Anthropomorphizing AlphaGo: a content analysis of the framing of Google DeepMind’s AlphaGo in the Chinese and American press

Curran, Nathaniel Ming; Sun, Jingyi; Hong, Joo-Wha

doi:10.1007/s00146-019-00908-9

Cited by 20 publications

(14 citation statements)

References 19 publications

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“…Methods such as deep learning, which take advantage of large data sets, have allowed AI systems to profoundly advance, after they gained momentum in 2012 with a paper specifying a new approach (Krizhevsky et al 2012). By now, a wide range of different AI systems that process images, as well as language, have been implemented to deliver assisting tasks, and reports agree that those systems are projected to impact societies and economies profoundly (Perrault et al 2019;Crawford et al 2019). One of the sectors for which these new skills have been considered early on as exceptionally promising is the healthcare sector (Topol 2019).…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…Through mimicking human strategies first, and then playing against different versions of itself (Silver et al, 2017), the system is able to improve and adapt its strategies autonomously. When challenged by some of the best human players, AlphaGo has repeatedly proved its efficacy in the game (Andras et al, 2018;Curran et al, 2019).…”

Section: Alphago: a Case Studymentioning

confidence: 99%

“…Curran and colleagues conducted a content analysis of how the Chinese and American press approached AlphaGo's games. Beside the predictable cultural differences, they also highlight how it is not unusual to attribute qualities such as 'intuition' and 'creativity' to the system (Curran et al, 2019). Furthermore, they argue, if such qualities "are no longer the sole domain of humans, there is a demand for a reconceptualization first and foremost of what it means to be human" (Curran et al, 2019, p. 733).…”

Section: Alphago: a Case Studymentioning

confidence: 99%

“…Several contextual elements that contribute to triggering the attribution of intentions can be identified. For instance, as illustrated by the example of AlphaGo, a society's cultural background can have repercussions for its perception of artificial agents (Haring et al, 2014;Curran et al, 2019). Even more, attribution of anthropomorphic traits appears to be influenced by whether people perceive artificial agents to be members of the same in-group (e.g., in terms of nationality or gender) rather than of out-groups Kuchenbrandt et al, 2013).…”

Section: Contextual Conditionsmentioning

confidence: 99%

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A Pragmatic Approach to the Intentional Stance Semantic, Empirical and Ethical Considerations for the Design of Artificial Agents

Papagni

Koeszegi

2021

Minds & Machines

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Artificial agents are progressively becoming more present in everyday-life situations and more sophisticated in their interaction affordances. In some specific cases, like Google Duplex, GPT-3 bots or Deep Mind’s AlphaGo Zero, their capabilities reach or exceed human levels. The use contexts of everyday life necessitate making such agents understandable by laypeople. At the same time, displaying human levels of social behavior has kindled the debate over the adoption of Dennett’s ‘intentional stance’. By means of a comparative analysis of the literature on robots and virtual agents, we defend the thesis that approaching these artificial agents ‘as if’ they had intentions and forms of social, goal-oriented rationality is the only way to deal with their complexity on a daily base. Specifically, we claim that this is the only viable strategy for non-expert users to understand, predict and perhaps learn from artificial agents’ behavior in everyday social contexts. Furthermore, we argue that as long as agents are transparent about their design principles and functionality, attributing intentions to their actions is not only essential, but also ethical. Additionally, we propose design guidelines inspired by the debate over the adoption of the intentional stance.

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“…Threat frames signal AI is dangerous ( Baum, 2017 ): actually being or potentially becoming malevolent ( Sun et al, 2020 ) or fostering undesired outcomes (e.g., risking privacy; Ouchchy et al, 2020 ). Some frames emphasize relative humanness, accentuating whether machine agents look or function as do humans: variably (dis)similar to humans in mental and emotional capacities ( Curran et al, 2020 ) or becoming super, true, or real in their intelligence ( Sun et al, 2020 ). Finally, frames characterize AI as the output of human productivity; positive or negative impacts result from human ingenuity or failings.…”

Section: Review Of Literaturementioning

confidence: 99%

Framing Effects on Judgments of Social Robots’ (Im)Moral Behaviors

Banks

Koban

2021

Front. Robot. AI

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Frames—discursive structures that make dimensions of a situation more or less salient—are understood to influence how people understand novel technologies. As technological agents are increasingly integrated into society, it becomes important to discover how native understandings (i.e., individual frames) of social robots are associated with how they are characterized by media, technology developers, and even the agents themselves (i.e., produced frames). Moreover, these individual and produced frames may influence the ways in which people see social robots as legitimate and trustworthy agents—especially in the face of (im)moral behavior. This three-study investigation begins to address this knowledge gap by 1) identifying individually held frames for explaining an android’s (im)moral behavior, and experimentally testing how produced frames prime judgments about an android’s morally ambiguous behavior in 2) mediated representations and 3) face-to-face exposures. Results indicate that people rely on discernible ground rules to explain social robot behaviors; these frames induced only limited effects on responsibility judgments of that robot’s morally ambiguous behavior. Evidence also suggests that technophobia-induced reactance may move people to reject a produced frame in favor of a divergent individual frame.

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Anthropomorphizing AlphaGo: a content analysis of the framing of Google DeepMind’s AlphaGo in the Chinese and American press

Cited by 20 publications

References 19 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

A Pragmatic Approach to the Intentional Stance Semantic, Empirical and Ethical Considerations for the Design of Artificial Agents

Framing Effects on Judgments of Social Robots’ (Im)Moral Behaviors

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