Webinar – IEEE Ethics and COVID-19 related Initiatives by Prof Ali Hessami (Chair, UK & Ireland SIGHT) and Dr Keeley Crocket (Chair, WIE Computational Intelligence)
The Special Interest Group on Humanitarian Technologies (SIGHT) in the UK & Ireland Section is pleased to announce a series of online lectures in connection with the Covid-19 related technologies. These are being organised in collaboration with the Technology Ethics SIG and the SMC Chapters.
IEEE Ethics & COVID Related Certification Initiatives
The IEEE Standards Association’s Ethics Certification Program for Autonomous and Intelligent Systems (ECPAIS) aims to create specifications for certification and marking processes that advance transparency, accountability and reduction in algorithmic bias in Autonomous and Intelligent Systems (A/IS). The value of this certification process in the marketplace and society at large cannot be underestimated. The proliferation of systems in the form of smart homes, companion robots, autonomous vehicles or any myriad of products and services that already exist today desperately need to easily and visually communicate to consumers and citizens whether they are deemed “safe” or “trusted” by a globally recognized body of experts providing a publicly available and transparent series of marks.
In this introductory talk, An overview of the ECPAIS programme and a special initiative regarding developing ethical certification criteria for Contact Tracing Technologies is given by Prof Ali Hessami, the IEEE ECPAIS programme VC and Process Architect.
The spread of misinformation poses a severe threat to the future take-up of a Covid-19 vaccine (Nature 13 May; ArXiv2004.00673). There are careful analyses of the sources of misinformation on social media (Covid-19 Disinformation Briefings (ISD 2020)) but these require significant individual human expertise. Prior to the COVID-19 pandemic, the take-up of childhood vaccinations in England was declining, suggesting an already increasing wariness around vaccination. Therefore, tracing how, and when, stories about vaccines spread through the general population is important as it will improve our understanding of these processes allowing misinformation about a COVID-19 vaccine to be more effectively countered.
Understanding the structure of the spread of news about Covid19 vaccination – a machine learning approach applied to Twitter data by Dr Keeley Crockett provides a brief overview of a project that applies machine learning techniques and semantic similarity measures to obtain a wider, data-led picture of how and when information about Covid-19 and vaccines is spread via tweets.
