The ranking task is the critical step performed during a recommendation process to predict the top-list of most-wanted products for users. Learn-to-rank algorithms have been developed to refine the ranking process. However, the underrepresentation of some demographic user categories leads to unwanted biased ranking performances that affect the fairness aspects of the recommendation. Bayesian Pairwise Ranking (BPR) is among the most popular ranking algorithms for its important ranking accuracy performance. BPR with machine learning recommendation models can unfairly perform for minority user groups. We tackle the unfairness in the recommendation by proposing the FEBPR method. Our proposal is a fair pairwise Bayesian ranking in which the data debiasing is performed by using adversarial learning fed by enriched embeddings. In our proposal, user and item embeddings are learned to obey the adversarial constraint and mislead the adversary classifier that should not be able to have a priori assumptions about user membership. Extensive experiments are performed on real-world datasets and show that the performances of the proposed debiasing method improve fairness ranking aspects, and therefore the recommendation fairness. It is also shown that our proposal outperforms state-of-the-art fairness ranking methods and presents an interesting tradeoff between the fairness aspects and ranking accuracy.

Recent technological advances rely on accurate decision support systems that can be perceived as black boxes due to their overwhelming complexity. This lack of transparency can lead to technical, ethical, legal, and trust issues. For example, if the control module of a self-driving car failed at detecting a pedestrian, it becomes crucial to know why the system erred. In some other cases, the decision system may reflect unacceptable biases that can generate distrust. The General Data Protection Regulation (GDPR), approved by the European Parliament in 2018, suggests that individuals should be able to obtain explanations of the decisions made from their data by automated processing, and to challenge those decisions. All these reasons have given rise to the domain of interpretable and explainable AI. AIMLAI aims at gathering researchers, experts and professionals, from inside and outside the domain of AI, interested in the topic of interpretable ML and interpretable AI. The workshop encourages interdisciplinary collaborations, with particular emphasis in knowledge management, Infovis, human computer interaction and psychology. It also welcomes applied research for use cases where interpretability matters. AIMLAI envisions to become a discussion venue for the advent of novel interpretable algorithms and explainability modules that mediate the communication between complex ML/AI systems and users.

The Third Workshop on "Advances in Interpretable Machine Learning and Artificial Intelligence" (AIMLAI) presents contributions in the fields of (i) interpretable ML and AI, i.e., algorithms that are natively interpretable, and (ii) interpretability modules, i.e., explanation layers on top of black-box models, also called post-hoc interpretability. AIMLAI encourages interdisciplinary collaborations with particular emphasis in knowledge management, infovis, human computer interaction and psychology. It also welcomes applied research for use cases where interpretability matters.

Linear data transformations constitute essential operations in various machine learning algorithms, ranging from linear regression up to adaptive metric transformation. Often, linear scalings are not only used to improve the model accuracy, rather feature coefficients as provided by the mapping are interpreted as an indicator for the relevance of the feature for the task at hand. This principle, however, can be misleading in particular for high-dimensional or correlated features, since it easily marks irrelevant features as relevant or vice versa. In this contribution, we propose a mathematical formalisation of the minimum and maximum feature relevance for a given linear transformation which can efficiently be solved by means of linear programming. We evaluate the method in several benchmarks, where it becomes apparent that the minimum and maximum relevance closely resembles what is often referred to as weak and strong relevance of the features; hence unlike the mere scaling provided by the linear mapping, it ensures valid interpretability.