Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting

Current AI regulations require discarding sensitive features (e.g., gender, race, religion) in the algorithm's decision-making process to prevent unfair outcomes. However, even without sensitive features in the training set, algorithms can persist in discrimination. Indeed, when sensitive features are omitted (fairness under unawareness), they could be inferred through non-linear relations with the so called proxy features. In this work, we propose a way to reveal the potential hidden bias of a machine learning model that can persist even when sensitive features are discarded. This study shows that it is possible to unveil whether the black-box predictor is still biased by exploiting counterfactual reasoning. In detail, when the predictor provides a negative classification outcome, our approach first builds counterfactual examples for a discriminated user category to obtain a positive outcome. Then, the same counterfactual samples feed an external classifier (that targets a sensitive feature) that reveals whether the modifications to the user characteristics needed for a positive outcome moved the individual to the non-discriminated group. When this occurs, it could be a warning sign for discriminatory behavior in the decision process. Furthermore, we leverage the deviation of counterfactuals from the original sample to determine which features are proxies of specific sensitive information. Our experiments show that, even if the model is trained without sensitive features, it often suffers discriminatory biases

Counterfactual Fair Opportunity: Measuring Decision Model Fairness with Counterfactual Reasoning

The increasing application of Artificial Intelligence and Machine Learning models poses potential risks of unfair behavior and, in light of recent regulations, has attracted the attention of the research community. Several researchers focused on seeking new fairness definitions or developing approaches to identify biased predictions. However, none try to exploit the counterfactual space to this aim. In that direction, the methodology proposed in this work aims to unveil unfair model behaviors using counterfactual reasoning in the case of fairness under unawareness setting. A counterfactual version of equal opportunity named counterfactual fair opportunity is defined and two novel metrics that analyze the sensitive information of counterfactual samples are introduced. Experimental results on three different datasets show the efficacy of our methodologies and our metrics, disclosing the unfair behavior of classic machine learning and debiasing models

UNIBA: Exploiting a Distributional Semantic Model for Disambiguating and Linking Entities in Tweets

ABSTRACT This paper describes the participation of the UNIBA team in the Named Entity rEcognition and Linking (NEEL) Challenge. We propose a knowledge-based algorithm able to recognize and link named entities in English tweets. The approach combines the simple Lesk algorithm with information coming from both a distributional semantic model and usage frequency of Wikipedia concepts. The algorithm performs poorly in the entity recognition, while it achieves good results in the disambiguation step

Special Issue on: Personalisation in E-Government and Smart Cities

The abstract is included in the text

Mining User Interests from Social Media

Social media users readily share their preferences, life events, sentiment and opinions, and implicitly signal their thoughts, feelings, and psychological behavior. This makes social media a viable source of information to accurately and effectively mine users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, (2) techniques that have been adopted or proposed for mining user interests, (3) different evaluation methodologies and benchmark datasets, (4) different applications that have been taking advantage of user interest mining from social media platforms, and (5) existing challenges, open research questions and exciting opportunities for further work

HealthAssistantBot: A Personal Health Assistant for the Italian Language

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Cross-lingual link discovery with TR-ESA

Cross-lingual data linking is the problem of establishing links between resources, such as places, services, or movies, which are described in different languages. In cross-lingual data linking it is often the case that very short descriptions have to be matched, which makes the problem even more challenging. This work presents a method named TRanslation-based Explicit Semantic Analysis (TR-ESA) to represent and match short textual descriptions available in different languages. TR-ESA translates short descriptions in any given language into a pivot language by exploiting a machine translation tool. Then, it generates a Wikipedia-based representation of the translated text by using the Explicit Semantic Analysis technique. The resulting representations are used to match short descriptions in different languages. The method is incorporated in CroSeR (Cross-lingual Service Retrieval), an interactive data linking tool that recommends potential matches to users. We compared results coming from an in-vitro evaluation on a gold standard consisting of five datasets in different languages, with an in-vivo experiment that involved human experts supported by CroSeR. The in-vivo evaluation confirmed the results of the in-vitro evaluation and the overall effectiveness of the proposed method

Cross-language semantic matching for discovering links to e-gov services in the LOD cloud

The large diffusion of e-gov initiatives is increasing the attention of public administrations towards the Open Data initiative. The adoption of open data in the e-gov domain produces different advantages in terms of more transparent government, development of better public services, economic growth and social value. However, the process of data opening should adopt standards and open formats. Only in this way it is possible to share experiences with other service providers, to exploit best practices from other cities or countries, and to be easily connected to the Linked Open Data (LOD) cloud. In this paper we present CroSeR (Cross-language Service Retriever), a tool able to match and retrieve cross-language e-gov services stored in the LOD cloud. The main goal of this work is to help public administrations to connect their e-gov services to services, provided by other administrations, already connected to the LOD cloud. We adopted a Wikipedia-based semantic representation in order to overcome the problems related to match really short textual descriptions associated to the services. A preliminary evaluation on an open catalog of e-gov services showed that the adopted techniques are promising and are more effective than techniques based only on keyword representation