69 citations · 117 across the 16 of their papers we have counts for
18 papers
Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions
Seyedali Mohammadi, Bhaskara Hanuma Vedula, Hemank Lamba +4
Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlled experiments across mu…
Operationalizing AI for Good: Spotlight on Deployment and Integration of AI Models in Humanitarian Work
Anton Abilov, Ke Zhang, Hemank Lamba +3
Publications in the AI for Good space have tended to focus on the research and model development that can support high-impact applications. However, very few AI for Good papers dis…
NLP for Social Good: A Survey and Outlook of Challenges, Opportunities, and Responsible Deployment
Antonia Karamolegkou, Angana Borah, Eunjung Cho +30
Natural language processing (NLP) now shapes many aspects of our world, yet its potential for positive social impact is underexplored. This paper surveys work in ``NLP for Social G…
CEHA: A Dataset of Conflict Events in the Horn of Africa
Rui Bai, Di Lu, Shihao Ran +5
Natural Language Processing (NLP) of news articles can play an important role in understanding the dynamics and causes of violent conflict. Despite the availability of datasets cat…
Uchaguzi-2022: A Dataset of Citizen Reports on the 2022 Kenyan Election
Roberto Mondini, Neema Kotonya, Robert L. Logan +8
Online reporting platforms have enabled citizens around the world to collectively share their opinions and report in real time on events impacting their local communities. Systemat…
HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid
Hemank Lamba, Anton Abilov, Ke Zhang +8
Humanitarian organizations can enhance their effectiveness by analyzing data to discover trends, gather aggregated insights, manage their security risks, support decision-making, a…
Dissecting users' needs for search result explanations
Prerna Juneja, Wenjuan Zhang, Alison Marie Smith-Renner +3
There is a growing demand for transparency in search engines to understand how search results are curated and to enhance users' trust. Prior research has introduced search result e…
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs
Puxuan Yu, Daniel Cohen, Hemank Lamba +2
In search settings, calibrating the scores during the ranking process to quantities such as click-through rates or relevance levels enhances a system's usefulness and trustworthine…
Counterfactual Editing for Search Result Explanation
Zhichao Xu, Hemank Lamba, Qingyao Ai +2
Search Result Explanation (SeRE) aims to improve search sessions' effectiveness and efficiency by helping users interpret documents' relevance. Existing works mostly focus on factu…
Co-WIN: Really Winning? Analysing Inequity in India's Vaccination Response
Tanvi Karandikar, Avinash Prabhu, Mehul Mathur +3
The COVID-19 pandemic has so far accounted for reported 5.5M deaths worldwide, with 8.7% of these coming from India. The pandemic exacerbated the weakness of the Indian healthcare…
Glowing Experience or Bad Trip? A Quantitative Analysis of User Reported Drug Experiences on Erowid.org
Angelina Mooseder, Momin M. Malik, Hemank Lamba +3
Erowid.org is a website dedicated to documenting information about psychoactive substances, with over 36,000 user-submitted drug Experience Reports. We study the potential of these…
An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings
Hemank Lamba, Kit T. Rodolfa, Rayid Ghani
Applications of machine learning (ML) to high-stakes policy settings -- such as education, criminal justice, healthcare, and social service delivery -- have grown rapidly in recent…
Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy
Kit T. Rodolfa, Hemank Lamba, Rayid Ghani
Growing use of machine learning in policy and social impact settings have raised concerns for fairness implications, especially for racial minorities. These concerns have generated…
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions
Kasun Amarasinghe, Kit Rodolfa, Hemank Lamba +1
Explainability is highly-desired in Machine Learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment. Whil…
Driving The Last Mile: Characterizing and Understanding Distracted Driving Posts on Social Networks
Hemank Lamba, Shashank Srikanth, Dheeraj Reddy Pailla +3
In 2015, 391,000 people were injured due to distracted driving in the US. One of the major reasons behind distracted driving is the use of cell-phones, accounting for 14% of fatal…
Item Recommendation with Evolving User Preferences and Experience
Subhabrata Mukherjee, Hemank Lamba, Gerhard Weikum
Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global ba…
The Many Faces of Link Fraud
Neil Shah, Hemank Lamba, Alex Beutel +1
Most past work on social network link fraud detection tries to separate genuine users from fraudsters, implicitly assuming that there is only one type of fraudulent behavior. But i…
Me, Myself and My Killfie: Characterizing and Preventing Selfie Deaths
Hemank Lamba, Varun Bharadhwaj, Mayank Vachher +3
Over the past couple of years, clicking and posting selfies has become a popular trend. However, since March 2014, 127 people have died and many have been injured while trying to c…