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Bracha Shapira

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7 papers
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7

ECAI Conference 2025 Conference Paper

DiFair-LLM: Evaluating Fairness Disparities in LLMs Toward Demographic Groups

  • Nurit Cohen-Inger
  • Roei Zaady
  • Adir Solomon
  • Lior Rokach
  • Bracha Shapira

Large Language Models (LLMs) are increasingly integrated into real-world applications, making equitable treatment of all demographic groups a critical concern. Existing fairness evaluations often rely on binary, template-based tests, which overlook subtle disparities in open-ended responses. We present DiFair-LLM, a model-agnostic framework for detecting and quantifying fairness disparities - any unequal treatment that benefits or disadvantages a demographic group. DiFair-LLM uses open-ended, group-specific and neutral prompts, measures semantic distances between groups’ responses, applies non-parametric statistical tests, and ranks groups by deviation from a neutral baseline. Evaluations across eight state-of-the-art LLMs and multiple demographic attributes reveal minimal disparities for gender but significant differences for age, especially older adults, and ethnicity, with the largest gaps affecting certain non-Caucasian groups. By mapping nuanced patterns of differential treatment rather than flagging only overt bias, DiFair-LLM offers a practical, reproducible approach for auditing fairness and guiding more inclusive LLM deployments.

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ECAI Conference 2024 Conference Paper

FairUS - UpSampling Optimized Method for Boosting Fairness

  • Nurit Cohen-Inger
  • Guy Rozenblatt
  • Seffi Cohen
  • Lior Rokach
  • Bracha Shapira

The increasing application of machine learning (ML) in critical areas such as healthcare and finance highlights the importance of fairness in ML models, challenged by biases in training data that can lead to discrimination. We introduce ‘FairUS’, a novel pre-processing method for reducing bias in ML models utilizing the Conditional Generative Adversarial Network (CTGAN) to synthesize upsampled data. Unlike traditional approaches that focus solely on balancing subgroup sample sizes, FairUS strategically optimizes the quantity of synthesized data. This optimization aims to achieve an ideal balance between enhancing fairness and maintaining the overall performance of the model. Extensive evaluations of our method over several canonical datasets show that the proposed method enhances fairness by 2. 7 times more than the related work and 4 times more than the baseline without mitigation, while preserving the performance of the ML model. Moreover, less than a third of the amount of synthetic data was needed on average. Uniquely, the proposed method enables decision-makers to choose the working point between improved fairness and model’s performance according to their preferences.

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AAAI Conference 2022 Conference Paper

Q-Ball: Modeling Basketball Games Using Deep Reinforcement Learning

  • Chen Yanai
  • Adir Solomon
  • Gilad Katz
  • Bracha Shapira
  • Lior Rokach

Basketball is one of the most popular types of sports in the world. Recent technological developments have made it possible to collect large amounts of data on the game, analyze it, and discover new insights. We propose a novel approach for modeling basketball games using deep reinforcement learning. By analyzing multiple aspects of both the players and the game, we are able to model the latent connections among players’ movements, actions, and performance, into a single measure – the Q-Ball. Using Q-Ball, we are able to assign scores to the performance of both players and whole teams. Our approach has multiple practical applications, including evaluating and improving players’ game decisions and producing tactical recommendations. We train and evaluate our approach on a large dataset of National Basketball Association games, and show that the Q-Ball is capable of accurately assessing the performance of players and teams. Furthermore, we show that Q-Ball is highly effective in recommending alternatives to players’ actions.

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TIST Journal 2016 Journal Article

Anytime Algorithms for Recommendation Service Providers

  • David Ben-Shimon
  • Lior Rokach
  • Guy Shani
  • Bracha Shapira

Recommender systems (RS) can now be found in many commercial Web sites, often presenting customers with a short list of additional products that they might purchase. Many commercial sites do not typically have the ability and resources to develop their own system and may outsource the RS to a third party. This had led to the growth of a recommendation as a service industry, where companies, referred to as RS providers, provide recommendation services. These companies must carefully balance the cost of building recommendation models and the payment received from the e-business, as these payments are expected to be low. In such a setting, restricting the computational time required for model building is critical for the RS provider to be profitable. In this article, we propose anytime algorithms as an attractive method for balancing computational time and the recommendation model performance, thus tackling the RS provider problem. In an anytime setting, an algorithm can be stopped after any amount of computational time, always ensuring that a valid, although suboptimal, solution will be returned. Given sufficient time, however, the algorithm should converge to an optimal solution. In this setting, it is important to evaluate the quality of the returned solution over time, monitoring quality improvement. This is significantly different from traditional evaluation methods, which mostly estimate the performance of the algorithm only after its convergence is given sufficient time. We show that the popular item-item top-N recommendation approach can be brought into the anytime framework by smartly considering the order by which item pairs are being evaluated. We experimentally show that the time-accuracy trade-off can be significantly improved for this specific problem.

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IS Journal 2007 Journal Article

MarCol: A Market-Based Recommender System

  • Dan Melamed
  • Bracha Shapira
  • Yuval Elovici

Collaborative information-filtering systems maintain user judgments on the relevance of data items. These systems recommend relevant information to other users on the basis of similarity between user profiles or recommended items. This market-based collaborative information-filtering system employs a pricing mechanism to motivate users to provide judgments. Results show that the model increases feedback and improves recommendation quality. MarCol uses Google as the underlying search engine. It stores all user logs, including queries and judgments.

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