In an age where we increasingly converse with artifacts – whose interfaces include AI, a pressing question emerges: Who, or what, are we really talking with? As AI-based chatbots, often termed \u2018conversational Ais\u2019, become more sophisticated, the line between human and machine communication blurs, raising profound questions about the nature of knowledge, trust, responsibility, and values.<\/p>\n
The roots of conversation with inanimate objects stretches way back. In 1637, Ren\u00e9 Descartes declared:<\/p>\n
We can certainly conceive of a machine so constructed that it utters words, and even utters words which correspond to bodily actions causing a change in its organs (e.g., if you touch it in one spot it asks what you want of it, if you touch it in another spot it cries out that you are hurting it, and so on). But it is not conceivable that such a machine should produce different arrangements of words so as to give an appropriately meaningful answer to whatever is said in its presence, as the dullest of men can do[1]<\/a>.<\/p>\n Fast forward to the 1950s, and Alan Turing proposed his famous test, challenging us to distinguish between human and machine responses. ELIZA in the 1960s, mimicked a psychotherapist, and merely rephrased user inputs as questions. One of the insights resulting from observing people chat with ELIZA is the \u201cELIZA effect\u201d \u2013 attributing human-like characteristics to computer products. Another is the ability of designers to \u201celicit trust\u201d[2]<\/a> \u2013 using this effect to ‘trick’ users into trusting the system. Another example is the 1990s MS-DOS program Dr. Sbaitso – a digital \u201cpsychologist\u201d that offered predetermined responses based on keywords. Early attempts at conversations with machines were extremely simplistic by today’s standards.<\/p>\n Moving to November 2022 to the launching of the chatbot ChatGPT \u2013 with its array of technical new jargon such as \u2018Large Language Models\u2019 (LLMs) and \u2018Generative AI\u2019. ChatGPT began as a product that engages in human-like conversation. It is a type of a LLM \u2013 an AI trained on vast datasets of text (like Wikipedia and newspapers) to \u201cunderstand\u201d (i.e. statistically infer the relations between words) and generate outputs. This technology is beyond chatting, introducing a broader category of technologies that generates (hence \u2018Generative AI\u2019) new content beyond text – such as images, videos, voices, music, but also drug discovery and more \u2013 all based on learned patterns from existing data.<\/p>\n While the collaboration of humans and AIs benefits scholar work, creativity, and art, we do leave something behind. Consider the use of AI in writing, for example: While AI might offer efficiency gains and improve quality, and even level the playing field for authors whose English is second (or third) language, in some cases it ultimately risks undermining the intellectual engagement and pleasure that comes with writing. The act of writing – choosing words, carefully crafting sentences, structuring arguments – is a fundamental practice for deep thinking. By outsourcing this process to an AI-based product, we lose the invaluable cognitive exercise of working with the building blocks of thoughts \u2013 words – and the pleasure and insights that often comes when we do it well.<\/p>\n While we lose some cognitive benefits in our personal work, the advancing capabilities of AIs are reshaping society. By now, the generated text has a human-like voice, and soon perhaps a human-like body, rendering machine outputs as indistinguishable from human outputs. While there are arguably many positive applications of \u2018humanizing\u2019 products, such as enhancing user experience in customer service or providing companionship in elderly care, I leave the advocacy of such benefits to others. I have experienced an AI \u201ceye-rolling\u201d at my jokes, and vowed to be more critical ever since.<\/p>\n Navigating the landscape of increasingly human-like AI interactions, necessitates understanding the fundamental limitations and differences[3]<\/a>. Despite AI-based products\u2019 impressive outputs, these AIs don’t “know” or “understand” in the way humans do. They are pattern-matching statistical machines, extraordinarily good at predicting what words, pitch, or pixels should come next. These products are not sentient, do not have minds, and their lingual outputs are not the expression of thoughts or grounded in intent. These products are not ethical reasoners[4]<\/a>.<\/p>\n As we interact more with artifacts in natural language, we face the “anthropomorphic trap” – the tendency to attribute human-like qualities to these products. This can lead to misplaced trust or emotional attachment, raising a whole new set of philosophical question, such as what is the nature of love between a human and a device? Can you be cruel to a machine?<\/p>\n In philosophical circles, I suggested framing this issue as the debate between “testimony-based beliefs” – knowledge from human sources – and “technology-based beliefs”, i.e. knowledge from AI sources[5]<\/a>. This distinction underscores the need for a nuanced understanding of the digital source of information.<\/p>\n Guided by this philosophical framing, it is possible to inform policy-makers and propose several key principles for regulating one-on-one human-machine interactions. For example:<\/p>\n Ultimately, the philosophical question of who, or what, we are really talking with, isn’t merely about AI products but about ourselves. It challenges us to reflect on how we listen and interpret digital information.<\/p>\n As we integrate chatbots and automated decision-makers into our lives, we should ask more about the value of the human interaction we’re losing, consider the broader implications, and identify the relevant philosophical questions.<\/p>\n Asking philosophical questions could guide us towards a more self-reflective adoption and implementation of technologies, as our relationship with technology, the nature of knowledge, and communication, changes for the generations to come.<\/p>\n Picture created by Dell-E 3 in response to prompt “A realistic black and white photograph of a person standing in front of a full-length mirror speaking with a robot reflection”<\/p>\n <\/p>\n [1]<\/a> Descartes, R. (1985) [1637]. \u201cDiscourse on the Method.\u201d, p. 140, in: The Philosophical Works of Descartes, vol. I. (J. Cottingham, R. Stoothoff, and D. Murdoch, Translators). Cambridge University Press.<\/p>\n [2]<\/a> Turkle, S. (2011). \u201cAuthenticity in the age of digital companions\u201d, p. 63, in: Machine Ethics<\/em>. M. Anderson & S. Anderson (eds.). Cambridge University Press. doi:10.1017\/CBO9780511978036.008<\/p>\n [3]<\/a> Freiman, O., and Miller, B. (2020). \u201cCan Artificial Entities Assert?\u201d, pp. 415-436, in: S. C. Goldberg (ed.) The Oxford Handbook of Assertion<\/em>. Oxford University Press. https:\/\/academic.oup.com\/edited-volume\/34275\/chapter-abstract\/290604123<\/a><\/p>\n [4]<\/a> Freiman, O. (2024). \u201cAI-Testimony, Conversational AIs and Our Anthropocentric Theory of Testimony\u201d, Social Epistemology <\/em>38(4): 476\u2013490. https:\/\/doi.org\/10.1080\/02691728.2024.2316622<\/a><\/p>\n [5]<\/a> Freiman, O. (2023). Analysis of Beliefs Acquired from a Conversational AI: Instruments-based Beliefs, Testimony-based Beliefs, and Technology-based Beliefs. Episteme<\/em>. doi:10.1017\/epi.2023.12<\/p>\n [6]<\/a> Nowak, En. (2024). AI voices should sound weird. https:\/\/blogs.cardiff.ac.uk\/openfordebate\/2075-2\/<\/a><\/p>\n [7]<\/a>Freiman, O. and Geslevich Packin N. (2023). Automation’s Hidden Costs: The Case Against a Paywalled Human Touch. Forbes<\/em>, May 22, 2023. https:\/\/www.forbes.com\/sites\/nizangpackin\/2023\/05\/22\/automations-hidden-costs-the-case-against-a-paywalled-human-touch<\/a><\/p>\n [8]<\/a> Melnick K. (2024). Air Canada chatbot promised a discount. Now the airline has to pay it. The Washington Post, <\/em>February 18, 2024. https:\/\/www.washingtonpost.com\/travel\/2024\/02\/18\/air-canada-airline-chatbot-ruling<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"In an age where we increasingly converse with artifacts - whose interfaces include AI, a pressing question emerges: Who, or what, are we really talking with? As AI-based chatbots, often […]","protected":false},"author":1319,"featured_media":2165,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"Ori Freiman \"Philosophical Foundations for Chatbot Regulation\" on Open for Debate","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[19],"tags":[379,473,160,474,363,475],"custom_author":[476],"class_list":["post-2161","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-open-for-debate","tag-ai","tag-chatbots","tag-ethics","tag-human-computer-interaction","tag-law","tag-policy","custom_author-freiman-ori"],"jetpack_publicize_connections":[],"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p8KPlM-yR","jetpack-related-posts":[{"id":1870,"url":"https:\/\/blogs.cardiff.ac.uk\/openfordebate\/can-machines-manipulate-us\/","url_meta":{"origin":2161,"position":0},"title":"Can Machines Manipulate Us?","author":"Alessandra Tanesini","date":"January 22, 2024","format":false,"excerpt":"YouTube, Facebook, X (formerly Twitter), Instagram, Pinterest, TikTok, and chatbots like ChatGPT and Bard\u2014all of them are manipulating you. More specifically, their algorithms are manipulating you. Or so we now commonly hear from pundits, activists, and academics. But just what would it mean for a machine, at least one short\u2026","rel":"","context":"In "Open for Debate"","block_context":{"text":"Open for Debate","link":"https:\/\/blogs.cardiff.ac.uk\/openfordebate\/category\/open-for-debate\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/blogs.cardiff.ac.uk\/openfordebate\/wp-content\/uploads\/sites\/533\/2024\/01\/falling-7372662_1280.jpg?resize=350%2C200&ssl=1","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/blogs.cardiff.ac.uk\/openfordebate\/wp-content\/uploads\/sites\/533\/2024\/01\/falling-7372662_1280.jpg?resize=350%2C200&ssl=1 1x, https:\/\/i0.wp.com\/blogs.cardiff.ac.uk\/openfordebate\/wp-content\/uploads\/sites\/533\/2024\/01\/falling-7372662_1280.jpg?resize=525%2C300&ssl=1 1.5x, https:\/\/i0.wp.com\/blogs.cardiff.ac.uk\/openfordebate\/wp-content\/uploads\/sites\/533\/2024\/01\/falling-7372662_1280.jpg?resize=700%2C400&ssl=1 2x, https:\/\/i0.wp.com\/blogs.cardiff.ac.uk\/openfordebate\/wp-content\/uploads\/sites\/533\/2024\/01\/falling-7372662_1280.jpg?resize=1050%2C600&ssl=1 3x"},"classes":[]},{"id":2075,"url":"https:\/\/blogs.cardiff.ac.uk\/openfordebate\/2075-2\/","url_meta":{"origin":2161,"position":1},"title":"AI voices should sound weird","author":"Alessandra Tanesini","date":"August 19, 2024","format":false,"excerpt":"There has been a lot of discussion recently \u2013 both among academics and in the popular press \u2013\u00a0 about what kinds of limits should be placed on content generated by AI. 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Re-evaluating the Kasparov reply","author":"Alessandra Tanesini","date":"March 6, 2023","format":false,"excerpt":"In 1997, Garry Kasparov became the first world chess champion to lose a match to a computer, IBM\u2019s Deep Blue. Kasparov initially thought the IBM team cheated after the computer played what GM Yasser Seirawan described as a \u2018human like move\u2019 that \u2018sent Garry into a tizzy\u2019. 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