AI deception: A survey of examples, risks, and potential solutions

Patterns, Год журнала: 2024, Номер 5(5), С. 100988 - 100988

Опубликована: Май 1, 2024

This paper argues that a range of current AI systems have learned how to deceive humans. We define deception as the systematic inducement false beliefs in pursuit some outcome other than truth. first survey empirical examples deception, discussing both special-use (including Meta's CICERO) and general-purpose large language models). Next, we detail several risks from such fraud, election tampering, losing control AI. Finally, outline potential solutions: first, regulatory frameworks should subject are capable robust risk-assessment requirements; second, policymakers implement bot-or-not laws; finally, prioritize funding relevant research, including tools detect make less deceptive. Policymakers, researchers, broader public work proactively prevent destabilizing shared foundations our society.

Язык: Английский

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ChatGPT or Bard: Who is a better Certified Ethical Hacker?

Computers & Security, Год журнала: 2024, Номер 140, С. 103804 - 103804

Опубликована: Март 9, 2024

In this study, we compare two leading Generative AI (GAI) tools, ChatGPT and Bard, specifically in Cybersecurity, using a robust set of standardized questions from validated Certified Ethical Hacking (CEH) dataset. the rapidly evolving domain large language models (LLM), comparative analysis tools becomes essential to measure their performance. We determine Comprehensiveness, Clarity, Conciseness AI-generated responses through detailed questioning-based framework. The study revealed an overall accuracy rate 80.8% for 82.6% indicating comparable capabilities specific differences. Bard slightly outperformed accuracy, while exhibited superiority responses. Introducing confirmation query like "Are you sure?" increased both generative illustrating potential iterative processing enhancing GAI tools' effectiveness. readability evaluation placed at college reading level, with marginally more accessible. While evaluating certain questions, distinct pattern emerged where provided generic denials assistance referenced "ethics." This discrepancy illustrates contrasting philosophies developers these possibly following stricter guidelines, especially sensitive topics Cybersecurity. explore implications identify key areas future research that become increasingly relevant as see broader adoption.

Язык: Английский

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AI wellbeing

Asian Journal of Philosophy, Год журнала: 2025, Номер 4(1)

Опубликована: Фев. 1, 2025

Язык: Английский

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Can AI Rely on the Systematicity of Truth? The Challenge of Modelling Normative Domains

Philosophy & Technology, Год журнала: 2025, Номер 38(1)

Опубликована: Март 1, 2025

Язык: Английский

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Why we need to be careful with LLMs in medicine

Frontiers in Medicine, Год журнала: 2024, Номер 11

Опубликована: Дек. 4, 2024

Large language models (LLMs), the core of many generative AI (genAI) tools, are gaining attention for their potential applications in healthcare. These wide-ranging, including tasks such as assisting with diagnostic processes, streamlining patient communication, and providing decision support to healthcare professionals. Their ability process generate large volumes text makes them promising tools managing medical documentation enhancing efficiency clinical workflows (Harrer, 2023). LLMs offer a distinct advantage that they relatively straightforward use, particularly since introduction ChatGPT-3.5, exhibit notable alignment human communication patterns, facilitating more natural interactions (Ayers et al., 2023) acceptance LLMs' conclusions (Shekar 2024). operate by predicting next word sequence based on statistical correlations identified datasets (Patil 2021;Schubert However, while these effective at producing appears coherent contextually appropriate, do so without genuine understanding meaning or context. This limitation is significant healthcare, where accuracy critical. Unlike cognition, which driven complex array goals behaviors, narrowly focused generation. focus can lead production plausible sounding but inaccurate information, phenomenon referred "AI hallucination" (OpenAI In high-stakes environments like prediction, triaging, diagnosis, monitoring, care, inaccuracies have serious consequences.While numerous articles across various Frontiers journals discuss LLMs, few hallucinations central issue. For example, Jin al. (2023) Medicine note "While ChatGPT tremendous ophthalmology, addressing challenges hallucination misinformation paramount." Similarly, Giorgino Surgery emphasize "The responsible use this tool must be an awareness its limitations biases. Foremost among dangerous concept hallucination." Beyond realm Williams (2024) Education observes gained widespread around 2022, coinciding rise ChatGPT. Users noticed chatbots often generated random falsehoods responses, seemingly indifferent relevance accuracy." continues stressing "term has been criticized anthropomorphic connotations, it likens perception behavior models." Despite critical discussions, remain sparse compared praising medicine, highlighting need greater engagement technologies. imbalance highlights emphasis mitigating risks posed models. Building concern, Hicks, Humphries, Slater challenge conventional thinking paper "ChatGPT Bullshit." They assert produced should not simply labeled "hallucinations," "bullshit," term philosopher Harry Frankfurt's (2009) work. According perspective, "bullshit" reflects disregard accuracy, poses genAI By reconceptualizing "bullshiting" instead "hallucinating," aims provide perspective pose applications. It explores practical solutions layered LLM architectures improved XAI methods, emphasizes urgency implementing tailored oversight mechanisms counterbalance political industry push deregulation sensitive domains medicine.LLMs datasets. While produce human-like text, don't inherently understand verify acting "prop-oriented make-believe tools" (Mallory, errors result technical glitches resolved better data refined algorithms stem from fundamental nature-they evaluate evidence reason sense. distinction between processing reasoning misconceptions, when portrayed perceived capable cognition. accurate relevant outputs correlations, comprehension. As Bender (2021) famously argued, sequences learned function "stochastic parrots." contrast, involves deeper cognitive processes understanding, thinking, interpretation. some, Downes (2024), view, suggesting sensible answers leveraging higher-level structural information inherent design, fact remains fundamentally agnostic empirical reality. Recognizing crucial, predictions made models-no matter how convincing-should equated deliberate, evidence-based mind. When systems make mistakes, because malfunctioning way fixed tweaked algorithms. arbitrate first place. Hicks point out: trying communicate something believe perceive. inaccuracy due misperception hallucination. we pointed out, convey all. bullshitting." indifference especially concerning interpretability, liability paramount. Consider implications using advice assist diagnosing patients-if nature misunderstood, risks. Trusting potentially flawed could misdiagnoses improper treatments, consequences care. stated Harrer (2023): "Health buyers beware: experimental technology yet ready primetime."Recognizing rather than "hallucinations" calls cautious skeptical approach, according colleagues. Titus convincingly "Attributing semantic warranted doing social ethical related anthropormorphizing (sic) over-trusting meaningful truthful responses." health sector, implies that, mMedical professionals wary about avoid standalone sources (Cohen, Instead, serve supplementary all rigorously validated experts before being applied used any setting. The medicine significant. If truth, there heightened responsibility developers users ensure cause harm. only improving also clearly communicating users. al note, "Calling chatbot 'hallucinations' feeds into overblown hype abilities cheerleaders, unnecessary consternation general public. suggests problems might work, misguided efforts amongst specialists." Given expert validation both design prior (Bélisle-Pipon 2021;Cohen, 2023).Ensuring trustworthiness requires shared responsibility, creating transparent critically assessing (Amann 2020;Díaz-Rodríguez 2023;Siala & Wang, 2022;Smith, 2021). Medical trained AI-generated content may sound convincing, always reliable. Developers prioritize interfaces highlight encourage evaluation outputs. disclaimers confidence scores help assess reliability provided (Gallifant basically what Notice Explanation section White House's Bill Rights (2022) requires: "Medical source advice. tool, setting." disclosure enough itself conducive problems, shifting burden onto Such accessible understandable does reproduce consumer products' Terms Conditions, ridiculously long nobody reads (Solove, 2024).Employing multiple layers mitigate individual solve previously raised issues. Work currently underway area (Farquhar Usually entails enabling one model cross-validate another identify correct inaccuracies, thereby reducing incidence wherein different assigned specialized factchecking contextual validation, enhance robustness (Springer, methodology introduces complexity, risk error propagation associated coordination Furthermore, strategy, Verspoor "fighting fire fire," incrementally improve outputs, fails address foundational issue lack true understanding. An over-reliance diminishing returns, added complexity novel negate anticipated benefits enhanced accuracy. Additionally, approach fostering overdependence (Levinstein Herrmann, 2024), undermining role expertise requiring nuanced decision-making.LLMs still valuable contributions practice if wisely. administrative tasks, documentation, preliminary topics. even useful defending patients' interests insurance claims (Rosenbluth, designed safeguards prevent One utility rely solely them, implement verification reliable databases (not just web-scrapping). Even concerns "bullshit." connecting trusted database provides cross-referenced sources. system would incorporate mechanism arbitrating evidence, further certain level trustworthiness. integration implemented carefully introducing new forms inadvertently embedding values inconsistent context deployed 2021).Explainable (XAI) increase transparency decision-making, LLMs. Techniques post-hoc explanations generates fields limitation: depend (Titus, Moreover, techniques tracing back underlying fail expose epistemic inability evidence. explanations, therefore, reflect patterns Regulatory frameworks, European Union's Regulation ( 2024) US Blueprint (The House, 2022), establish standards transparency, safety, accountability. adapting meet overcome decision-making. Experts argue refining developing paradigms, neurosymbolic AI, combines neural networks logical gaps.Neurosymbolic offers alternative, integrating adaptability precision enable robust (Hamilton 2024;Wan key offering interpretability. Vivek Wadhwa suggests, nearing developmental ceiling, investment returns. regulators investors explore advancing drive generation innovation, ensuring increased trustworthy reasoning. promise, panacea. faces scalability, handling real-world (Marra reliance structures fully capture nuances probabilistic ambiguous common medicine. Thus, represents incremental advance, oversight, multidisciplinary collaboration, continued innovation essential AI's healthcare.A deep, examination crucial ways safety integrity. fluent, proficiency conceals troubling reality: responses necessarily grounded verified facts consistent logic. field, decision-making paramount, relying flaws presents core, predict training data. mechanism, though powerful generating truth. goal most statistically likely response appropriate one, infiltrating workflows.As underscore, "Responsible implementation continuous monitoring harness minimizing risks." A concern reproducibility. traditional software systems, identical inputs yield same question occasions. unpredictability undermines needed settings, consistency delivering safe Medicine, discipline, cannot afford embrace "epistemic insouciance"-a validity knowledge. problematic given cases, anchored factual reality merely sounds plausible. "hallucination" describe factually incorrect statements trivializes severity problem: medicine-an 1990s-this flaw adoption unreliable compromise integrity care.The standard ChatGPT, warn mistakes. Check important info," insufficient settings. points out "In defence OpenAI, never advertised advisor crowdsourced refinement experiment"; acknowledged mitigation genAI, sparked growing caution amid internet-level hype. sector significant, (especially Hhealthcare professionals) time every piece high-pressure stake margin slim, Entrusting fact-checking giving resources assurances exposes field well arguably ethics dumping, offload downstream Victor, casual use-particularly life-threatening consequences-reflects complacency. Transparency, luxury necessity. Healthcare recommends why arrived conclusions. Explainability building trust informed decisions output. Without "black boxes," accountability justification-an untenable situation decision-making.The amplified current climate, United States. incoming Trump administration expected removal "unnecessary" regulations accelerate (Chalfant, lobbying influential tech organizations BSA | Software Alliance -which companies OpenAI Microsoft-advocate policies reduce regulatory constraints promote adoption. group acknowledges importance international governance standards, removing barriers deprioritizing (such government-imposed mechanisms). President-elect Trump's plans undo previous administration-including management framework foster accountability-signal shift toward (Verma Vynck, perhaps regulation winter. move weaken deploying highstakes healthcare.Given context, systems. Developers, policymakers, institutions collaborate uphold deployment, regardless environment. efforts, exacerbate tendency misleading Trustworthy treated secondary consideration, outcomes lives directly stake.Reframing seen harmless recognizing terminology-it reframing small, occasional mistakes operate. Policymakers, providers, recognize stakes high, rigorous safeguards, erode quality

Язык: Английский

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Identifying the Source of Generation for Large Language Models

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 91 - 105

Опубликована: Янв. 1, 2025

Язык: Английский

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Are current AI systems capable of well-being?

Asian Journal of Philosophy, Год журнала: 2025, Номер 4(1)

Опубликована: Март 15, 2025

Язык: Английский

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Cut the crap: a critical response to “ChatGPT is bullshit”

Ethics and Information Technology, Год журнала: 2025, Номер 27(2)

Опубликована: Апрель 18, 2025

Язык: Английский

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Future Shock: Generative AI and the International AI Policy and Governance Crisis

Harvard data science review, Год журнала: 2024, Номер Special Issue 5

Опубликована: Май 31, 2024

Crisis 5 stakeholders from across industry, academia, government, and civil society, around the globe, had made concerted efforts to develop standards, policies, governance mechanisms ensure ethical, responsible, equitable production use of AI systems.However, as we then show, despite these ostensibly supportive activities background conditions, several primary drivers future shock converged produce an international policy crisis in wake dawning GenAI era.Such a crisis, argue, was marked by disconnect between strengthening thrust public concerns about hazards posed hasty industrial scaling absence effectual regulatory needed interventions address such hazards.In painting broad-stroked picture this underscore two sets contributing factors.First, there have been factors that demonstrated various vital aspects capability execution-and thus key preconditions for readiness resilience managing technological transformation.These include prevalent enforcement gaps existing digital-and data-related laws (e.g., intellectual property data protection statutes), lack capacity, democratic deficits standards trustworthy AI, widespread evasionary tactics ethic washing state-enabled deregulation.Second, significantly contributed presence new scale order systemic-, societal-, biospheric-level risks harms.Chief among were closely connected dynamics unprecedented centralization emerged both by-products revolution.We focus, particular, on model scaling.Whereas data, size, compute linked emergence serious intrinsic deriving unfathomability training opacity complexity, emergent capabilities, exponentially expanding costs, rapid industrialization FMs systems meant onset systemic spanned social, political, economic, cultural, natural ecosystems which embedded.The brute-force commercialization ushered age exposure increasing numbers impacted people communities at large susceptible harms issuing possibilities misuse, abuse, cascading system-level effects.Alongside scaling, patterns economic geopolitical only further intensified conditions shock.The steering momentum lay largely hands few tech corporations, essentially controlled compute, skills knowledge infrastructures required systems.This small number corporate actors labs disproportionate influence direction pace revolution, pursuing market-oriented values led acceleration.This also that, if left unchecked, concentration techno-scientific market power could lead consolidation.Moreover, impetus industry consolidation

Язык: Английский

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Whisper for L2 speech scoring

International Journal of Speech Technology, Год журнала: 2024, Номер 27(4), С. 923 - 934

Опубликована: Ноя. 1, 2024

Язык: Английский

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