相关论文: Why am I seeing this: Democratizing End User Audit…
Big Data analytics and Artificial Intelligence systems derive non-intuitive and often unverifiable inferences about individuals' behaviors, preferences, and private lives. Drawing on diverse, feature-rich datasets of unpredictable value,…
Being able to check whether an online advertisement has been targeted is essential for resolving privacy controversies and implementing in practice data protection regulations like GDPR, CCPA, and COPPA. In this paper we describe the…
Recommender systems often struggle with over-specialization, which severely limits users' exposure to diverse content and creates filter bubbles that reduce serendipitous discovery. To address this fundamental limitation, this paper…
Online controlled experiments are the primary tool for measuring the causal impact of product changes in digital businesses. It is increasingly common for digital products and services to interact with customers in a personalised way. Using…
Fairness auditing of AI systems can identify and quantify biases. However, traditional auditing using real-world data raises security and privacy concerns. It exposes auditors to security risks as they become custodians of sensitive…
Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as…
This paper examines artificial intelligence (AI) companionship as a site where intimate relations are simultaneously produced, extracted from, and governed through datafied systems. Drawing on critical data studies and platform studies, we…
User preferences for automated assistance often vary widely, depending on the situation, and quality or presentation of help. Developing effectivemodels to learn individual preferences online requires domain models that associate…
The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the…
Online social network analysis has attracted great attention with a vast number of users sharing information and availability of APIs that help to crawl online social network data. In this paper, we study the research studies that are…
Recent research has shown that the performance of search personalization depends on the richness of user profiles which normally represent the user's topical interests. In this paper, we propose a new embedding approach to learning user…
In the contemporary era, social media has its influence on people in making decisions. The proliferation of online reviews with diversified and verbose content often causes problems inaccurate decision making. Since online reviews have an…
The tech industry has been criticised for designing applications that undermine individuals' autonomy. Recommender systems, in particular, have been identified as a suspected culprit that might exercise unwanted control over peoples' lives.…
We demonstrate that users may be prone to place an inordinate amount of trust in black box algorithms that are framed as intelligent. We deploy an algorithm that purportedly assesses the positivity and negativity of a users' writing…
Facebook uses Artificial Intelligence for targeting users with advertisements based on the events in which they engage like sharing, liking, making comments, posts by a friend, a group creation, etcetera. Each user interacts with these…
These days, due to the increasing amount of information generated on the web, most web service providers try to personalize their services. Users also interact with web-based systems in multiple ways and state their interests and…
Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification risk in such user representations. Our framework, based on…
Short-video platforms rely on personalized recommendation, raising concerns about filter bubbles that narrow content exposure. Auditing such phenomena at scale is challenging because real user studies are costly and privacy-sensitive, and…
Algorithmic agents permeate every instant of our online existence. Based on our digital profiles built from the massive surveillance of our digital existence, algorithmic agents rank search results, filter our emails, hide and show news…
Since recommender systems have been created and developed to automate the recommendation process, users can easily consume their desired video content on online platforms. In this line, several content recommendation algorithms are…