Search Results for author: Alessandro Bozzon

Found 11 papers, 2 papers with code

Normalization of Long-tail Adverse Drug Reactions in Social Media

no code implementations EMNLP (Louhi) 2020 Emmanouil Manousogiannis, Sepideh Mesbah, Alessandro Bozzon, Robert-Jan Sips, Zoltan Szlanik, Selene Baez

The automatic mapping of Adverse Drug Reaction (ADR) reports from user-generated content to concepts in a controlled medical vocabulary provides valuable insights for monitoring public health.

Towards a multi-stakeholder value-based assessment framework for algorithmic systems

1 code implementation9 May 2022 Mireia Yurrita, Dave Murray-Rust, Agathe Balayn, Alessandro Bozzon

We, therefore, contribute to current ML auditing practices with an assessment framework that visualizes closeness and tensions between values and we give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.

Empowering Local Communities Using Artificial Intelligence

no code implementations5 Oct 2021 Yen-Chia Hsu, Ting-Hao 'Kenneth' Huang, Himanshu Verma, Andrea Mauri, Illah Nourbakhsh, Alessandro Bozzon

Artificial Intelligence (AI) is increasingly used to analyze large amounts of data in various practices, such as object recognition.

Object Recognition

This Is Not What We Ordered: Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated Topics

no code implementations4 May 2021 Tim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon, Benjamin Timmermans

To better understand the mechanisms underlying SEME, we present a pre-registered, 5 × 3 factorial user study investigating whether order effects (i. e., users adopting the viewpoint pertaining to higher-ranked documents) can cause SEME.

Active Hybrid Classification

no code implementations21 Jan 2021 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.

Active Learning Classification +1

Active Learning from Crowd in Document Screening

no code implementations11 Nov 2020 Evgeny Krivosheev, Burcu Sayin, Alessandro Bozzon, Zoltán Szlávik

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 machine-learning filters.

Active Learning

Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics

no code implementations27 Oct 2020 Tim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon, Benjamin Timmermans

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.

Fairness

Unfairness towards subjective opinions in Machine Learning

no code implementations6 Nov 2019 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.

Designing Evaluations of Machine Learning Models for Subjective Inference: The Case of Sentence Toxicity

no code implementations6 Nov 2019 Agathe Balayn, Alessandro Bozzon

Machine Learning (ML) is increasingly applied in real-life scenarios, raising concerns about bias in automatic decision making.

Decision Making

A User Modeling Pipeline for Studying Polarized Political Events in Social Media

1 code implementation25 Jul 2018 Roberto Napoli, Ali Mert Ertugrul, Alessandro Bozzon, Marco Brambilla

In the scope of this work, our proposed pipeline is applied to two referendum scenarios (independence of Catalonia in Spain and autonomy of Lombardy in Italy) in order to assess the performance of the approach with respect to the capability of collecting correct insights on the demographics of social media users and of predicting the poll results based on the opinions shared by the users.

Social and Information Networks Computers and Society

Interacting Attention-gated Recurrent Networks for Recommendation

no code implementations5 Sep 2017 Wenjie Pei, Jie Yang, Zhu Sun, Jie Zhang, Alessandro Bozzon, David M. J. Tax

In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions.

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