Digital humanism: A philosophy for 21st century digital society

Our contemporary global digital society is not always a good place to live. Authoritarianism, hatred, false news, post-truth culture, the COVID-19 anti-vaccination movement, COVID-19 conspiracy theories, and political polarisation are organised via the Internet. The public sphere is highly polarised. Today, many humans tend to think of other humans mainly in terms of friends and enemies. Robots and Artificial Intelligence-based automation have created new challenges for the world of work. Decades of neoliberalism have increased inequalities. The COVID-19 pandemic has shown the vulnerability of humanity to viruses and health crises. Humanity and society are in a major crisis and digitalisation mediates this crisis. Digital Humanism explores how Humanism can help us to critically understand how digital technologies shape society and humanity, providing an introduction to Humanism in the digital age. Fuchs introduces the approach of Digital Humanism and outlines foundations of a Radical Digital Humanism, analysing what decolonisation of academia and the study of the digital, media and communication means;what the roles are of robots, automation, and Artificial Intelligence in digital capitalism, and how the communication of death and dying has been mediated by digital technologies, capitalist necropower, and digital capitalism. In order to save humanity and society, we need Radical Digital Humanism now. © 2022 Emerald Publishing Limited.

Predicting and Characterizing Legal Claims of Hospitals with Computational Intelligence: the Legal and Ethical Implications

In this paper we propose a fuzzy logic-based approach to analyze UK National Health Service (NHS) public administrative data related to pre-and post-pandemic claims filed by patients, analyzing the legal and ethical issues connected to the use of Artificial Intelligence systems, including our own, to take critical decisions having a significant impact on patients, such as employing computational intelligence to justify the management choices related to Intensive Care Unit (ICU) bed allocation. Differently from previous papers, in this work we follow an unsupervised approach and, specifically, we perform an analysis of UK hospitals by means of a computational intelligence algorithm integrating Fuzzy C-Means and swarm intelligence. The dataset that we analyse allows us to compare pre-and post-pandemic data, to analyze the ethical and legal challenges of the use of computational intelligence for critical decision-making in the health care field. © 2022 IEEE.

Comparing Interpretable AI Approaches for the Clinical Environment: an Application to COVID-19

Machine Learning (ML) models play an important role in healthcare thanks to their remarkable performance in predicting complex phenomena. During the COVID-19 pandemic, different ML models were implemented to support decisions in the medical settings. However, clinical experts need to ensure that these models are valid, provide clinically useful information, and are implemented and used correctly. In this vein, they need to understand the logic behind the models to be able to trust them. Hence, developing transparent and interpretable models has increasing relevance. In this work, we applied four interpretable ML models including logistic regression, decision tree, pyFUME, and RIPPER to classify suspected COVID-19 patients based on clinical data collected from blood samples. After preprocessing the data set and training the models, we evaluate the models based on their predictive performance. Then, we illustrate that interpretability can be achieved in different ways. First, SHAP explanations are built from logistic regression and decision trees to obtain the features' importance. Then, the potential of pyFUME and RIPPER in providing inherent interpretability are reflected. Finally, potential ways to achieve trust in future studies are briefly discussed. © 2022 IEEE.

Examining the Effects of HIV Infection on Severity of Outcomes in Those with COVID-19

Background. Throughout the SARS-CoV-2 pandemic, there have been many questions about how COVID-19 affects patients living with HIV (PLWH). We examined the clinical courses of 45 PLWH who required hospitalization with SARS-CoV-2 infection. Methods. This is a retrospective cohort study in which ICD-10 codes were used to identify PLWH who were admitted to three large hospital systems in Memphis, TN with COVID-19. We included all patients ≥ 18 years of age with HIV and a documented positive SARS-CoV-2 PCR test. After manual ion from the electronic health records, chi-squared and T-tests were performed to evaluate associations between patient-level factors and outcomes. Results. A total of 45 patients with HIV who tested positive for SARS-CoV-2 were admitted to Memphis, TN area hospitals between March 2020 and October 2020. 18 (40%) were female, 43 (95.6%) were Black, and the average age was 50.3 years (SD 12.6). The average BMI was 30.2 (SD 8.6). 40 (88.9%) patients admitted had at least one comorbidity with the most common being hypertension (28 patients, 62.2%) and diabetes (14 patients, 31.1%). 24 (46.7%) patients had a Charlson Comorbidity Index > 3. 15/43 (48.4%) patients had a CD4 count < 200, and 35 (77.8%) were on ART. 30 (66.7%) patients met SIRS criteria within 24 hours of admission, and 27 (60%) required some form of oxygen supplementation during hospitalization, including 4 (8.9%) who required intubation. The average length of stay was 10.4 days (SD 12.5). 9 (20%) patients required an ICU stay, and 3 (6.7%) died. BMI > 30, CD4 count < 200, and viral load > 1000 were not associated with worse outcomes. Both a Charlson Comorbidity Index > 3 and the absence of ART were associated with need for ICU-level care. Conclusion. Viral load, CD4 count, and BMI were not correlated with differences in mortality or oxygen use in our study. Patients with higher Charlson Comorbidity Indices and patients who were not on ART at presentation were significantly more likely to require the ICU. Further study is needed to definitively determine factors affecting the outcomes of PLWH with SARS-CoV-2 infection.

Engels@200: Friedrich engels and digital capitalism. how relevant are engels’s works 200 years after his birth?

This paper takes Friedrich Engels 200th birthday on 28 November 2020 as occasion to ask: How relevant are Friedrich Engels’s works in the age of digital capitalism? It shows that Engels class-struggle oriented theory can and should inform 21st century social science and digital social research. Based on a reading of Engels’s works, the article discusses how to think of scientific socialism as critical social science today, presents a critique of computational social science as digital positivism, engages with foundations of digital labour analysis, the analysis of the international division of digital labour, updates Engels’s Condition of the Working Class in England in the age of digital capitalism, analyses the role of trade unions and digital class struggles in digital age, analyses the social murder of workers in the COVID-19 crisis, engages with platform co-operatives, digital commons projects and public service Internet platforms are concrete digital utopias that point beyond digital capital(ism). Engels’s analysis is updated for critically analysing the digital conditions of the working class today, including the digital labour of hardware assemblers at Foxconn and Pegatron, the digital labour aristocracy of software engineers at Google, online freelance workers, platform workers at capitalist platform corporations such as Uber, Deliveroo, Fiverr, Upwork, or Freelancer, and the digital labour of Facebook users. Engels’s 200th birthday reminds us of the class character of digital capitalism and that we need critical digital social science as a new form of scientific socialism. © 2020, Unified Theory of Information Research Group. All rights reserved.