (NMT) systems, three automated strategies for the removal of bias are considered – downsampling, upsampling, and counterfactual augmentation – and it is shown that systems trained on datasets debiased using these approaches all achieve general translation performance that is much worse than a baseline system. In addition, most of them also achieve worse performance in relation to metrics that quantify the degree of gender bias in the system outputs. By contrast, it is shown that the technique of domain adaptation can be effectively deployed to debias existing NMT systems after they have been fully trained. This enables them to produce translations that are quantitatively far less biased when analysed using gender-based metrics, but which also achieve state-of-the-art general performance. It is hoped that the discussion presented here will reinvigorate ongoing debates about how and why bias can be most effectively reduced in state-of-the-art AI systems.</jats:p