Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth.This article is categorized under:

Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.

Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: Abstract--Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based knowledge bases to improve search over large document repositories. In our view of Information Retrieval on the Semantic Web, a search engine returns documents rather than, or in addition to, exact values in response to user queries. For this purpose, our approach includes an ontology-based scheme for the semiautomatic annotation of documents, and a retrieval system. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with conventional keyword-based retrieval to achieve tolerance to knowledge base incompleteness. Experiments are shown where our approach is tested on corpora of significant scale, showing clear improvements with respect to keyword-based search.

The 'manosphere' has been a recent subject of feminist scholarship on the web. Serious accusations have been levied against it for its role in encouraging misogyny and violent threats towards women online, as well as for potentially radicalising lonely or disenfranchised men. Feminist scholars evidence this through a shift in the language and interests of some men's rights activists on the manosphere, away from traditional subjects of family law or mental health and towards more sexually explicit, violent, racist and homophobic language. In this paper, we study this phenomenon by investigating the flow of extreme language across seven online communities on Reddit, with openly misogynistic members (e.g., Men Going Their Own Way, Involuntarily Celibates), and investigate if and how misogynistic ideas spread within and across these communities. Grounded on feminist critiques of language, we created nine lexicons capturing specific misogynistic rhetoric (Physical Violence, Sexual Violence, Hostility, Patriarchy, Stoicism, Racism, Homophobia, Belittling, and Flipped Narrative) and used these lexicons to explore how language evolves within and across misogynistic groups. This analysis was conducted on 6 million posts, from 300K conversations created between 2011 and December 2018. Our results shows increasing patterns on misogynistic content and users as well as violent attitudes, corroborating existing theories of feminist studies that the amount of misogyny, hostility and violence is steadily increasing in the manosphere.