Do You Mind? User Perceptions of Machine Consciousness

Scott, Ava Elizabeth; Neumann, Daniel; Niess, Jasmin; Woźniak, Paweł W.

doi:10.1145/3544548.3581296

Cited by 12 publications

(6 citation statements)

References 106 publications

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“…Research shows that people consistently treat computers as social actors, such as by extending them courtesies such as "please" and "thank you" in conversation [11]. People even perceive some degree of consciousness in ChatGPT [63], which should in turn be associated with moral consideration [28]. We found a few studies suggesting that there are positive effects of AI language capacities on outcomes relevant to moral consideration such as anthropomorphism [20,60] and trust [76].…”

Section: Languagementioning

confidence: 77%

Which Artificial Intelligences Do People Care About Most? A Conjoint Experiment on Moral Consideration

Ladak,

Harris,

Anthis

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Many studies have identified particular features of artificial intelligences (AI), such as their autonomy and emotion expression, that affect the extent to which they are treated as subjects of moral consideration. However, there has not yet been a comparison of the relative importance of features as is necessary to design and understand increasingly capable, multi-faceted AI systems. We conducted an online conjoint experiment in which 1,163 participants evaluated descriptions of AIs that varied on these features. All 11 features increased how morally wrong participants considered it to harm the AIs. The largest effects were from human-like physical bodies and prosociality (i.e., emotion expression, emotion recognition, cooperation, and moral judgment). For human-computer interaction designers, the importance of prosociality suggests that, because AIs are often seen as threatening, the highest levels of moral consideration may only be granted if the AI has positive intentions. CCS CONCEPTS• Human-centered computing → Empirical studies in collaborative and social computing; Empirical studies in HCI.

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Section: Languagementioning

confidence: 77%

Which Artificial Intelligences Do People Care About Most? A Conjoint Experiment on Moral Consideration

Ladak,

Harris,

Anthis

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…The term AI itself is riddled with fallacies, ambiguities [21] and divergent interpretations [17,30]. AI is a diffuse sociotechnical system [32] which is subject to diverse perceptions, such as anthropomorphization [27,37] and conceptual drift [6], which can pose downstream problems for how people reason about these technologies [4,7].…”

Section: Perceptions Of Ai-generated Mediamentioning

confidence: 99%

“…and their evaluation of the systems (such as trust). Scott et al [30] find that people perceive a degree of consciousness in generative AI systems. Epstein et al [7] find that anthropomorphizing the AI system can impact the perceived credit and responsibility to human artists involved in the creation of AI art.…”

Section: Perceptions Of Ai-generated Mediamentioning

confidence: 99%

“…Whichever of these goals a labeling policy decides to pursue, it remains unclear what term(s) would be widely understood by the public if used to label content. People have many different conceptions of what AI is, and how it operates [7,10,17,30]. Furthermore, there is a continuum of different types of content with varying levels of algorithmic intervention, making binary classification difficult.…”

Section: Introductionmentioning

confidence: 99%

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What label should be applied to content produced by generative AI?

Epstein

Arechar²,

Rand

2023

Preprint

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With the rise of generative AI, there has been a recent push for disclosing whether content is produced by AI. However, it is not clear what the right terms are to use for such labels. Even the answer to the question of what counts as AI-generated is not clear, since there exists a continuum of different types of content with varying levels of algorithmic intervention. Furthermore, it is unclear how members of the general public understand various terms used in conjunction with generative AI. In this paper, we therefore investigate how the public understands the mapping between ten potential labeling terms and 15 different types of content that vary in the extent to which they are AI generated, and the extent to which they are potentially misleading. To do so, we conduct a study using N=2038 Americans (quota-sampled to the national distribution on age, gender, ethnicity, and geographic region), as well as replications with samples from Mexico and Brazil (with translated terms, N=825 and 839, respectively; quota-matched to the national distributions on age and gender). Participants are randomly assigned one of the ten terms, and then indicate which pieces of content they would consider that term as applying to. We ask which terms are most effective in terms of classification accuracy (i.e. are applied to content that should be labeled and not applied to content that should not be labeled). We find that different terms satisfy different labeling goals. Across all three countries, ``AI Generated'' is one of the terms most consistently associated by participants with content that is generated using AI, regardless of whether that content is misleading - and thus is not consistently associate with misleading content. Conversely, ``Manipulated'' and ``Not real'' were two of the terms most consistently ascribed to content that is misleading, regardless of whether the misleading content was generated using AI - and thus is not consistently associated with content that was AI generated. Interestingly, ``Artificial'' does fairly well at both classification tasks in the US and Brazil (although less so in Mexico). Finally, while labels using any of the ten terms evoked at least some amount of negative feeling towards the content and the poster among US participants, numerous terms (including ``AI generated'' and ``Artificial'') had positive associations on average for participants in Brazil and Mexico. These results have important implications for how and where generative AI disclosure is implemented, and suggest that platforms and civil society must decide carefully what their objective is for such disclosures.

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“…There is something special about words. The recently developed Large Language Models (LLMs), which are exceptionally proficient at writing and reading, are also claimed to be “a spark of Artificial General Intelligence …” (Bubeck et al, 2023 ), “self-conscious” (Scott et al, 2023 ), and even “an existential threat to humanity.” 1 At the same time, or rather for several decades, we have had computers that add and multiply numbers several billion times faster than any human. Doing math is also very “General,” but these computers have never been seen as particularly intelligent, at least not compared to us humans.…”

Section: Introductionmentioning

confidence: 99%

AI and its consequences for the written word

Hellström

2024

Front. Artif. Intell.

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The latest developments of chatbots driven by Large Language Models (LLMs), more specifically ChatGPT, have shaken the foundations of how text is created, and may drastically reduce and change the need, ability, and valuation of human writing. Furthermore, our trust in the written word is likely to decrease, as an increasing proportion of all written text will be AI-generated – and potentially incorrect. In this essay, I discuss these implications and possible scenarios for us humans, and for AI itself.

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Do You Mind? User Perceptions of Machine Consciousness

Cited by 12 publications

References 106 publications

Which Artificial Intelligences Do People Care About Most? A Conjoint Experiment on Moral Consideration

Which Artificial Intelligences Do People Care About Most? A Conjoint Experiment on Moral Consideration

What label should be applied to content produced by generative AI?

AI and its consequences for the written word

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