OpinioNetIt: A Structured and Faceted Knowledge-base of Opinions

Semantic Web technologies and bias in artificial intelligence: A systematic literature review

“…From a statistical point of view, systematic deviations of the, possibly unknown, real distributions of the variables represented in the data can lead to inaccurate estimations and constitute a statistical bias. For example, representation disparities in the data of the users [29], items [2,20,65], or their recorded interactions [85] can compromise the Categories of bias depending on the location in the AI workflow where bias originates [64] Bias location Due to Reference Bias at source External bias [5,6,18,61,67] Functional bias [2,27,29,54,65,74,85] Bias at collection Sampling [34,43,47] Querying [19,76] Data pre-processing Annotation [3,14,17,20,21,24,31,38,39,41,46,51,57] Aggregation [88] Data analysis Inference and prediction [22,53,80] quality and fairness of RS. Searching for information based only on the distributions of a specific dataset can lead to irrelevant results or results biased to other meanings of the words used in the query [19,76].…”

“…The first concerns factors outside the AI system that can influence the reliability and representativeness of the data. For example, the prejudice against specific demographic groups [6,61], or context of a specific political affiliation [5], or community views about particular topics [18,67] may be reflected in the dataset and limit the generalisability of the conclusions that can be drawn from it. The second involves similar limitations due to the design of the AI system.…”

“…Bias assessment Amazon KG [29] Cellosaurus, DrugBank [27] YAGO [5] SentiWordNet [18] prototype [67] FrameNet [57] Wikidata [14] prototype [38] WordNet [17] proprietary [31] SentiWordNet [46] medical KG [51] Bias representation Wikidata [61] prototype [47] MVSO [43] IMAGACT [34] DBpedia [20] TIACRITIS [80] CBOntology [53] CODM [22] Bias mitigation WordNet [6] Wikidata [54] prototype [74] Wikidata [3] DBpedia [2,65] Freebase [85] ConceptNet [88] prototype [24] ConceptNet [21,39,41] DBpedia, WebChild [21] ConceptNet, WordNet [19] prototype [76] in the metadata and normalisation to map every instance to all its possible names. This analysis generated a new resource with unduplicated and normalised data that allows examination across study platforms.…”

“…This study draws attention to the lack of study and the importance of including sex as a study variable in future analyses. Another use of KGs relevant for this task is to assess disparities in the presentation of news reported by different sources, i.e., with different political leaning [5]. This media bias can affect groups or individuals who are part of the story or use these web search systems to build an opinion, since the news is written with the reporter's or media outlet's perception, which can be done, in some cases, partially or unfairly.…”

“…We also emphasise the opportunities of semantics in future human-AI interfaces. We encountered several examples where the structure of KGs could enable addressing bias in the interaction with AI systems, e.g., when using web search engines [5,18,54,74]. Of particular interest is the vision of knowledge graphs as enablers of interactive and exploration-based explainability techniques [38], as integrating humans in the training, testing, and deployment phases of AI is necessary to bring these systems into real-world contexts.…”