Search Results for author: Rumi Chunara

Found 18 papers, 2 papers with code

Leveraging CLIP for Inferring Sensitive Information and Improving Model Fairness

no code implementations15 Mar 2024 Miao Zhang, Rumi Chunara

In this work, we explore a new paradigm that does not require sensitive attribute labels, and evades the need for extra training by leveraging the vision-language model, CLIP, as a rich knowledge source to infer sensitive information.

Attribute Fairness +1

Disparate Effect Of Missing Mediators On Transportability of Causal Effects

no code implementations13 Mar 2024 Vishwali Mhasawade, Rumi Chunara

We study this issue of missing mediators, motivated by challenges in public health, wherein mediators can be missing, not at random.

Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model

no code implementations14 Feb 2024 Salman Rahman, Lavender Yao Jiang, Saadia Gabriel, Yindalon Aphinyanaphongs, Eric Karl Oermann, Rumi Chunara

Overall, this study provides new insights for enhancing the deployment of large language models in the societally important domain of healthcare, and improving their performance for broader populations.

Descriptive Language Modelling +2

Impact on Public Health Decision Making by Utilizing Big Data Without Domain Knowledge

no code implementations8 Feb 2024 Miao Zhang, Salman Rahman, Vishwali Mhasawade, Rumi Chunara

Relevant to such uses, important examples of bias in the use of AI are evident when decision-making based on data fails to account for the robustness of the data, or predictions are based on spurious correlations.

Decision Making

Understanding Disparities in Post Hoc Machine Learning Explanation

no code implementations25 Jan 2024 Vishwali Mhasawade, Salman Rahman, Zoe Haskell-Craig, Rumi Chunara

Previous work has highlighted that existing post-hoc explanation methods exhibit disparities in explanation fidelity (across 'race' and 'gender' as sensitive attributes), and while a large body of work focuses on mitigating these issues at the explanation metric level, the role of the data generating process and black box model in relation to explanation disparities remains largely unexplored.

Attribute

A Brief Tutorial on Sample Size Calculations for Fairness Audits

1 code implementation7 Dec 2023 Harvineet Singh, Fan Xia, Mi-Ok Kim, Romain Pirracchio, Rumi Chunara, Jean Feng

In fairness audits, a standard objective is to detect whether a given algorithm performs substantially differently between subgroups.

Binary Classification Fairness

Fair contrastive pre-training for geographic image segmentation

no code implementations16 Nov 2022 Miao Zhang, Rumi Chunara

Contrastive self-supervised learning is widely employed in visual recognition for geographic image data (remote or proximal sensing), but because of landscape heterogeneity, models can show disparate performance across spatial units.

Attribute Contrastive Learning +5

Segmenting across places: The need for fair transfer learning with satellite imagery

no code implementations9 Apr 2022 Miao Zhang, Harvineet Singh, Lazarus Chok, Rumi Chunara

This work highlights the need to conduct fairness analysis for satellite imagery segmentation models and motivates the development of methods for fair transfer learning in order not to introduce disparities between places, particularly urban and rural locations.

Fairness Image Segmentation +4

Causal Multi-Level Fairness

no code implementations14 Oct 2020 Vishwali Mhasawade, Rumi Chunara

While work in algorithmic fairness to-date has primarily focused on addressing discrimination due to individually linked attributes, social science research elucidates how some properties we link to individuals can be conceptualized as having causes at macro (e. g. structural) levels, and it may be important to be fair to attributes at multiple levels.

Attribute Causal Inference +1

Machine Learning in Population and Public Health

no code implementations21 Jul 2020 Vishwali Mhasawade, Yuan Zhao, Rumi Chunara

Research in population and public health focuses on the mechanisms between different cultural, social, and environmental factors and their effect on the health, of not just individuals, but communities as a whole.

BIG-bench Machine Learning

Fairness Violations and Mitigation under Covariate Shift

no code implementations2 Nov 2019 Harvineet Singh, Rina Singh, Vishwali Mhasawade, Rumi Chunara

We study the problem of learning fair prediction models for unseen test sets distributed differently from the train set.

Domain Adaptation Fairness +2

Population-aware Hierarchical Bayesian Domain Adaptation via Multiple-component Invariant Learning

1 code implementation24 Aug 2019 Vishwali Mhasawade, Nabeel Abdur Rehman, Rumi Chunara

Based on sources of stability in the model, we posit that for human-sourced data and health prediction tasks we can combine environment and population information in a novel population-aware hierarchical Bayesian domain adaptation framework that harnesses multiple invariant components through population attributes when needed.

Domain Adaptation

Using Contextual Information to Improve Blood Glucose Prediction

no code implementations24 Aug 2019 Mohammad Akbari, Rumi Chunara

Person-generated data sources, such as actively contributed surveys as well as passively mined data from social media offer opportunity to capture such context, however the self-reported nature and sparsity of such data mean that such data are noisier and less specific than physiological measures such as blood glucose values themselves.

Gaussian Processes Management +1

Deep Landscape Features for Improving Vector-borne Disease Prediction

no code implementations3 Apr 2019 Nabeel Abdur Rehman, Umar Saif, Rumi Chunara

We then incorporate landscape features from satellite image data from Pakistan, labelled using the CNN, in a well-known Susceptible-Infectious-Recovered epidemic model, alongside dengue case data from 2012-2016 in Pakistan.

Disease Prediction

From the User to the Medium: Neural Profiling Across Web Communities

no code implementations3 Dec 2018 Mohammad Akbari, Kunal Relia, Anas Elghafari, Rumi Chunara

Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management.

Clustering Community Detection +1

Population-aware Hierarchical Bayesian Domain Adaptation

no code implementations21 Nov 2018 Vishwali Mhasawade, Nabeel Abdur Rehman, Rumi Chunara

Population attributes are essential in health for understanding who the data represents and precision medicine efforts.

Domain Adaptation

Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts

no code implementations22 Jun 2018 Nabeel Abdur Rehman, Maxwell Matthaios Aliapoulios, Disha Umarwani, Rumi Chunara

Acute respiratory infections have epidemic and pandemic potential and thus are being studied worldwide, albeit in many different contexts and study formats.

Domain Adaptation Transfer Learning

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