Abstract Ensuring fairness when training Machine Learning (ML) models remains a critical challenge, particularly when biases are embedded in the underlying data. This paper presents a fairness-aware graph structure learning framework demonstrating how learning fair graphs leads to fairer data for ML training and, consequently, fairer Artificial Intelligence (AI) decisioning based on such models. Our method incorporates a fairness regularization term into score-based structure learning algorithms, guiding the search towards graph structures that minimize discriminatory pathways while preserving statistical relationships. The learned fair graph structures enable the generation of synthetic datasets with mitigated biases, which can be used to train diverse ML models. This modification is non-trivial, as structure learning algorithms rely on local search strategies, while fairness is a global property that depends on the entire graph structure. Our framework is highly adaptable, compatible with various structure learning algorithms, and seamlessly incorporates different fairness metrics to meet specific contextual needs. Extensive experiments on both real-world and synthetic datasets demonstrate that our approach significantly improves fairness while maintaining competitive predictive performance, offering an interpretable and versatile solution for mitigating bias in AI systems..

Analysing and learning from sequentially ordered symbolic data is increasingly important in applications such as finance and biology. Finding patterns that show interesting behaviour, such as regularly repeating occurrences within a time interval, can provide useful insight. In this paper we address the problem of efficiently identifying such behaviours. Existing approaches often require a target period to be specified, which will limit possible patterns to those approximating the specified periodicity, such as daily, monthly, quarterly and so on. In this paper we extend one such approach, derived from frequent pattern mining, to operate without the need for user-specified periodicity. Our new algorithms can identify the time interval and periodicity, or frequency of occurrence, of all periodically occurring patterns within a certain used-specified tolerance. Experimental results of our implementation show that the new approach can identify many more patterns in a real-world financial dataset, while on other sequential datasets it finds similar numbers of patterns without significant reduction in efficiency compared to existing approaches. We also verify the algorithm’s ability to recover recurring patterns in controlled experiments on synthetic data.

Users of online social networks such as dating websites often need help to find successful matches. People-to-people recommender systems can be used in social networks to help users find better matches, which requires solving the problem of reciprocal recommendation. However, most existing reciprocal recommenders use either profile similarity or interaction similarity to recommend new matches, without considering temporal features. In this paper we introduce a method for temporal reciprocal recommender systems using Hidden Markov Models to generate recommendations. Instead of summarising the whole historical data in one past state, we propose a model that formalises historical data on interactions as a series of successive states changing over time and then tries to find the recommended next state. We have implemented this new approach and the results of testing on industrial-scale data from a real dating website show a noticeable improvement over the previous best-performing recommenders.

Predicting people who other people may like has recently become an important task in many online social networks. Traditional collaborative filtering (CF) approaches are popular in recommender systems to effectively predict user preferences for items. One major problem in CF is computing similarity between users or items. Traditional CF methods often use heuristic methods to combine the ratings given to an item by similar users, which may not reflect the characteristics of the active user and can give unsatisfactory performance. In contrast to heuristic approaches we have developed CollabNet, a novel algorithm that uses gradient descent to learn the relative contributions of similar users or items to the ranking of recommendations produced by a recommender system, using weights to represent the contributions of similar users for each active user. We have applied CollabNet to the challenging problem of people to people recommendation in social networks, where people have a dual role as both "users" and "items", e.g., both initiating and receiving communications, to recommend other users to a given user, based on user similarity in terms of both taste (whom they like) and attractiveness (who likes them). Evaluation of CollabNet recommendations on datasets from a commercial online social network shows improved performance over standard CF.