RLUK Digital Shift Forum (April 2022) - The Reasonable Robot: Artificial Intelligence and the Law by Ryan Abbott, Professor of Law and Health Sciences, University of Surrey

Title of  talk:

The Reasonable Robot: Artificial Intelligence and the Law

 

Presenter:

Ryan Abbott, Professor of Law and Health Sciences, University of Surrey

Focus of talk:

AI and people do not compete on a level-playing field. Self-driving vehicles may be safer than human drivers, but laws often penalise such technology. People may provide superior customer service, but businesses are automating to reduce their taxes. AI may innovate more effectively, but an antiquated legal framework constrains inventive AI. In The Reasonable Robot, Ryan  argues that the law should not discriminate between AI and human behaviour and proposes a new legal principle that will ultimately improve human well-being.

To watch presentation:

https://www.rluk.ac.uk/dsf-on-demand/#DSFRA

Duration:

49:26

Research led by University of Lyon, France & University College, UK - Your Navigational Skills are Intricately Linked to your Past, Research Shows [Science Alert, April 2022]

Title:

Your Navigational Skills are Intricately Linked to your Past, Research Shows 

Author:

Conor Feehly

Published:

Science Alert, 18 April 2022

https://www.sciencealert.com/your-navigational-skills-are-intricately-linked-to-your-past-research-shows

From the article:

An international team led by researchers from CNRS in France and University College London found that people are better at navigating environments topologically similar to where they grew up.

ALSO SEE

Coutrot, A., Manley, E., Goodroe, S. et al. Entropy of city street networks linked to future spatial navigation ability. Nature 604, 104–110 (2022). 

https://doi.org/10.1038/s41586-022-04486-7

Impact of COVID-19 lockdown on mental health in Germany: longitudinal observation of different mental health trajectories and protective factors [Scholarly Article - Translational Psychiatry, 2021]

 

Title:

Impact of COVID-19 lockdown on mental health in Germany: longitudinal observation of different mental health trajectories and protective factors 

 

Authors:

K. F. Ahrens, R. J. Neumann, B. Kollmann, J. Brokelmann, N. M. von Werthern, A. Malyshau, D. Weichert, B. Lutz, C. J. Fiebach, M. Wessa, R. Kalisch, M. M. Plichta, K. Lieb, O. Tüscher & A. Reif 

 

Published:

Translational Psychiatry, 17 July 2021

https://www.nature.com/articles/s41398-021-01508-2

 

Abstract:

The COVID-19 pandemic and resulting measures can be regarded as a global stressor. Cross-sectional studies showed rather negative impacts on people’s mental health, while longitudinal studies considering pre-lockdown data are still scarce. The present study investigated the impact of COVID-19 related lockdown measures in a longitudinal German sample, assessed since 2017. During lockdown, 523 participants completed additional weekly online questionnaires on e.g., mental health, COVID-19-related and general stressor exposure. Predictors for and distinct trajectories of mental health outcomes were determined, using multilevel models and latent growth mixture models, respectively. Positive pandemic appraisal, social support, and adaptive cognitive emotion regulation were positively, whereas perceived stress, daily hassles, and feeling lonely negatively related to mental health outcomes in the entire sample. Three subgroups (“recovered,” 9.0%; “resilient,” 82.6%; “delayed dysfunction,” 8.4%) with different mental health responses to initial lockdown measures were identified. Subgroups differed in perceived stress and COVID-19-specific positive appraisal. Although most participants remained mentally healthy, as observed in the resilient group, we also observed inter-individual differences. Participants’ psychological state deteriorated over time in the delayed dysfunction group, putting them at risk for mental disorder development. Consequently, health services should especially identify and allocate resources to vulnerable individuals.

 

Columbia University - AI learns to predict human behavior from videos [TechXplore, June 2021]

Title:

AI learns to predict human behavior from videos

 

By:

Columbia University School of Engineering and Applied Science

 

Published:

TechXplore, 28 June 2021

https://techxplore.com/news/2021-06-ai-human-behavior-videos.html

 

From the article:

In a new study, Columbia Engineering researchers unveil a computer vision technique for giving machines a more intuitive sense for what will happen next by leveraging higher-level associations between people, animals, and objects.

 

University of Alberta, Canada - AI Predicts Schizophrenia Symptoms in At-Risk Population [TechnologyNetworks, 27 January 2021]

Title:

AI Predicts Schizophrenia Symptoms in At-Risk Population 

 

Original story from:

University of Alberta, Canada

 

Published:

TechnologyNetworks, 27 January 2021

https://www.technologynetworks.com/informatics/news/ai-predicts-schizophrenia-symptoms-in-at-risk-population-344937

 

From the article:

University of Alberta researchers have taken another step forward in developing an artificial intelligence tool to predict schizophrenia by analyzing brain scans. 

 

In recently published research, the tool was used to analyze functional magnetic resonance images of 57 healthy first-degree relatives (siblings or children) of schizophrenia patients. It accurately identified the 14 individuals who scored highest on a self-reported schizotypal personality trait scale.

 

Scholarly Article (2020) - Applying principles of behaviour change to reduce SARS-CoV-2 transmission

Title:
Applying principles of behaviour change to reduce SARS-CoV-2 transmission

Authors:
Robert West, Susan Michie, G. James Rubin & Richard Amlôt 

Published:
Nature Human Behaviour, 2020

Available:
https://www.nature.com/articles/s41562-020-0887-9

Abstract:
Human behaviour is central to transmission of SARS-Cov-2, the virus that causes COVID-19, and changing behaviour is crucial to preventing transmission in the absence of pharmaceutical interventions. Isolation and social distancing measures, including edicts to stay at home, have been brought into place across the globe to reduce transmission of the virus, but at a huge cost to individuals and society. In addition to these measures, we urgently need effective interventions to increase adherence to behaviours that individuals in communities can enact to protect themselves and others: use of tissues to catch expelled droplets from coughs or sneezes, use of face masks as appropriate, hand-washing on all occasions when required, disinfecting objects and surfaces, physical distancing, and not touching one’s eyes, nose or mouth. There is an urgent need for direct evidence to inform development of such interventions, but it is possible to make a start by applying behavioural science methods and models.