79 citations · 189 across the 31 of their papers we have counts for
34 papers
Beyond Accuracy: Interpreting Topic Representation in Suicide Ideation Detection Models
Hamideh Ghanadian, Isar Nejadgholi, Hussein Al Osman
Suicide ideation detection models are typically evaluated using aggregate performance metrics, yet little is known about how they internally represent psychologically meaningful ri…
CIRCLE: A Framework for Evaluating AI from a Real-World Lens
Reva Schwartz, Carina Westling, Morgan Briggs +12
This study proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and AI system outcomes in deployment. Current…
Improving Methodologies for LLM Evaluations Across Global Languages
Akriti Vij, Benjamin Chua, Darshini Ramiah +43
As frontier AI models are deployed globally, it is essential that their behaviour remains safe and reliable across diverse linguistic and cultural contexts. To examine how current…
Improving Methodologies for Agentic Evaluations Across Domains: Leakage of Sensitive Information, Fraud and Cybersecurity Threats
Ee Wei Seah, Yongsen Zheng, Naga Nikshith +67
The rapid rise of autonomous AI systems and advancements in agent capabilities are introducing new risks due to reduced oversight of real-world interactions. Yet agent testing rema…
From Perceived Effectiveness to Measured Impact: Identity-Aware Evaluation of Automated Counter-Stereotypes
Svetlana Kiritchenko, Anna Kerkhof, Isar Nejadgholi +1
We investigate the effect of automatically generated counter-stereotypes on gender bias held by users of various demographics on social media. Building on recent NLP advancements a…
Semantic Differentiation in Speech Emotion Recognition: Insights from Descriptive and Expressive Speech Roles
Rongchen Guo, Vincent Francoeur, Isar Nejadgholi +2
Speech Emotion Recognition (SER) is essential for improving human-computer interaction, yet its accuracy remains constrained by the complexity of emotional nuances in speech. In th…
A Taxonomy for Design and Evaluation of Prompt-Based Natural Language Explanations
Isar Nejadgholi, Mona Omidyeganeh, Marc-Antoine Drouin +1
Effective AI governance requires structured approaches for stakeholders to access and verify AI system behavior. With the rise of large language models, Natural Language Explanatio…
Fine-Tuning Lowers Safety and Disrupts Evaluation Consistency
Kathleen C. Fraser, Hillary Dawkins, Isar Nejadgholi +1
Fine-tuning a general-purpose large language model (LLM) for a specific domain or task has become a routine procedure for ordinary users. However, fine-tuning is known to remove th…
Gender-Neutral Machine Translation Strategies in Practice
Hillary Dawkins, Isar Nejadgholi, Chi-kiu Lo
Gender-inclusive machine translation (MT) should preserve gender ambiguity in the source to avoid misgendering and representational harms. While gender ambiguity often occurs natur…
Tackling Social Bias against the Poor: A Dataset and Taxonomy on Aporophobia
Georgina Curto, Svetlana Kiritchenko, Muhammad Hammad Fahim Siddiqui +2
Eradicating poverty is the first goal in the United Nations Sustainable Development Goals. However, aporophobia -- the societal bias against people living in poverty -- constitutes…
WildFireCan-MMD: A Multimodal Dataset for Classification of User-Generated Content During Wildfires in Canada
Braeden Sherritt, Isar Nejadgholi, Efstratios Aivaliotis +2
Rapid information access is vital during wildfires, yet traditional data sources are slow and costly. Social media offers real-time updates, but extracting relevant insights remain…
WMT24 Test Suite: Gender Resolution in Speaker-Listener Dialogue Roles
Hillary Dawkins, Isar Nejadgholi, Chi-kiu Lo
We assess the difficulty of gender resolution in literary-style dialogue settings and the influence of gender stereotypes. Instances of the test suite contain spoken dialogue inter…
Adaptable Moral Stances of Large Language Models on Sexist Content: Implications for Society and Gender Discourse
Rongchen Guo, Isar Nejadgholi, Hillary Dawkins +2
This work provides an explanatory view of how LLMs can apply moral reasoning to both criticize and defend sexist language. We assessed eight large language models, all of which dem…
Human-Centered AI Applications for Canada's Immigration Settlement Sector
Isar Nejadgholi, Maryam Molamohammadi, Kimiya Missaghi +1
While AI has been frequently applied in the context of immigration, most of these applications focus on selection and screening, which primarily serve to empower states and authori…
Social and Ethical Risks Posed by General-Purpose LLMs for Settling Newcomers in Canada
Isar Nejadgholi, Maryam Molamohammadi, Samir Bakhtawar
The non-profit settlement sector in Canada supports newcomers in achieving successful integration. This sector faces increasing operational pressures amidst rising immigration targ…
Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes
Isar Nejadgholi, Kathleen C. Fraser, Anna Kerkhof +1
Gender stereotypes are pervasive beliefs about individuals based on their gender that play a significant role in shaping societal attitudes, behaviours, and even opportunities. Rec…
Projective Methods for Mitigating Gender Bias in Pre-trained Language Models
Hillary Dawkins, Isar Nejadgholi, Daniel Gillis +1
Mitigation of gender bias in NLP has a long history tied to debiasing static word embeddings. More recently, attention has shifted to debiasing pre-trained language models. We stud…
Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models
Hamideh Ghanadian, Isar Nejadgholi, Hussein Al Osman
Suicidal ideation detection is a vital research area that holds great potential for improving mental health support systems. However, the sensitivity surrounding suicide-related da…
Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers
Isar Nejadgholi, Svetlana Kiritchenko, Kathleen C. Fraser +1
Classifiers tend to learn a false causal relationship between an over-represented concept and a label, which can result in over-reliance on the concept and compromised classificati…
ChatGPT for Suicide Risk Assessment on Social Media: Quantitative Evaluation of Model Performance, Potentials and Limitations
Hamideh Ghanadian, Isar Nejadgholi, Hussein Al Osman
This paper presents a novel framework for quantitatively evaluating the interactive ChatGPT model in the context of suicidality assessment from social media posts, utilizing the Un…
The crime of being poor
Georgina Curto, Svetlana Kiritchenko, Isar Nejadgholi +1
The criminalization of poverty has been widely denounced as a collective bias against the most vulnerable. NGOs and international organizations claim that the poor are blamed for t…
A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?
Kathleen C. Fraser, Svetlana Kiritchenko, Isar Nejadgholi
As text-to-image systems continue to grow in popularity with the general public, questions have arisen about bias and diversity in the generated images. Here, we investigate proper…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information
Isar Nejadgholi, Esma Balkır, Kathleen C. Fraser +1
Previous works on the fairness of toxic language classifiers compare the output of models with different identity terms as input features but do not consider the impact of other im…