FATES 2021 - Third International Workshop on Fairness, Accountability, Transparency, Ethics and Society on the Web

Following the successful editions of FATES in 2019 and 2020, this third edition of the FATES workshop will again promote the discussion around these critical questions and join forces towards a Web that is truly inclusive, transparent and open.
Data is learned from people. Personal data collected from social media and mobile devices, often considered sensitive information, has been extensively used by systems for a number of purposes, including user behavior forecasting, content recommendation and fraud detection. User behavior, in turn, is changing based on the algorithms that users are exposed to. Recent studies have revealed that many machine-learning based systems exhibit biases, including racial and gender bias. This scenario raises new challenges concerning algorithmic fairness and accountability, transparency of machine-learning models, the importance of developing better AI systems on the Web and tools to deal with privacy matters, and ethics on modeling and analyzing online communities, such as social media interactions, mobility data, political engagement networks, healthcare communities, and so on.
The goal of this workshop is to gather researchers and developers from academia, industry, and civil society to present and debate topics of the importance of developing better AI systems on the Web and tools to deal with privacy matters. To achieve this, we will seek contributions that describe research initiatives, projects, results, and design techniques and experiments that are being developed to deal with fairness and accountability, transparency, and ethics on AI and privacy. In this sense, we will encourage submissions in various degrees of progress, such as new results, visions, techniques, innovative application papers, and progress reports.
In this way, we will stimulate an interdisciplinary debate about emerging topics on the Web, creating an open forum for Web researchers, professionals, and industrial practitioners to share evolving knowledge and report ongoing work.
Topics and Themes
* Algorithmic fairness and algorithmic bias, particularly on web data
* Credibility and reputation in social media
* Fairness, accountability, transparency, and ethics in web search and (social) web mining
* Fairness-aware recommender systems and diversity in recommendation
* Ethics of opinion mining and opinion formation on the web
* Ethical models/frameworks around web platforms and data
* Investigation of black-box systems, particular web platforms and algorithms
* Innovative methods for studying/analyzing the fairness, accountability, transparency and ethics of web platforms
* Impact of web platforms and algorithms on employment and the future of work
* Transparency and ethics of web-scale data analysis
* Transparency, fairness, and ethics of crowd-sourcing
* Transparency-aware algorithms for online civic engagement
* Web platforms and the public interest
* Algorithmic fairness and bias for smart cities
* Ethical and privacy aspects in mobility data analysis
* Ethical-aware machine learning models
* Ethics and legal audits on the use of sensitive data
* Evaluation methods for human-centered machine learning
* Fairness Metrics with Human Supervision
* Fairness Warnings
* Fake news, social bots, misinformation, and disinformation on social media
* Hate speech in social media
* Human-centered research for end-user ML
* Human-in-the-loop for privacy-aware machine learning
* Humans perceived consequences of surveillance algorithms
* Information/knowledge design/visualization for Privacy
* Methods and models for Social Computing and Digital Humanities
* Models for ensuring transparency and responsibility of government data
* Privacy-preserving and fairness-aware machine learning on the web
* Search Design for services on the webSocial web mining
* Usability challenges of machine learning
* User Experience (UX) for Privacy
* Design patterns and design research for ML Systems
* Transparency and Explainability in ML