'Of Water Drops and Atomic Nuclei: Analogies and Pursuit Worthiness in Science'

This paper highlights a use of analogies in science that so far has received relatively little systematic discussion: providing reasons for pursuing a model or theory. Using the development of the liquid drop model as a test case, I critically assess two extant pursuit worthiness accounts: (i) that analogies justify pursuit by supporting plausibility arguments and (ii) that analogies can serve as a guide to potential theoretical unification. Neither of these fit the liquid drop model case. Instead, I develop an alternative account, based on the idea that analogies facilitate the transfer of a well-understood modelling strategy to a new domain.Leverhulme Trust Research Centre Grant (awarded to the Leverhulme Centre for the Future of Intelligence

Motivations and Risks of Machine Ethics

This paper surveys reasons for and against pursuing the field of machine ethics, understood as research aiming to build 'ethical machines.' We clarify the nature of this goal, why it is worth pursuing, and the risks involved in its pursuit. First, we survey and clarify some of the philosophical issues surrounding the concept of an 'ethical machine' and the aims of machine ethics. Second, we argue that while there are good prima facie reasons for pursuing machine ethics, including the potential to improve the ethical alignment of both humans and machines, there are also potential risks that must be considered. Third, we survey these potential risks and point to where research should be devoted to clarifying and managing potential risks. We conclude by making some recommendations about the questions that future work could address

Ethical considerations in the early detection of Alzheimer's disease using speech and AI

While recent studies indicate that AI could play an important role in detecting early signs of Alzheimer's disease in speech, this use of data from individuals with cognitive decline raises numerous ethical concerns. In this paper, we identify and explain concerns related to autonomy (including consent, depersonalization and disclosure), privacy and data protection (including the handling of personal content and medical information), welfare (including distress, discrimination and reliability), transparency (including the interpretability of language features and AI-based decision-making for developers and clinicians), and fairness (including bias and the distribution of benefits). Our aim is to not only raise awareness of the ethical concerns posed by the use of AI in speech-based Alzheimer's detection, but also identify ways in which these concerns might be addressed. To this end, we conclude with a list of suggestions that could be incorporated into ethical guidelines for researchers and clinicians working in this area

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