96 citations · 150 across the 13 of their papers we have counts for
14 papers
Model Selection with Model Zoo via Graph Learning
Ziyu Li, Hilco van der Wilk, Danning Zhan +3
Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i.e., model zoos. Given a new prediction task, finding the best model to fine-tune can be…
Safeguarding Crowdsourcing Surveys from ChatGPT with Prompt Injection
Chaofan Wang, Samuel Kernan Freire, Mo Zhang +7
ChatGPT and other large language models (LLMs) have proven useful in crowdsourcing tasks, where they can effectively annotate machine learning training data. However, this means th…
Metadata Representations for Queryable ML Model Zoos
Ziyu Li, Rihan Hai, Alessandro Bozzon +1
Machine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that ar…
Towards a multi-stakeholder value-based assessment framework for algorithmic systems
Mireia Yurrita, Dave Murray-Rust, Agathe Balayn +1
In an effort to regulate Machine Learning-driven (ML) systems, current auditing processes mostly focus on detecting harmful algorithmic biases. While these strategies have proven t…
Empowering Local Communities Using Artificial Intelligence
Yen-Chia Hsu, Ting-Hao 'Kenneth' Huang, Himanshu Verma +3
Artificial Intelligence (AI) is increasingly used to analyze large amounts of data in various practices, such as object recognition. We are specifically interested in using AI-powe…
Active Hybrid Classification
Evgeny Krivosheev, Fabio Casati, Alessandro Bozzon
Hybrid crowd-machine classifiers can achieve superior performance by combining the cost-effectiveness of automatic classification with the accuracy of human judgment. This paper sh…
Active Learning from Crowd in Document Screening
Evgeny Krivosheev, Burcu Sayin, Alessandro Bozzon +1
In this paper, we explore how to efficiently combine crowdsourcing and machine intelligence for the problem of document screening, where we need to screen documents with a set of m…
Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics
Tim Draws, Nava Tintarev, Ujwal Gadiraju +2
The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints. However, this viewp…
Designing Evaluations of Machine Learning Models for Subjective Inference: The Case of Sentence Toxicity
Agathe Balayn, Alessandro Bozzon
Machine Learning (ML) is increasingly applied in real-life scenarios, raising concerns about bias in automatic decision making. We focus on bias as a notion of opinion exclusion, t…
Unfairness towards subjective opinions in Machine Learning
Agathe Balayn, Alessandro Bozzon, Zoltan Szlavik
Despite the high interest for Machine Learning (ML) in academia and industry, many issues related to the application of ML to real-life problems are yet to be addressed. Here we pu…
A User Modeling Pipeline for Studying Polarized Political Events in Social Media
Roberto Napoli, Ali Mert Ertugrul, Alessandro Bozzon +1
This paper presents a user modeling pipeline to analyze discussions and opinions shared on social media regarding polarized political events (e.g., public polls). The pipeline foll…
Interacting Attention-gated Recurrent Networks for Recommendation
Wenjie Pei, Jie Yang, Zhu Sun +3
Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history…
Chatbots as Conversational Recommender Systems in Urban Contexts
Pavel Kucherbaev, Achilleas Psyllidis, Alessandro Bozzon
In this paper, we outline the vision of chatbots that facilitate the interaction between citizens and policy-makers at the city scale. We report the results of a co-design session…
On the Improvement of Quality and Reliability of Trust Cues in Micro-task Crowdsourcing (Position paper)
Jie Yang, Alessandro Bozzon
Micro-task crowdsourcing has become a successful mean to obtain high-quality data from a large crowd of diverse people. In this context, trust between all the involved actors (i.e.…