Artificial wisdom: a philosophical framework

Tsai, Cheng‐Hung

doi:10.1007/s00146-020-00949-5

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…Explainability as to why things are fair and what makes them intrinsically fair. Indeed, a genuinely wise agent, it has been posited, must be able to realize what makes good things for well-being good (Tsai 2020).…”

Section: Discussionmentioning

confidence: 99%

Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments

Izzidien

2021

AI & Soc

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Programming artificial intelligence (AI) to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair (e.g., slur, insult) or fair (e.g., thank, appreciate). It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be willing to accept if done to themselves. Secondly, that such a construal is ontologically reflected in Word Embeddings, by virtue of their ability to reflect the dimensions of such a perception. These dimensions being: responsibility vs. irresponsibility, gain vs. loss, reward vs. sanction, joy vs. pain, all as a single vector (FairVec). The paper finds it possible to quantify and qualify a verb as fair or unfair by calculating the cosine similarity of the said verb’s embedding vector against FairVec—which represents the above dimensions. We apply this to Glove and Word2Vec embeddings. Testing on a list of verbs produces an F1 score of 95.7, which is improved to 97.0. Lastly, a demonstration of the method’s applicability to sentence measurement is carried out.

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Section: Discussionmentioning

confidence: 99%

Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments

Izzidien

2021

AI & Soc

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“…The notion of creating AI that shares our societal values and could be considered to be wise is a relatively new area of exploration. [ 22 , 23 , 24 ] The future AI will need to have some aspects of emotional intelligence,[ 25 ] morality,[ 26 ] and empathy. [ 27 ] Paiva et al .…”

Section: Future Of Ai: Artificial Wisdom (Aw)mentioning

confidence: 99%

Clinical Practice Guidelines on using artificial intelligence and gadgets for mental health and well-being

Singh,

Sarkar,

Gaur

et al. 2024

Indian Journal of Psychiatry

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“…Therefore, this is the absolute correct time to study and understand the wisdom and convert it into an implementable idea as the fundamental concept for AW. AW is only possible if we go with the specificationism approach (Tsai, 2020) (i.e., to specify and discuss the practical approach of how the system should be able to generate practical reasoning) rather than the instrumentalism approach (i.e., talking about practical reasoning only in philosophical terms and concepts) (Tsai, 2020).…”

Section: Man-machine Symbiosis: Artificial Wisdom and Empathymentioning

confidence: 99%

Artificial Wisdom vs. Human Wisdom: A Potential Quest

Sharma¹,

Kaman²,

Banerjee³

2023

Preprint

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Artificial wisdom, also known as artificial general intelligence (AGI), aims to design machines that can grasp and learn any intellectual phenomenon - like contemplation, learning and decision-making - as a person can. AGI is still in its early stages of development. Human cognition underpins AI, where people build learning models and datasets for AI training. However, it is crucial to have humans in charge and human supervision in decision-making while developing and using AI. This article examines artificial wisdom and discusses real-world AI applications that exhibit it. Self-driving vehicles, self-interfaces, medical diagnostics, game-playing AI, natural language processing, robotics, and generative AI are examples. AI may help with decision-making, problem-solving, language processing, and data production. They also emphasize the necessity of continual research and development to improve AI and ensure its ethical use. This article also discusses the triumphs and pitfalls of artificial wisdom research, admitting that AGI development is still controversial. It emphasizes human-centered AI development and application. As AI becomes more significant in our lives, it must be built and utilized in a way that respects human values, ethics, and decision-making. In conclusion, AI research into artificial wisdom has the potential to revolutionize different sectors and help mankind, but it also has ethical and societal repercussions that must be carefully considered.

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Artificial wisdom: a philosophical framework

Cited by 10 publications

References 21 publications

Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments

Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments

Clinical Practice Guidelines on using artificial intelligence and gadgets for mental health and well-being

Artificial Wisdom vs. Human Wisdom: A Potential Quest

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