What kind of trust does AI deserve, if any?

Alvarado, Rafael

doi:10.1007/s43681-022-00224-x

Cited by 12 publications

(11 citation statements)

References 82 publications

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“…Should the score meet a specific threshold, an investigation is mandatory. But in all other cases, the decision is left to the Alvarado, 2023). But here we are interested in trust in general, which, as argued above, is an implicitly moral notion.…”

Section: Why Morally Trustworthy Ai Is Unnecessarymentioning

confidence: 98%

“Quasi-Metacognitive Machines: Why We Don’t Need Morally Trustworthy AI and Communicating Reliability is Enough”

Dorsch,

Deroy

2024

Philos. Technol.

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Many policies and ethical guidelines recommend developing “trustworthy AI”. We argue that developing morally trustworthy AI is not only unethical, as it promotes trust in an entity that cannot be trustworthy, but it is also unnecessary for optimal calibration. Instead, we show that reliability, exclusive of moral trust, entails the appropriate normative constraints that enable optimal calibration and mitigate the vulnerability that arises in high-stakes hybrid decision-making environments, without also demanding, as moral trust would, the anthropomorphization of AI and thus epistemically dubious behavior. The normative demands of reliability for inter-agential action are argued to be met by an analogue to procedural metacognitive competence (i.e., the ability to evaluate the quality of one’s own informational states to regulate subsequent action). Drawing on recent empirical findings that suggest providing reliability scores (e.g., F1-scores) to human decision-makers improves calibration in the AI system, we argue that reliability scores provide a good index of competence and enable humans to determine how much they wish to rely on the system.

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Section: Why Morally Trustworthy Ai Is Unnecessarymentioning

confidence: 98%

“Quasi-Metacognitive Machines: Why We Don’t Need Morally Trustworthy AI and Communicating Reliability is Enough”

Dorsch,

Deroy

2024

Philos. Technol.

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“…Another exception is made by Alvarado (2022a), who argues that AI is a specific kind of instrument that manipulates its content through epistemic operations and is aimed for tasks which are epistemic in nature. Moreover, Alvarado (2022b) makes the case that within the realm of science, computer simulations can be understood as scientific instruments.…”

Section: Instrument-based Beliefsmentioning

confidence: 99%

Analysis of Beliefs Acquired from a Conversational AI: Instruments-based Beliefs, Testimony-based Beliefs, and Technology-based Beliefs

Freiman

2023

Episteme

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Speaking with conversational AIs, technologies whose interfaces enable human-like interaction based on natural language, has become a common phenomenon. During these interactions, people form their beliefs due to the say-so of conversational AIs. In this paper, I consider, and then reject, the concepts of testimony-based beliefs and instrument-based beliefs as suitable for analysis of beliefs acquired from these technologies. I argue that the concept of instrument-based beliefs acknowledges the non-human agency of the source of the belief. However, the analysis focuses on perceiving signs and indicators rather than content expressed in natural language. At the same time, the concept of testimony-based beliefs does refer to natural language propositions, but there is an underlying assumption that the agency of the testifier is human. To fill the lacuna of analyzing belief acquisition from conversational AIs, I suggest a third concept: technology-based beliefs. It acknowledges the non-human agency-status of the originator of the belief. Concurrently, the focus of analysis is on the propositional content that forms the belief. Filling the lacuna enables analysis that considers epistemic, ethical, and social issues of conversational AIs without excluding propositional content or compromising accepted assumptions about the agency of technologies.

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“…She concludes her position by pointing out that not all AI systems are built to these standards. Ramón Alvarado's (2022) approach begins by questioning the definition of trust in technology in the form "A trusts B". In human relationships trust is more complicated.…”

Section: Should We Trust Ai?mentioning

confidence: 99%

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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What kind of trust does AI deserve, if any?

Cited by 12 publications

References 82 publications

“Quasi-Metacognitive Machines: Why We Don’t Need Morally Trustworthy AI and Communicating Reliability is Enough”

“Quasi-Metacognitive Machines: Why We Don’t Need Morally Trustworthy AI and Communicating Reliability is Enough”

Analysis of Beliefs Acquired from a Conversational AI: Instruments-based Beliefs, Testimony-based Beliefs, and Technology-based Beliefs

Trustworthy AI Alone Is Not Enough.

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