Behavioural artificial intelligence: an agenda for systematic empirical studies of artificial inference

Pedersen, Tore; Johansen, Christian

doi:10.1007/s00146-019-00928-5

Cited by 13 publications

(3 citation statements)

References 58 publications

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“…(2015 , 356) have shown, when evaluating AI or robots people tend to ‘have much higher expectations regarding their capabilities’. Likewise, Pedersen and Johansen (2020 : 523) have argued that ‘whereas the inherent traits of simple tools are easily decoded, the same traits are not easily decoded—nor even discovered—in advanced technology’ such as AI systems. Now that those systems are being introduced to assist with healthcare, however, we need an open debate about their ‘traits’ and to encourage an understanding of their technical capacities and difficulties ( Thomas 2020 ).…”

Section: Discussionmentioning

confidence: 99%

The AI doctor will see you now: assessing the framing of AI in news coverage

Bunz

Braghieri

2021

AI & Soc

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One of the sectors for which Artificial Intelligence applications have been considered as exceptionally promising is the healthcare sector. As a public-facing sector, the introduction of AI applications has been subject to extended news coverage. This article conducts a quantitative and qualitative data analysis of English news media articles covering AI systems that allow the automation of tasks that so far needed to be done by a medical expert such as a doctor or a nurse thereby redistributing their agency. We investigated in this article one particular framing of AI systems and their agency: the framing that positions AI systems as (1a) replacing and (1b) outperforming the human medical expert, and in which (2) AI systems are personified and/or addressed as a person. The analysis of our data set consisting of 365 articles written between the years 1980 and 2019 will show that there is a tendency to present AI systems as outperforming human expertise. These findings are important given the central role of news coverage in explaining AI and given the fact that the popular frame of ‘outperforming’ might place AI systems above critique and concern including the Hippocratic oath. Our data also showed that the addressing of an AI system as a person is a trend that has been advanced only recently and is a new development in the public discourse about AI.

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Section: Discussionmentioning

confidence: 99%

The AI doctor will see you now: assessing the framing of AI in news coverage

Bunz

Braghieri

2021

AI & Soc

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“…Thus, rather than pointing once more to the problem itself, the present work provides insights into why the bias transfer phenomenon may occur. We operate within the same paradigm and with a similar agenda as those who study human behavior in multidisciplinary research themes such as Behavioral Economics (Tversky and Kahneman 1974;Kahneman et al 1991), Behavioral Transportation Research (Pedersen et al 2011Gärling et al 2014) and our own contributions termed Behavioral Artificial Intelligence (Pedersen and Johansen 2019) and Behavioural Computer Science (Pedersen et al 2018).…”

Section: Algorithmic Bias: Data or The Programmermentioning

confidence: 99%

Studying human-to-computer bias transference

2021

Self Cite

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It is generally agreed that one origin of machine bias is resulting from characteristics within the dataset on which the algorithms are trained, i.e., the data does not warrant a generalized inference. We, however, hypothesize that a different ‘mechanism’ may also be responsible for machine bias, namely that biases may originate from (i) the programmers’ cultural background, including education or line of work, or (ii) the contextual programming environment, including software requirements or developer tools. Combining an experimental and comparative design, we study the effects of cultural and contextual metaphors, and test whether each of these are ‘transferred’ from the programmer to the program, thus constituting a machine bias. Our results show that (i) cultural metaphors influence the programmer’s choices and (ii) contextual metaphors induced through priming can be used to moderate or exacerbate the effects of the cultural metaphors. Our studies are purposely performed with users of varying educational backgrounds and programming skills stretching from novice to proficient.

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“…[36]. An additional consideration can be given to the methodology of AI whereby the training of it can be considered and includes; Training AI -can learn and improve over time; and Inference AI -requires human interference to make more relevant suggestions such that [55] -expertise in epistemology, critical thinking and reasoning are crucial to ensure human oversight of the artificial intelligent judgements and decisions that are made, because only competent human insight into AI-inference processes will ensure accountability‖. This method of classifying AI shares some similarities, but is advanced, with the following where AI has been classified is by the level of human systems interaction and includes; Supervised AI -which requires human monitoring and feedback; Unsupervised AIthe unsupervised suffers from the lack of -expert‖ touch (in the context of dermatology) during the training [1] and could be considered Black Box AI which does not require human interference, and Reinforcement AI whereby occasional human interference is needed.…”

Section: Categorisation Of Aimentioning

confidence: 99%

A Solution to the Hyper Complex, Cross Domain Reality of Artificial Intelligence: The Hierarchy of AI

Kear¹,

Folkes²

2020

IJACSA

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Artificial Intelligence (AI) is an umbrella term used to describe machine-based forms of learning. This can encapsulate anything from Siri, Apple's smartphone-based assistant, to Tesla's autonomous vehicles (self-driving cars). At present, there are no set criteria to classify AI. The implications of which include public uncertainty, corporate scepticism, diminished confidence, insufficient funding and limited progress. Current substantial challenges exist with AI such as the use of combinationally large search space, prediction errors against ground truth values, the use of quantum error correction strategies. These are discussed in addition to fundamental data issues across collection, sample error and quality. The concept of cross realms and domains used to inform AI, is considered. Furthermore there is the issue of the confusing range of current AI labels. This paper aims to provide a more consistent form of classification, to be used by institutions and organisations alike, as they endeavour to make AI part of their practice. In turn, this seeks to promote transparency and increase trust. This has been done through primary research, including a panel of data scientists / experts in the field, and through a literature review on existing research. The authors propose a model solution in that of the Hierarchy of AI.

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Behavioural artificial intelligence: an agenda for systematic empirical studies of artificial inference

Cited by 13 publications

References 58 publications

The AI doctor will see you now: assessing the framing of AI in news coverage

The AI doctor will see you now: assessing the framing of AI in news coverage

Studying human-to-computer bias transference

A Solution to the Hyper Complex, Cross Domain Reality of Artificial Intelligence: The Hierarchy of AI

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