Fuzzy bilateral matchmaking in e-marketplaces

We present a novel Fuzzy Description Logic (DL) based approach to automate matchmaking in e-marketplaces. We model traders’ preferences with the aid of Fuzzy DLs and, given a request, use utility values computed w.r.t. Pareto agreements to rank a set of offers. In particular, we introduce an expressive Fuzzy DL, extended with concrete domains in order to handle numerical, as well as non numerical features, and to deal with vagueness in buyer/seller preferences. Hence, agents can express preferences as e.g., I am searching for a passenger car costing about 22000e yet if the car has a GPS system and more than two-year warranty I can spend up to 25000e. Noteworthy our matchmaking approach, among all the possible matches, chooses the mutually beneficial ones

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

Aspect-based Sentiment Analysis of Scientific Reviews

Scientific papers are complex and understanding the usefulness of these papers requires prior knowledge. Peer reviews are comments on a paper provided by designated experts on that field and hold a substantial amount of information, not only for the editors and chairs to make the final decision, but also to judge the potential impact of the paper. In this paper, we propose to use aspect-based sentiment analysis of scientific reviews to be able to extract useful information, which correlates well with the accept/reject decision. While working on a dataset of close to 8k reviews from ICLR, one of the top conferences in the field of machine learning, we use an active learning framework to build a training dataset for aspect prediction, which is further used to obtain the aspects and sentiments for the entire dataset. We show that the distribution of aspect-based sentiments obtained from a review is significantly different for accepted and rejected papers. We use the aspect sentiments from these reviews to make an intriguing observation, certain aspects present in a paper and discussed in the review strongly determine the final recommendation. As a second objective, we quantify the extent of disagreement among the reviewers refereeing a paper. We also investigate the extent of disagreement between the reviewers and the chair and find that the inter-reviewer disagreement may have a link to the disagreement with the chair. One of the most interesting observations from this study is that reviews, where the reviewer score and the aspect sentiments extracted from the review text written by the reviewer are consistent, are also more likely to be concurrent with the chair's decision.Comment: Accepted in JCDL'2

Reviewing peer review: a quantitative analysis of peer review

In this paper we focus on the analysis of peer reviews and reviewers behavior in a number of different review processes. More specifically, we report on the development, definition and rationale of a theoretical model for peer review processes to support the identification of appropriate metrics to assess the processes main properties. We then apply the proposed model and analysis framework to data sets from conference evaluation processes and we discuss the results implications and their eventual use toward improving the analyzed peer review processes. A number of unexpected results were found, in particular: (1) the low correlation between peer review outcome and impact in time of the accepted contributions and (2) the presence of an high level of randomness in the analyzed peer review processes

Is peer review any good? A quantitative analysis of peer review

In this paper we focus on the analysis of peer reviews and reviewers behavior in conference review processes. We report on the development, definition and rationale of a theoretical model for peer review processes to support the identification of appropriate metrics to assess the processes main properties. We then apply the proposed model and analysis framework to data sets about reviews of conference papers. We discuss in details results, implications and their eventual use toward improving the analyzed peer review processes. Conclusions and plans for future work close the paper