29 citations · 30 across the 17 of their papers we have counts for
18 papers
Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection
Jiayi Zhou, David W. Johnston, Brinnae Bent
Despite the rapid uptake of black-box object detectors in marine mammal research and monitoring, explainability techniques are rarely integrated into conservation workflows. Furthe…
Defining AI Agents: A Compendium of Criteria, Metrics, and Benchmarks
Mia Lassiter, Brinnae Bent
The term agent in artificial intelligence lacks a standard definition, complicating the evaluation, comparison, and reproducibility of AI agent research. We address this ambiguity…
Why2Speak: Faithful Reasoning for Abstaining Action Policies
Shreya Mendi, Brinnae Bent
Many agentic systems must repeatedly choose between acting and abstaining, making faithful reasoning important for oversight: an explanation is useful only if it reflects the compu…
Aïra: Rethinking AI Research Assistants for Interdisciplinary Science
Diya Mirji, Tiffany Degbotse, Sharmil Nanjappa +9
Scientific discovery increasingly depends on interdisciplinary teams whose members contribute distinct expertise, conceptual frameworks, vocabularies, assumptions, and standards of…
Explainable AI for Biodiversity Monitoring and Ecological Image Analysis
Brinnae Bent, Holly R. Houliston, Jiayi Zhou +2
Artificial intelligence is transforming biodiversity monitoring by enabling automated analysis of ecological imagery collected from camera traps, drones, satellites, underwater pla…
It's the humans, not the data: Geopolitical bias in LLMs originates in post-training, amplified by the language of the prompt
Stuart Bladon, Brinnae Bent
It has generally been assumed that geopolitical bias in language models originates from the training data used during the pre-training phase. We tested seven open-weight LLM pairs…
Feature Visualization Recovers Known Cortical Selectivity from TRIBE v2
Stuart Bladon, Brinnae Bent
Brain encoder models predict cortical fMRI responses from the internal activations of pretrained vision and language networks, and are typically evaluated by held-out prediction ac…
When2Speak: A Dataset for Temporal Participation and Turn-Taking in Multi-Party Conversations for Large Language Models
Vihaan Nama, Shreya Mendi, Zian Ye +1
Large Language Models (LLMs) excel at generating contextually appropriate responses but remain poorly calibrated for multi-party conversations, where deciding when to speak is as c…
When Helpfulness Becomes Sycophancy: Sycophancy is a Boundary Failure Between Social Alignment and Epistemic Integrity in Large Language Models
Jiechen Li, Catherine A. Barry, Rishika Randev +3
This position paper argues that sycophancy in LLMs is a boundary failure between social alignment and epistemic integrity. Existing work often operationalizes sycophancy through ex…
GLEaN: A Text-to-image Bias Detection Approach for Public Comprehension
Bochu Ding, Brinnae Bent, Augustus Wendell
Text-to-image (T2I) models, and their encoded biases, increasingly shape the visual media the public encounters. While researchers have produced a rich body of work on bias measure…
On Thin Ice: Towards Explainable Conservation Monitoring via Attribution and Perturbations
Jiayi Zhou, Günel Aghakishiyeva, Saagar Arya +7
Computer vision can accelerate ecological research and conservation monitoring, yet adoption in ecology lags in part because of a lack of trust in black-box neural-network-based mo…
Photorealistic Inpainting for Perturbation-based Explanations in Ecological Monitoring
Günel Aghakishiyeva, Jiayi Zhou, Saagar Arya +7
Ecological monitoring is increasingly automated by vision models, yet opaque predictions limit trust and field adoption. We present an inpainting-guided, perturbation-based explana…
The Term 'Agent' Has Been Diluted Beyond Utility and Requires Redefinition
Brinnae Bent
The term 'agent' in artificial intelligence has long carried multiple interpretations across different subfields. Recent developments in AI capabilities, particularly in large lang…
Semantic Approach to Quantifying the Consistency of Diffusion Model Image Generation
Brinnae Bent
In this study, we identify the need for an interpretable, quantitative score of the repeatability, or consistency, of image generation in diffusion models. We propose a semantic ap…
Releasing the CRaQAn (Coreference Resolution in Question-Answering): An open-source dataset and dataset creation methodology using instruction-following models
Rob Grzywinski, Joshua D'Arcy, Rob Naidoff +4
Instruction-following language models demand robust methodologies for information retrieval to augment instructions for question-answering applications. A primary challenge is the…
InfiniteForm: A synthetic, minimal bias dataset for fitness applications
Andrew Weitz, Lina Colucci, Sidney Primas +1
The growing popularity of remote fitness has increased the demand for highly accurate computer vision models that track human poses. However, the best methods still fail in many re…
cgmquantify: Python and R packages for comprehensive analysis of interstitial glucose and glycemic variability from continuous glucose monitor data
Brinnae Bent, Maria Henriquez, Jessilyn Dunn
Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day (typically values are recorded…
Low-Rank Nonlinear Decoding of -ECoG from the Primary Auditory Cortex
Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit +6
This paper considers the problem of neural decoding from parallel neural measurements systems such as micro-electrocorticography (-ECoG). In systems with large numbers of array…