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Found 43 papers, 17 papers with code
Disparate Model Performance and Stability in Machine Learning Clinical Support for Diabetes and Heart Diseases
Machine Learning (ML) algorithms are vital for supporting clinical decision-making in biomedical informatics.
Understanding and Addressing Gender Bias in Expert Finding Task
To the best of our knowledge, this study is the first to focus on detecting and mitigating gender bias in EF methods.
Responsible AI Research Needs Impact Statements Too
All types of research, development, and policy work can have unintended, adverse consequences - work in responsible artificial intelligence (RAI), ethical AI, or ethics in AI is no exception.
Disparity, Inequality, and Accuracy Tradeoffs in Graph Neural Networks for Node Classification
Graph neural networks (GNNs) are increasingly used in critical human applications for predicting node labels in attributed graphs.
Fairness and Diversity in Information Access Systems
Among the seven key requirements to achieve trustworthy AI proposed by the High-Level Expert Group on Artificial Intelligence (AI-HLEG) established by the European Commission (EC), the fifth requirement ("Diversity, non-discrimination and fairness") declares: "In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system's life cycle.
Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study
We present the results of a 12-week longitudinal user study wherein the participants, 110 subjects from Southern Europe, received on a daily basis Electronic Music (EM) diversified recommendations.
Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers
Queries are written in one of the languages supported by the embeddings, and the extracted sentences can be in any of the other languages supported.
Human Response to an AI-Based Decision Support System: A User Study on the Effects of Accuracy and Bias
We observe that this setting elicits mostly rational behavior from participants, who place a moderate amount of trust in the DSS and show neither algorithmic aversion (under-reliance) nor automation bias (over-reliance). However, their stated willingness to accept the DSS in the exit survey seems less sensitive to the accuracy of the DSS than their behavior, suggesting that users are only partially aware of the (lack of) accuracy of the DSS.
Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways
Hence, we define the problem of reducing the prevalence of radicalization pathways by selecting a small number of edges to "rewire", so to minimize the maximum of segregation scores among all radicalized nodes, while maintaining the relevance of the recommendations.
Diversity in the Music Listening Experience: Insights from Focus Group Interviews
In this study, we interview several listeners about the role that diversity plays in their listening experience, trying to get a better understanding of how they interact with music recommendations.
SciClops: Detecting and Contextualizing Scientific Claims for Assisting Manual Fact-Checking
This paper describes SciClops, a method to help combat online scientific misinformation.
Predicting Early Dropout: Calibration and Algorithmic Fairness Considerations
In this work, the problem of predicting dropout risk in undergraduate studies is addressed from a perspective of algorithmic fairness.
Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics
Shared practices to assess the diversity of retrieval system results are still debated in the Information Retrieval community, partly because of the challenges of determining what diversity means in specific scenarios, and of understanding how diversity is perceived by end-users.
A Note on the Significance Adjustment for FA*IR with Two Protected Groups
In this report we provide an improvement of the significance adjustment from the FA*IR algorithm of Zehlike et al., which did not work for very short rankings in combination with a low minimum proportion $p$ for the protected group.
Social Media Alerts can Improve, but not Replace Hydrological Models for Forecasting Floods
Social media can be used for disaster risk reduction as a complement to traditional information sources, and the literature has suggested numerous ways to achieve this.
Exploring Artist Gender Bias in Music Recommendation
Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i. e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users' specific music preferences.
SciLens News Platform: A System for Real-Time Evaluation of News Articles
We demonstrate the SciLens News Platform, a novel system for evaluating the quality of news articles.
Towards Data-Driven Affirmative Action Policies under Uncertainty
In this paper, we study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs.
Poisoning Attacks on Algorithmic Fairness
Research in adversarial machine learning has shown how the performance of machine learning models can be seriously compromised by injecting even a small fraction of poisoning points into the training data.
Addressing multiple metrics of group fairness in data-driven decision making
The Fairness, Accountability, and Transparency in Machine Learning (FAT-ML) literature proposes a varied set of group fairness metrics to measure discrimination against socio-demographic groups that are characterized by a protected feature, such as gender or race. Such a system can be deemed as either fair or unfair depending on the choice of the metric.
Algorithms for Hiring and Outsourcing in the Online Labor Market
In this paper, we provide algorithms for outsourcing and hiring workers in a general setting, where workers form a team and contribute different skills to perform a task.
Uneven Coverage of Natural Disasters in Wikipedia: the Case of Flood
We also note how coverage of floods in countries with the lowest income, as well as countries in South America, is substantially lower than the coverage of floods in middle-income countries.
EviDense: a Graph-based Method for Finding Unique High-impact Events with Succinct Keyword-based Descriptions
Despite the significant efforts made by the research community in recent years, automatically acquiring valuable information about high impact-events from social media remains challenging.
Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate
Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming.
Modeling Human Annotation Errors to Design Bias-Aware Systems for Social Stream Processing
High-quality human annotations are necessary to create effective machine learning systems for social media.
FairSearch: A Tool For Fairness in Ranked Search Results
We implement two algorithms from the fair ranking literature, namely FA*IR (Zehlike et al., 2017) and DELTR (Zehlike and Castillo, 2018) and provide them as stand-alone libraries in Python and Java.
SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicators
Based on these aspects, we describe a series of indicators of news quality.
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs.
Reducing Disparate Exposure in Ranking: A Learning To Rank Approach
Ranked search results have become the main mechanism by which we find content, products, places, and people online.
Information Retrieval Computers and Society H.3.3
The Effect of Extremist Violence on Hateful Speech Online
In this paper, we focus on quantifying the impact of violent events on various types of hate speech, from offensive and derogatory to intimidation and explicit calls for violence.
Social and Information Networks Computers and Society
FA*IR: A Fair Top-k Ranking Algorithm
In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n >> k candidates, maximizing utility (i. e., select the "best" candidates) subject to group fairness criteria.
The Do's and Don'ts for CNN-based Face Verification
While the research community appears to have developed a consensus on the methods of acquiring annotated data, design and training of CNNs, many questions still remain to be answered.
Deep Heterogeneous Feature Fusion for Template-Based Face Recognition
Thus, in this work, we propose a deep heterogeneous feature fusion network to exploit the complementary information present in features generated by different deep convolutional neural networks (DCNNs) for template-based face recognition, where a template refers to a set of still face images or video frames from different sources which introduces more blur, pose, illumination and other variations than traditional face datasets.
Deep Convolutional Neural Network Features and the Original Image
The results show that the DCNN features contain surprisingly accurate information about the yaw and pitch of a face, and about whether the face came from a still image or a video frame.
UMDFaces: An Annotated Face Dataset for Training Deep Networks
Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets.
A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting
An emerging challenge in the online classification of social media data streams is to keep the categories used for classification up-to-date.
Biconvex Relaxation for Semidefinite Programming in Computer Vision
We propose a general framework to approximately solve large-scale semidefinite problems (SDPs) at low complexity.
Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages
Microblogging platforms such as Twitter provide active communication channels during mass convergence and emergency events such as earthquakes, typhoons.
Triplet Probabilistic Embedding for Face Verification and Clustering
Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem.
Ranked #11 on
Face Verification
on IJB-A
Controversy and Sentiment in Online News
We find that in general, when it comes to controversial issues, the use of negative affect and biased language is prevalent, while the use of strong emotion is tempered.
Engineering Crowdsourced Stream Processing Systems
A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream.
Says who? Automatic Text-Based Content Analysis of Television News
We perform an automatic analysis of television news programs, based on the closed captions that accompany them.
