I humanize, therefore I understand? Effects of explanations and humanization of intelligent systems on perceived and objective user understanding

Ngo, Thao; Krämer, Nicole C.

doi:10.31234/osf.io/6az2h

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…Gunning and Aha (2019), summarise three main needs for good explainable AI: (1) producing more explainable models, which would be necessary for a technical and social evaluation of an AI product (2) designing better explanation interfaces to facilitate interaction with the required knowledge, and (3) understanding the psychological requirements for providing effective explanations to humans, which will impact the opportunity for technically literate or non-technically literate individuals in a given society to participate in the evaluation of a tool. A paper by Ngo and Krämer (2022) concludes that explanations lead to a sense of misleading understanding.…”

Section: Transparencymentioning

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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Section: Transparencymentioning

confidence: 99%

Trustworthy AI Alone Is Not Enough.

Pérez y Madrid,

Wright

2023

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In Seal We Trust? Investigating the Effect of Certifications on Perceived Trustworthiness of AI Systems

Wischnewski,

Krämer,

Janiesch

et al. 2024

HMC

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Trust certification through so-called trust seals is a common strategy to help users ascertain the trustworthiness of a system. In this study, we examined trust seals for AI systems from two perspectives: (1) In a pre-registered online study participants, we asked whether trust seals can increase user trust in AI systems, and (2) qualitatively, we investigated what participants expect from such AI seals of trust. Our results indicate mixed support for the use of AI seals. While trust seals generally did not affect the participants’ trust, their trust in the AI system increased if they trusted the seal-issuing institution. Moreover, although participants understood verification seals the least, they desired verifications of the AI system the most.

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I humanize, therefore I understand? Effects of explanations and humanization of intelligent systems on perceived and objective user understanding

Cited by 2 publications

References 41 publications

Trustworthy AI Alone Is Not Enough.

Trustworthy AI Alone Is Not Enough.

In Seal We Trust? Investigating the Effect of Certifications on Perceived Trustworthiness of AI Systems

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