Search Results for author:
Found 27 papers, 5 papers with code
Extracting Age-Related Stereotypes from Social Media Texts
Age-related stereotypes are pervasive in our society, and yet have been under-studied in the NLP community.
Does Moral Code have a Moral Code? Probing Delphi’s Moral Philosophy
In an effort to guarantee that machine learning model outputs conform with human moral values, recent work has begun exploring the possibility of explicitly training models to learn the difference between right and wrong.
Challenging Negative Gender Stereotypes: A Study on the Effectiveness of Automated Counter-Stereotypes
The strategies of counter-facts and broadening universals (i. e., stating that anyone can have a trait regardless of group membership) emerged as the most robust approaches, while humour, perspective-taking, counter-examples, and empathy for the speaker were perceived as less effective.
Uncovering Bias in Large Vision-Language Models with Counterfactuals
We comprehensively evaluate the text produced by different LVLMs under this counterfactual generation setting and find that social attributes such as race, gender, and physical characteristics depicted in input images can significantly influence toxicity and the generation of competency-associated words.
Examining Gender and Racial Bias in Large Vision-Language Models Using a Novel Dataset of Parallel Images
Following on recent advances in large language models (LLMs) and subsequent chat models, a new wave of large vision-language models (LVLMs) has emerged.
Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers
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 classification accuracy.
The crime of being poor
The criminalization of poverty has been widely denounced as a collective bias against the most vulnerable.
A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?
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.
Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information
For a multi-class toxic language classifier, we leverage a concept-based explanation framework to calculate the sensitivity of the model to the concept of sentiment, which has been used before as a salient feature for toxic language detection.
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models
In this paper, we briefly review trends in explainability and fairness in NLP research, identify the current practices in which explainability methods are applied to detect and mitigate bias, and investigate the barriers preventing XAI methods from being used more widely in tackling fairness issues.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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Does Moral Code Have a Moral Code? Probing Delphi's Moral Philosophy
In an effort to guarantee that machine learning model outputs conform with human moral values, recent work has begun exploring the possibility of explicitly training models to learn the difference between right and wrong.
Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection
We present a novel feature attribution method for explaining text classifiers, and analyze it in the context of hate speech detection.
Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors
Robustness of machine learning models on ever-changing real-world data is critical, especially for applications affecting human well-being such as content moderation.
Measuring Cognitive Status from Speech in a Smart Home Environment
We then present an overview of the preliminary results from pilot studies on active and passive smart home speech sensing for the measurement of cognitive health, and conclude with some recommendations and challenge statements for the next wave of work in this area, to help overcome both technical and ethical barriers to success.
Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model
In this work, we present a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology.
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective
The pervasiveness of abusive content on the internet can lead to severe psychological and physical harm.
Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience
When comparing entities extracted by a medical entity recognition system with gold standard annotations over a test set, two types of mismatches might occur, label mismatch or span mismatch.
Recognizing UMLS Semantic Types with Deep Learning
While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0. 90), our results on MedMentions are significantly lower (F1 = 0. 63), suggesting there is still plenty of opportunity for improvement on this new data.
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models
While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0. 90), our results on MedMentions are significantly lower (F1 = 0. 63), suggesting there is still plenty of opportunity for improvement on this new data.
Multilingual prediction of Alzheimer's disease through domain adaptation and concept-based language modelling
There is growing evidence that changes in speech and language may be early markers of dementia, but much of the previous NLP work in this area has been limited by the size of the available datasets.
How do we feel when a robot dies? Emotions expressed on Twitter before and after hitchBOT's destruction
In 2014, a chatty but immobile robot called hitchBOT set out to hitchhike across Canada.
The importance of sharing patient-generated clinical speech and language data
Increased access to large datasets has driven progress in NLP.
An analysis of eye-movements during reading for the detection of mild cognitive impairment
We present a machine learning analysis of eye-tracking data for the detection of mild cognitive impairment, a decline in cognitive abilities that is associated with an increased risk of developing dementia.
