Sex Robots, Marriage, Health, Procreation, and Human Image

Fan, Ruiping

doi:10.1007/978-3-030-82280-4_11

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…For our purposes, we will be using sex robots as a use case to show the regulatory and moral challenges facing trustworthy AI. In this case, normally in human form (Fan, 2022), sex robots go beyond the domain of sex toys through their ability to closely simulate an intimate human relationship. Sex robots include the possibility for humans to develop feelings of intimacy towards the anthropomorphised technology unmatched by other sexual objects (such as sex dolls) while also responding to different human cues and adjusting their behaviour accordingly (such as providing companionship (ibid.…”

Section: Chatgptmentioning

confidence: 99%

“…Nevertheless, Fan (2022) acknowledges how finding ourselves in need of considering such an option is problematic. Instead of creating a society where sex robots meet the needs of people suffering from disability, we should instead strive for a society in which we prioritise ways to maintain human relations between people in these situations.…”

Section: Human Autonomymentioning

confidence: 99%

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Trustworthy AI Alone Is Not Enough.

Pérez y Madrid,

Wright

2023

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Recent advances in AI through machine learning (ML) 1 and deep learning (DL) 2 techniques have arrived with a lot of momentum in society. In the 2010s there were several developments. The large amount of available data and the development of advanced model fitting algorithms based on artificial neural networks (ANN) 3 enabled the creation of advanced pattern recognition systems. First, the coveted task of handwritten digit recognition was achieved. The next stage was the recognition of shapes in images and faces, and from then its usefulness in other fields began to be tested, from industrial applications to others related to human behaviour.The great hope was and still is the possibility of solving problems considered reserved for human intelligence. During these years it has been possible to create models which we classify in two categories: GLOSSARY Artificial neural networks (ANNs): usually simply called neural networks (NN) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains (see also https://en.wikipedia.org/ wiki/Artificial_neural_network). Black box: A black box is a system that can only be known in terms of its inputs and outputs without knowing its inner workings. Its mode of operation is opaque, hence the name black. AI algorithms using artificial neural networks typically require a huge number of parameters. This makes human understanding of their operation impossible. For this reason, such algorithms are considered black boxes. Data Science: Data science is an interdisciplinary field focused on extracting knowledge from large data sets and applying that knowledge and insights to solve problems in a wide range of application domains. The field includes preparing data for analysis, framing data science problems, analysing data, developing data-driven solutions and presenting results to inform high-level decisions in a wide range of application domains. As such, it incorporates knowledge of computer science, statistics, mathematics, data visualisation, information visualisation, data integration, graphic design, complex systems, communication and business (See also https://en.wikipedia.org/wiki/Data_ science). Deep Learning (DL): Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Generative AI: is a type of artificial intelligence system capable of generating text, images or other media in response to instructions. Generative AI models learn the patterns and structure of their input training data and then generate new data with similar characteristics. Machine learning (ML): is a branch of AI that allows machines to learn from large amounts of data. Metadata: data that describe other data, e.g. their information content. They are analogous to indexes used in a library, such as author, title, publisher, date, etc. In the case of Spotify this could be singer, composer, style, etc.

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Section: Chatgptmentioning

confidence: 99%

Section: Human Autonomymentioning

confidence: 99%