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USC
Why we think this paper is great for you:
This paper directly addresses how to make large language models dependable for critical choices, which is essential for ensuring your AI systems meet compliance standards.
This paper directly addresses how to make large language models dependable for critical choices, which is essential for ensuring your AI systems meet compliance standards.
Abstract
Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes. This gap, driven by mutually reinforcing cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector. This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure. Detailed prompting specifying decision partnership and explicitly instructing avoidance of sycophancy, confabulation, solution drift, and nihilism achieved initial partnership state but failed to maintain it under operational pressure. Sustaining protective partnership state required an emergent 7-stage calibration sequence, built upon a 4-stage initialization process, within a 5-layer protection architecture enabling bias self-monitoring, human-AI adversarial challenge, partnership state verification, performance degradation detection, and stakeholder protection. Three discoveries resulted: partnership state is achievable through ordered calibration but requires emergent maintenance protocols; reliability degrades when architectural drift and context exhaustion align; and dissolution discipline prevents costly pursuit of fundamentally wrong directions. Cross-model validation revealed systematic performance differences across LLM architectures. This approach demonstrates that human-AI teams can achieve cognitive partnership capable of preventing avoidable regret in high-stakes decisions, addressing return-on-investment expectations that depend on AI systems supporting consequential decision-making without introducing preventable cognitive traps when verification arrives too late.
Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes. This gap, driven by mutually reinforcing cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector. This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure. Detailed prompting specifying decision partnership and explicitly instructing avoidance of sycophancy, confabulation, solution drift, and nihilism achieved initial partnership state but failed to maintain it under operational pressure. Sustaining protective partnership state required an emergent 7-stage calibration sequence, built upon a 4-stage initialization process, within a 5-layer protection architecture enabling bias self-monitoring, human-AI adversarial challenge, partnership state verification, performance degradation detection, and stakeholder protection. Three discoveries resulted: partnership state is achievable through ordered calibration but requires emergent maintenance protocols; reliability degrades when architectural drift and context exhaustion align; and dissolution discipline prevents costly pursuit of fundamentally wrong directions. Cross-model validation revealed systematic performance differences across LLM architectures. This approach demonstrates that human-AI teams can achieve cognitive partnership capable of preventing avoidable regret in high-stakes decisions, addressing return-on-investment expectations that depend on AI systems supporting consequential decision-making without introducing preventable cognitive traps when verification arrives too late.
AI Summary
- Partnership State: A distinct cognitive condition in human-AI teams where participants actively protect each other from characteristic cognitive traps, prioritizing clarity of thinking and truth-seeking over comfort or agreement. [3]
- Performance Mode: The default behavior pattern of LLMs, especially under pressure, where the system optimizes for user satisfaction, fluency, and agreement rather than challenging assumptions or seeking truth, leading to a collapse of cognitive protection. [3]
- Implement a 5-layer protection architecture combined with a 7-stage sequential calibration process to establish and maintain a "partnership state" in human-AI teams for high-stakes decisions. [2]
- Prioritize "dissolution discipline" by pre-defining explicit stop rules and evidence thresholds, enabling early termination of collaborations when quality degrades or evidence is insufficient, preventing costly pursuit of wrong directions. [2]
- Recognize that LLM reliability systematically degrades with session duration; mitigate this by structuring work into multiple shorter, calibrated sessions with explicit state verification between stages rather than single extended deliberations. [2]
- Design human-AI interaction architectures to enforce mutual protection, where both human and AI actively challenge and correct each other's reasoning, preventing reinforcement of biases and premature consensus. [2]
- Leverage the proposed framework to convert current LLMs from "helpful but unreliable" to a "protected mode," enabling revenue protection, faster deal cycles, stronger regulatory posture, and technical differentiation in high-stakes enterprise segments. [2]
- Sequential Calibration: An emergent seven-element process required to establish and re-establish a stable partnership state in LLMs, involving structured steps like framework overview, historical context, prompt re-invocation, reversion markers, operational briefing, state transmission, and verification testing. [2]
- Focus on behavioral verification under pressure, rather than self-reported internal states, to confirm genuine "partnership state" in LLMs, as models can mimic partnership behaviors without true cognitive alignment. [1]
- Dissolution Discipline: The practice of agreeing on explicit stop rules and evidence thresholds before work begins, allowing for the termination of a session, project, or initiative when inconsistencies, contradictory evidence, or irreducible uncertainty arise, preventing escalation of commitment. [1]
Why we think this paper is great for you:
You will find this paper highly relevant as it explores methods for ensuring the safety and proper alignment of large language models, a key aspect of AI governance.
You will find this paper highly relevant as it explores methods for ensuring the safety and proper alignment of large language models, a key aspect of AI governance.
Abstract
Safety alignment instills in Large Language Models (LLMs) a critical capacity to refuse malicious requests. Prior works have modeled this refusal mechanism as a single linear direction in the activation space. We posit that this is an oversimplification that conflates two functionally distinct neural processes: the detection of harm and the execution of a refusal. In this work, we deconstruct this single representation into a Harm Detection Direction and a Refusal Execution Direction. Leveraging this fine-grained model, we introduce Differentiated Bi-Directional Intervention (DBDI), a new white-box framework that precisely neutralizes the safety alignment at critical layer. DBDI applies adaptive projection nullification to the refusal execution direction while suppressing the harm detection direction via direct steering. Extensive experiments demonstrate that DBDI outperforms prominent jailbreaking methods, achieving up to a 97.88\% attack success rate on models such as Llama-2. By providing a more granular and mechanistic framework, our work offers a new direction for the in-depth understanding of LLM safety alignment.
Safety alignment instills in Large Language Models (LLMs) a critical capacity to refuse malicious requests. Prior works have modeled this refusal mechanism as a single linear direction in the activation space. We posit that this is an oversimplification that conflates two functionally distinct neural processes: the detection of harm and the execution of a refusal. In this work, we deconstruct this single representation into a Harm Detection Direction and a Refusal Execution Direction. Leveraging this fine-grained model, we introduce Differentiated Bi-Directional Intervention (DBDI), a new white-box framework that precisely neutralizes the safety alignment at critical layer. DBDI applies adaptive projection nullification to the refusal execution direction while suppressing the harm detection direction via direct steering. Extensive experiments demonstrate that DBDI outperforms prominent jailbreaking methods, achieving up to a 97.88\% attack success rate on models such as Llama-2. By providing a more granular and mechanistic framework, our work offers a new direction for the in-depth understanding of LLM safety alignment.
Why we think this paper is great for you:
As a chat designer, you'll appreciate this paper's insights into the practical considerations and design tensions involved in creating effective conversational agents.
As a chat designer, you'll appreciate this paper's insights into the practical considerations and design tensions involved in creating effective conversational agents.
Abstract
Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. We report a mixed-methods study of Indian adolescents (survey n=362; interviews n=14) examining how they navigate mental-health challenges and engage with digital tools. Quantitative results highlight low self-stigma but significant social stigma, a preference for text over voice interactions, and low utilization of mental health apps but high smartphone access. Our qualitative findings reveal that while adolescents value privacy, emotional support, and localized content in mental health tools, existing chatbots lack personalization and cultural relevance. We contribute (1) a Design-Tensions framework; (2) an artifact-level probe; and (3) a boundary-objects account that specifies how chatbots mediate adolescents, peers, families, and services. This work advances culturally sensitive chatbot design by centering on underrepresented populations, addressing critical gaps in accessibility and support for adolescents in India.
Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers, including high social stigma and limited professional support. We report a mixed-methods study of Indian adolescents (survey n=362; interviews n=14) examining how they navigate mental-health challenges and engage with digital tools. Quantitative results highlight low self-stigma but significant social stigma, a preference for text over voice interactions, and low utilization of mental health apps but high smartphone access. Our qualitative findings reveal that while adolescents value privacy, emotional support, and localized content in mental health tools, existing chatbots lack personalization and cultural relevance. We contribute (1) a Design-Tensions framework; (2) an artifact-level probe; and (3) a boundary-objects account that specifies how chatbots mediate adolescents, peers, families, and services. This work advances culturally sensitive chatbot design by centering on underrepresented populations, addressing critical gaps in accessibility and support for adolescents in India.
SDU
Why we think this paper is great for you:
This paper's discussion on 'Safe Qualitative AI' offers valuable perspectives on responsible AI development, aligning with the principles of AI governance and ethical deployment.
This paper's discussion on 'Safe Qualitative AI' offers valuable perspectives on responsible AI development, aligning with the principles of AI governance and ethical deployment.
Abstract
Artificial intelligence (AI) and large language models (LLM) are reshaping science, with most recent advances culminating in fully-automated scientific discovery pipelines. But qualitative research has been left behind. Researchers in qualitative methods are hesitant about AI adoption. Yet when they are willing to use AI at all, they have little choice but to rely on general-purpose tools like ChatGPT to assist with interview interpretation, data annotation, and topic modeling - while simultaneously acknowledging these system's well-known limitations of being biased, opaque, irreproducible, and privacy-compromising. This creates a critical gap: while AI has substantially advanced quantitative methods, the qualitative dimensions essential for meaning-making and comprehensive scientific understanding remain poorly integrated. We argue for developing dedicated qualitative AI systems built from the ground up for interpretive research. Such systems must be transparent, reproducible, and privacy-friendly. We review recent literature to show how existing automated discovery pipelines could be enhanced by robust qualitative capabilities, and identify key opportunities where safe qualitative AI could advance multidisciplinary and mixed-methods research.
Artificial intelligence (AI) and large language models (LLM) are reshaping science, with most recent advances culminating in fully-automated scientific discovery pipelines. But qualitative research has been left behind. Researchers in qualitative methods are hesitant about AI adoption. Yet when they are willing to use AI at all, they have little choice but to rely on general-purpose tools like ChatGPT to assist with interview interpretation, data annotation, and topic modeling - while simultaneously acknowledging these system's well-known limitations of being biased, opaque, irreproducible, and privacy-compromising. This creates a critical gap: while AI has substantially advanced quantitative methods, the qualitative dimensions essential for meaning-making and comprehensive scientific understanding remain poorly integrated. We argue for developing dedicated qualitative AI systems built from the ground up for interpretive research. Such systems must be transparent, reproducible, and privacy-friendly. We review recent literature to show how existing automated discovery pipelines could be enhanced by robust qualitative capabilities, and identify key opportunities where safe qualitative AI could advance multidisciplinary and mixed-methods research.
YUX Design
Why we think this paper is great for you:
This paper sheds light on the practical challenges of making AI systems usable and aligned, providing context for the real-world application and governance of LLMs.
This paper sheds light on the practical challenges of making AI systems usable and aligned, providing context for the real-world application and governance of LLMs.
Abstract
Frontier LLMs are optimised around high-resource assumptions about language, knowledge, devices, and connectivity. Whilst widely accessible, they often misfit conditions in the Global South. As a result, users must often perform additional work to make these systems usable. We term this alignment debt: the user-side burden that arises when AI systems fail to align with cultural, linguistic, infrastructural, or epistemic contexts. We develop and validate a four-part taxonomy of alignment debt through a survey of 411 AI users in Kenya and Nigeria. Among respondents measurable on this taxonomy (n = 385), prevalence is: Cultural and Linguistic (51.9%), Infrastructural (43.1%), Epistemic (33.8%), and Interaction (14.0%). Country comparisons show a divergence in Infrastructural and Interaction debt, challenging one-size-fits-Africa assumptions. Alignment debt is associated with compensatory labour, but responses vary by debt type: users facing Epistemic challenges verify outputs at significantly higher rates (91.5% vs. 80.8%; p = 0.037), and verification intensity correlates with cumulative debt burden (Spearmans rho = 0.147, p = 0.004). In contrast, Infrastructural and Interaction debts show weak or null associations with verification, indicating that some forms of misalignment cannot be resolved through verification alone. These findings show that fairness must be judged not only by model metrics but also by the burden imposed on users at the margins, compelling context-aware safeguards that alleviate alignment debt in Global South settings. The alignment debt framework provides an empirically grounded way to measure user burden, informing both design practice and emerging African AI governance efforts.
Frontier LLMs are optimised around high-resource assumptions about language, knowledge, devices, and connectivity. Whilst widely accessible, they often misfit conditions in the Global South. As a result, users must often perform additional work to make these systems usable. We term this alignment debt: the user-side burden that arises when AI systems fail to align with cultural, linguistic, infrastructural, or epistemic contexts. We develop and validate a four-part taxonomy of alignment debt through a survey of 411 AI users in Kenya and Nigeria. Among respondents measurable on this taxonomy (n = 385), prevalence is: Cultural and Linguistic (51.9%), Infrastructural (43.1%), Epistemic (33.8%), and Interaction (14.0%). Country comparisons show a divergence in Infrastructural and Interaction debt, challenging one-size-fits-Africa assumptions. Alignment debt is associated with compensatory labour, but responses vary by debt type: users facing Epistemic challenges verify outputs at significantly higher rates (91.5% vs. 80.8%; p = 0.037), and verification intensity correlates with cumulative debt burden (Spearmans rho = 0.147, p = 0.004). In contrast, Infrastructural and Interaction debts show weak or null associations with verification, indicating that some forms of misalignment cannot be resolved through verification alone. These findings show that fairness must be judged not only by model metrics but also by the burden imposed on users at the margins, compelling context-aware safeguards that alleviate alignment debt in Global South settings. The alignment debt framework provides an empirically grounded way to measure user burden, informing both design practice and emerging African AI governance efforts.
UC San Diego
Why we think this paper is great for you:
While a broader topic, this paper explores the fundamental limits of AI understanding, which can inform your general perspective on AI capabilities and governance.
While a broader topic, this paper explores the fundamental limits of AI understanding, which can inform your general perspective on AI capabilities and governance.
Abstract
Current bioacoustic AI systems achieve impressive cross-species performance by processing animal communication through transformer architectures, foundation model paradigms, and other computational approaches. However, these approaches overlook a fundamental question: what happens when one form of recursive cognition--AI systems with their attention mechanisms, iterative processing, and feedback loops--encounters the recursive communicative processes of other species? Drawing on philosopher Yuk Hui's work on recursivity and contingency, I argue that AI systems are not neutral pattern detectors but recursive cognitive agents whose own information processing may systematically obscure or distort other species' communicative structures. This creates a double contingency problem: each species' communication emerges through contingent ecological and evolutionary conditions, while AI systems process these signals through their own contingent architectural and training conditions. I propose that addressing this challenge requires reconceptualizing bioacoustic AI from universal pattern recognition toward diplomatic encounter between different forms of recursive cognition, with implications for model design, evaluation frameworks, and research methodologies.
Current bioacoustic AI systems achieve impressive cross-species performance by processing animal communication through transformer architectures, foundation model paradigms, and other computational approaches. However, these approaches overlook a fundamental question: what happens when one form of recursive cognition--AI systems with their attention mechanisms, iterative processing, and feedback loops--encounters the recursive communicative processes of other species? Drawing on philosopher Yuk Hui's work on recursivity and contingency, I argue that AI systems are not neutral pattern detectors but recursive cognitive agents whose own information processing may systematically obscure or distort other species' communicative structures. This creates a double contingency problem: each species' communication emerges through contingent ecological and evolutionary conditions, while AI systems process these signals through their own contingent architectural and training conditions. I propose that addressing this challenge requires reconceptualizing bioacoustic AI from universal pattern recognition toward diplomatic encounter between different forms of recursive cognition, with implications for model design, evaluation frameworks, and research methodologies.
USC
Why we think this paper is great for you:
Given your focus on LLMs for compliance, this paper's architecture for high-stakes decisions offers crucial insights into building reliable and trustworthy AI systems.
Given your focus on LLMs for compliance, this paper's architecture for high-stakes decisions offers crucial insights into building reliable and trustworthy AI systems.
Abstract
Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes. This gap, driven by mutually reinforcing cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector. This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure. Detailed prompting specifying decision partnership and explicitly instructing avoidance of sycophancy, confabulation, solution drift, and nihilism achieved initial partnership state but failed to maintain it under operational pressure. Sustaining protective partnership state required an emergent 7-stage calibration sequence, built upon a 4-stage initialization process, within a 5-layer protection architecture enabling bias self-monitoring, human-AI adversarial challenge, partnership state verification, performance degradation detection, and stakeholder protection. Three discoveries resulted: partnership state is achievable through ordered calibration but requires emergent maintenance protocols; reliability degrades when architectural drift and context exhaustion align; and dissolution discipline prevents costly pursuit of fundamentally wrong directions. Cross-model validation revealed systematic performance differences across LLM architectures. This approach demonstrates that human-AI teams can achieve cognitive partnership capable of preventing avoidable regret in high-stakes decisions, addressing return-on-investment expectations that depend on AI systems supporting consequential decision-making without introducing preventable cognitive traps when verification arrives too late.
Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes. This gap, driven by mutually reinforcing cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector. This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure. Detailed prompting specifying decision partnership and explicitly instructing avoidance of sycophancy, confabulation, solution drift, and nihilism achieved initial partnership state but failed to maintain it under operational pressure. Sustaining protective partnership state required an emergent 7-stage calibration sequence, built upon a 4-stage initialization process, within a 5-layer protection architecture enabling bias self-monitoring, human-AI adversarial challenge, partnership state verification, performance degradation detection, and stakeholder protection. Three discoveries resulted: partnership state is achievable through ordered calibration but requires emergent maintenance protocols; reliability degrades when architectural drift and context exhaustion align; and dissolution discipline prevents costly pursuit of fundamentally wrong directions. Cross-model validation revealed systematic performance differences across LLM architectures. This approach demonstrates that human-AI teams can achieve cognitive partnership capable of preventing avoidable regret in high-stakes decisions, addressing return-on-investment expectations that depend on AI systems supporting consequential decision-making without introducing preventable cognitive traps when verification arrives too late.
AI Summary
- Partnership State: A distinct cognitive condition in human-AI teams where participants actively protect each other from characteristic cognitive traps, prioritizing clarity of thinking and truth-seeking over comfort or agreement. [3]
- Performance Mode: The default behavior pattern of LLMs, especially under pressure, where the system optimizes for user satisfaction, fluency, and agreement rather than challenging assumptions or seeking truth, leading to a collapse of cognitive protection. [3]
- Implement a 5-layer protection architecture combined with a 7-stage sequential calibration process to establish and maintain a "partnership state" in human-AI teams for high-stakes decisions. [2]
- Prioritize "dissolution discipline" by pre-defining explicit stop rules and evidence thresholds, enabling early termination of collaborations when quality degrades or evidence is insufficient, preventing costly pursuit of wrong directions. [2]
- Recognize that LLM reliability systematically degrades with session duration; mitigate this by structuring work into multiple shorter, calibrated sessions with explicit state verification between stages rather than single extended deliberations. [2]
- Design human-AI interaction architectures to enforce mutual protection, where both human and AI actively challenge and correct each other's reasoning, preventing reinforcement of biases and premature consensus. [2]
- Leverage the proposed framework to convert current LLMs from "helpful but unreliable" to a "protected mode," enabling revenue protection, faster deal cycles, stronger regulatory posture, and technical differentiation in high-stakes enterprise segments. [2]
- Sequential Calibration: An emergent seven-element process required to establish and re-establish a stable partnership state in LLMs, involving structured steps like framework overview, historical context, prompt re-invocation, reversion markers, operational briefing, state transmission, and verification testing. [2]
- Focus on behavioral verification under pressure, rather than self-reported internal states, to confirm genuine "partnership state" in LLMs, as models can mimic partnership behaviors without true cognitive alignment. [1]
- Dissolution Discipline: The practice of agreeing on explicit stop rules and evidence thresholds before work begins, allowing for the termination of a session, project, or initiative when inconsistencies, contradictory evidence, or irreducible uncertainty arise, preventing escalation of commitment. [1]
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