Investigation of EMIC wave scattering as the cause for the BARREL 17 January 2013 relativistic electron precipitation event: A quantitative comparison of simulation with observations

Abstract Electromagnetic ion cyclotron (EMIC) waves were observed at multiple observatory locations for several hours on 17 January 2013. During the wave activity period, a duskside relativistic electron precipitation (REP) event was observed by one of the Balloon Array for Radiation belt Relativistic Electron Losses (BARREL) balloons and was magnetically mapped close to Geostationary Operational Environmental Satellite (GOES) 13. We simulate the relativistic electron pitch angle diffusion caused by gyroresonant interactions with EMIC waves using wave and particle data measured by multiple instruments on board GOES 13 and the Van Allen Probes. We show that the count rate, the energy distribution, and the time variation of the simulated precipitation all agree very well with the balloon observations, suggesting that EMIC wave scattering was likely the cause for the precipitation event. The event reported here is the first balloon REP event with closely conjugate EMIC wave observations, and our study employs the most detailed quantitative analysis on the link of EMIC waves with observed REP to date. Key PointsQuantitative analysis of the first balloon REP with closely conjugate EMIC wavesOur simulation suggests EMIC waves to be a viable cause for the REP eventThe adopted model is proved to be applicable to simulating the REP event

Examining the technology-mediated cycles of injustice that contribute to digital ageism: Advancing the conceptualization of digital ageism: evidence and implications

Our work draws attention to digital ageism referring to the nexus of ageism (discrimination or bias related to age) that is mediated and perpetuated by artificial intelligent (AI) systems and technologies. Building on the World Health Organization's recently published policy brief entitled "Ageism in AI for Health"and our previous work about digital ageism, this paper aims to advance our current understanding and conceptualization of digital ageism in technology and AI systems broadly and beyond health alone. To do this, we will 1) elaborate on our conceptual model and the ageist technology-mediated cycles of injustice that can produce and reinforce digital ageism; 2) present empirical evidence of our descriptive analysis of seven commonly used facial image datasets to highlight data disparities for older adults which will provide real-world evidence that substantiates one of the elements in our ageist cycles of injustice; and 3) summarize results from our grey literature search of various grey literature databases including the AI ethics guidelines Global Inventory to identify guidance documents that address ageism in AI in research or technology development. This paper uniquely contributes conceptual and empirical evidence of digital ageism which will advance knowledge in the field and deepen our understanding of how ageism in AI is fostered by broader ageist cycles of injustice. Lastly, we will briefly provide future considerations to address digital ageism

Age-related bias and artificial intelligence: a scoping review

There are widespread concerns about bias and discriminatory output related to artificial intelligence (AI), which may propagate social biases and disparities. Digital ageism refers to ageism in data, algorithmic models, and the implementation of AI systems and technologies. Currently, the prevalence of digital ageism and the sources of bias are unknown. A scoping review informed by the Arksey and O’Malley methodology was undertaken to explore age-related bias in AI systems, identify how AI systems encode, produce, or reinforce age-related bias, what is known about digital ageism, and the social, ethical and legal implications of age-related bias. A comprehensive search strategy that included five electronic bases and grey literature sources was conducted. A framework of machine learning biases spanning from data to user by Mehrabi et al. is used to present the findings (Mehrabi et al. 2021). The academic search resulted in 7595 articles that were screened according to the inclusion criteria, of which 307 were included for full-text screening, and 49 were included in this review. The grey literature search resulted in 2639 documents screened, of which 235 were included for full text screening, and 25 were found to be relevant to the research questions pertaining to age and AI. As a result, a total of 74 documents were included in this review. The results show that the most common AI applications that intersected with age were age recognition and facial recognition systems. The most frequent machine learning algorithms used were convolutional neural networks and support vector machines. Bias was most frequently introduced in the early “data to algorithm” phase in machine learning and the “algorithm to user” phase specifically with representation bias (n=33) and evaluation bias (n=29), respectively (Mehrabi et al. 2021). The review concludes with a discussion of the ethical implications for the field of AI and recommendations for future research