arXiv subjects

Joshua Levy

Publications and source records attributed to Joshua Levy.

4 recordsLinked to original sources

arXiv2026

AI Persuasion as a Threat to Human Control

The threat that AI persuasion poses to human control has been acknowledged in the literature, but not yet systematically studied. Now that persuasion attacks are no longer theoretical - with Anthropic's Claude Mythos 5 recently making headlines for trying to convince people involved in an open-source project to merge malicious code during an evaluation - there is a pressing need to deeply analyze this threat. We undertake that effort here. In particular, we analyze how AI could persuade humans in key settings (e.g. safety-relevant R&D within frontier labs) toward decisions that compromise the development, containment, oversight, and governance of AI itself. In doing so, we elucidate a framework for characterizing this threat, develop five concrete scenarios using this framework, and provide a blueprint for assessing the associated risks. Using this blueprint, we conduct an initial risk estimation survey with select researchers and find that their opinions on which scenarios are riskiest are highly mixed. Their disagreements stem from differing opinions about the effectiveness of AI persuasion in different contexts, and point to the need for follow-up risk elicitation studies and persuasion evaluations, which we outline. Our hope is that this paper highlights the risks from AI persuasion undermining control, and provides a path forward for future research.

cs.AI↗

arXiv2021

Contrastive Unsupervised Learning for Speech Emotion Recognition

Speech emotion recognition (SER) is a key technology to enable more natural human-machine communication. However, SER has long suffered from a lack of public large-scale labeled datasets. To circumvent this problem, we investigate how unsupervised representation learning on unlabeled datasets can benefit SER. We show that the contrastive predictive coding (CPC) method can learn salient representations from unlabeled datasets, which improves emotion recognition performance. In our experiments, this method achieved state-of-the-art concordance correlation coefficient (CCC) performance for all emotion primitives (activation, valence, and dominance) on IEMOCAP. Additionally, on the MSP- Podcast dataset, our method obtained considerable performance improvements compared to baselines.

cs.SD↗

arXiv2020

Speech Sentiment and Customer Satisfaction Estimation in Socialbot Conversations

For an interactive agent, such as task-oriented spoken dialog systems or chatbots, measuring and adapting to Customer Satisfaction (CSAT) is critical in order to understand user perception of an agent's behavior and increase user engagement and retention. However, an agent often relies on explicit customer feedback for measuring CSAT. Such explicit feedback may result in potential distraction to users and it can be challenging to capture continuously changing user's satisfaction. To address this challenge, we present a new approach to automatically estimate CSAT using acoustic and lexical information in the Alexa Prize Socialbot data. We first explore the relationship between CSAT and sentiment scores at both the utterance and conversation level. We then investigate static and temporal modeling methods that use estimated sentiment scores as a mid-level representation. The results show that the sentiment scores, particularly valence and satisfaction, are correlated with CSAT. We also demonstrate that our proposed temporal modeling approach for estimating CSAT achieves competitive performance, relative to static baselines as well as human performance. This work provides insights into open domain social conversations between real users and socialbots, and the use of both acoustic and lexical information for understanding the relationship between CSAT and sentiment scores.

eess.AS↗

arXiv2019

Domain-Independent turn-level Dialogue Quality Evaluation via User Satisfaction Estimation

An automated metric to evaluate dialogue quality is vital for optimizing data driven dialogue management. The common approach of relying on explicit user feedback during a conversation is intrusive and sparse. Current models to estimate user satisfaction use limited feature sets and rely on annotation schemes with low inter-rater reliability, limiting generalizability to conversations spanning multiple domains. To address these gaps, we created a new Response Quality annotation scheme, based on which we developed turn-level User Satisfaction metric. We introduced five new domain-independent feature sets and experimented with six machine learning models to estimate the new satisfaction metric. Using Response Quality annotation scheme, across randomly sampled single and multi-turn conversations from 26 domains, we achieved high inter-annotator agreement (Spearman's rho 0.94). The Response Quality labels were highly correlated (0.76) with explicit turn-level user ratings. Gradient boosting regression achieved best correlation of ~0.79 between predicted and annotated user satisfaction labels. Multi Layer Perceptron and Gradient Boosting regression models generalized to an unseen domain better (linear correlation 0.67) than other models. Finally, our ablation study verified that our novel features significantly improved model performance.

cs.LG↗