Tag: Artificial intelligence

The Dark Side of AI: A Human and Societal Perspective

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Author: Shafik, Wasswa Description: This book addresses how the growth of AI could undermine fundamental human values such as privacy, security, employment, and democracy. It emphasizes the critical challenges that arise when artificial intelligence technologies operate without proper ethical oversight or regulation. Key topics include AI’s potential for surveillance, the manipulation of media through deepfakes, autonomous weapons, and job displacement, as well as the growing concerns around algorithmic biases that may perpetuate social inequalities and harm marginalized communities. It offers insights into how AI can be integrated into society responsibly,…

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Is artificial intelligence a good servant but a bad master?

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Author: Lakhtakia, Akhlesh; Lakhtakia, Natalya S. Description: Artificial intelligence (AI) is rapidly moving from a specialized research topic into a widely deployed general-purpose technology, driven by advances in transformer-based architectures, convincing enough productivity gains, large private investment, geopolitical competition, and expanding educational adoption. AI comprises two distinct sets of methodologies: (i) predictive AI, which is grounded in statistical inference and aims to model measurable physical reality, and (ii) generative AI, which produces plausible synthetic content without intrinsic verification against physical reality. Whereas predictive AI can strengthen human decision-making through rigorously…

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Large language models (LLMs) and the institutionalization of misinformation

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Author: Garry, M; Chan, WM; Foster, J; Henkel, LA Description: Large language models (LLMs), such as ChatGPT, flood the Internet with true and false information, crafted and delivered with techniques that psychological science suggests will encourage people to think that information is true. What’s more, as people feed this misinformation back into the Internet, emerging LLMs will adopt it and feed it back in other models. Such a scenario means we could lose access to information that helps us tell what is real from unreal – to do ‘reality monitoring.’…

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Fooled twice: People cannot detect deepfakes but think they can

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Author: Köbis, NC; Doležalová, B; Soraperra, I Description: Hyper-realistic manipulations of audio-visual content, i.e., deepfakes, present new challenges for establishing the veracity of online content. Research on the human impact of deepfakes remains sparse. In a pre-registered behavioral experiment (N = 210), we show that (1) people cannot reliably detect deepfakes and (2) neither raising awareness nor introducing financial incentives improves their detection accuracy. Zeroing in on the underlying cognitive processes, we find that (3) people are biased toward mistaking deepfakes as authentic videos (rather than vice versa) and (4) they…

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Winners and losers of generative AI: Early Evidence of Shifts in Freelancer Demand

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Author: Teutloff, Ole; Einsiedler, Johanna; Kassi, Otto; Braesemann, Fabian; Mishkin, Pamela; del Rio-Chanona, R. Maria Description: We examine how ChatGPT has changed the demand for freelancers in jobs where generative AI tools can act as substitutes or complements to human labor. Using BERTopic we partition job postings from a leading online freelancing platform into 116 fine-grained skill clusters and with GPT-4o we classify them as substitutable, complementary or unaffected by LLMs. Our analysis reveals that labor demand increased after the launch of ChatGPT, but only in skill clusters that were…

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Why Hybrid Intelligence Is the Future of Human-AI Collaboration

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Author: Walther, Cornelia C. Description: Hybrid intelligence combines the computational strengths of artificial intelligence with the holistic comprehension of natural intelligence, leading to more sustainable, creative, and trustworthy results. Natural intelligence offers a holistic understanding of human complexity and the consequences of AI on society. Organizations can curate hybrid intelligence by investing in algorithmic fluency and humanistic insights. Subject headings: Hybrid intelligence; Artificial intelligence; Natural intelligence Publication year: 2025 Journal or book title: Knowledge at Wharton Find the full text: https://knowledge.wharton.upenn.edu/article/why-hybrid-intelligence-is-the-future-of-human-ai-collaboration/ Find more like this one (cited by): https://scholar.google.com/scholar?cites=8072983520551362401&as_sdt=1000005&sciodt=0,16&hl=en Serial…

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The Psychology of Authenticity

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Author: Newman, George E. Description: Perceptions of authenticity (or, inauthenticity) have been shown to affect people’s judgments and behavior across a wide variety of domains. However, there is still ambiguity about how the concept should be defined. This is attributable, at least in part, to a growing list of different “kinds of authenticity” with little discussion of the potential overlaps between them. The goal of this paper is to reduce these various notions of authenticity into a more manageable set of constructs. Building on the work of Newman and Smith,…

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Learning the Wrong Lessons: Syntactic-Domain Spurious Correlations in Language Models

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Author: Shaib, Chantal; Suriyakumar, Vinith M.; Sagun, Levent; Wallace, Byron C.; Ghassemi, Marzyeh Description: For an LLM to correctly respond to an instruction it must understand both the semantics and the domain (i.e., subject area) of a given task-instruction pair. However, syntax can also convey implicit information. Recent work shows that syntactic templates — frequent sequences of Part-of-Speech (PoS) tags — are prevalent in training data and often appear in model outputs. In this work we characterize syntactic templates, domain, and semantics in task-instruction pairs. We identify cases of spurious…

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The Medium is the Message: How Non-Clinical Information Shapes Clinical Decisions in LLMs

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Author: Gourabathina, Abinitha; Gerych, Walter; Pan, Eileen; Ghassemi, Marzyeh Description: The integration of large language models (LLMs) into clinical diagnostics necessitates a careful understanding of how clinically irrelevant aspects of user inputs directly influence generated treatment recommendations and, consequently, clinical outcomes for end-users. Building on prior research that examines the impact of demographic attributes on clinical LLM reasoning, this study explores how non-clinically relevant attributes shape clinical decision-making by LLMs. Through the perturbation of patient messages, we evaluate whether LLM behavior remains consistent, accurate, and unbiased when non-clinical information is…

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Language models cannot reliably distinguish belief from knowledge and fact

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Author: Suzgun, Mirac; Gur, Tayfun; Bianchi, Federico; Ho, Daniel E.; Icard, Thomas; Jurafsky, Dan; Zou, James Description: As language models (LMs) increasingly infiltrate into high-stakes domains such as law, medicine, journalism and science, their ability to distinguish belief from knowledge, and fact from fiction, becomes imperative. Failure to make such distinctions can mislead diagnoses, distort judicial judgments and amplify misinformation. Here we evaluate 24 cutting-edge LMs using a new KaBLE benchmark of 13,000 questions across 13 epistemic tasks. Our findings reveal crucial limitations. In particular, all models tested systematically fail…

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