1,720,981 research outputs found
Technological spillovers and productivity in Italian manufacturing firms
No full textWe study whether a firm's total factor productivity dynamics is positively influenced by its own R&D activity and by the technological spillovers generated at the intra- and inter-sectorial level. Our approach corrects simultaneously for the endogeneity and the selectivity biases introduced by the use of a firm's own R&D as a regressor. The evidence suggests that a firm's involvement in R&D activities accounts for significant productivity gains. Firms also benefit from spillovers originating from their own industries, as well as from innovative upstream sectors. © 2013 Springer Science+Business Media New York
Government Size and the Composition of Public Spending in a Neoclassical Growth Model
No full textThis paper develops a non-linear theoretical relationship between public spending and economic growth. The model identifies the “optimal” size of government and the “optimal” composition of government spending. Given the size of the government, different allocations of public resources lead to different growth rates in the transition dynamics, depending on their elasticity. We argue that neglecting the hypothesis of non-linearity and the different impact different kinds of public spending have on economic performance results in models which suffer from mis-specification. Traditional linear regression analysis may thus be biased
A Neoclassical Growth Model with Public Spending
No full textThis paper analyses the effect of public expenditures in the context of a modified Solow model of capital accumulation with optimising agents. The model identifies optimal government size and optimal composition of public expenditures which maximize the rate of growth in the dynamics to the steady state and maximize the long run level of per capita income. Different allocations of public resources lead to different growth rates in the transitional dynamics depending on their elasticity. However effects from fiscal policy are only temporary and disappear in the steady state. Finally we argue that neglecting the non-linear nature of the relationship between government spending and growth may lead empirical studies to biased results
Causal reasoning for algorithmic fairness in voice controlled cyber-physical systems
Full text linkAutomated speaker recognition is enabling personalized interactions with the voice-based interfaces and assistants part of the modern cyber-physical-social systems. Prior studies have unfortunately uncovered disparate impacts across demographic groups on the outcomes of speaker recognition systems and consequently proposed a range of countermeasures. Understanding why a speaker recognition system may lead to this disparate performance for different (groups of) individuals, going beyond mere data imbalance reasons and black-box countermeasures, is an essential yet under-explored perspective. In this paper, we propose an explanatory framework that aims to provide a better understanding of how speaker recognition models perform as the underlying voice characteristics on which they are tested change. With our framework, we evaluate two state-of-the-art speaker recognition models, comparing their fairness in terms of security, through a systematic analysis of the impact of more than twenty voice characteristics. Our findings include important takeaways to enable voice controlled cyber-physical-social systems for everyone. Source code and data are available at https://bit.ly/EA-PRLETTERS
Improving Fairness in Speaker Recognition
No full textThe human voice conveys unique characteristics of an individual, making voice biometrics a key technology for verifying identities in various industries. Despite the impressive progress of speaker recognition systems in terms of accuracy, a number of ethical and legal concerns has been raised, specifically relating to the fairness of such systems. In this paper, we aim to explore the disparity in performance achieved by state-of-the-art deep speaker recognition systems, when different groups of individuals characterized by a common sensitive attribute (e.g., gender) are considered. In order to mitigate the unfairness we uncovered by means of an exploratory study, we investigate whether balancing the representation of the different groups of individuals in the training set can lead to a more equal treatment of these demographic groups. Experiments on two state-of-the-art neural architectures and a large-scale public dataset show that models trained with demographically-balanced training sets exhibit a fairer behavior on different groups, while still being accurate. Our study is expected to provide a solid basis for instilling beyond-accuracy objectives (e.g., fairness) in speaker recognition
Fair Augmentation for Graph Collaborative Filtering
No full textRecent developments in recommendation have harnessed the collaborative power of graph neural networks (GNNs) in learning users’ preferences from user-item networks. Despite emerging regulations addressing fairness of automated systems, unfairness issues in graph collaborative filtering remain underexplored, especially from the consumer’s perspective. Despite numerous contributions on consumer unfairness, only a few of these works have delved into GNNs. A notable gap exists in the formalization of the latest mitigation algorithms, as well as in their effectiveness and reliability on cutting-edge models. This paper serves as a solid response to recent research highlighting unfairness issues in graph collaborative filtering by reproducing one of the latest mitigation methods. The reproduced technique adjusts the system fairness level by learning a fair graph augmentation. Under an experimental setup based on 11 GNNs, 5 non-GNN models, and 5 real-world networks across diverse domains, our investigation reveals that fair graph augmentation is consistently effective on high-utility models and large datasets. Experiments on the transferability of the fair augmented graph open new issues for future recommendation studies. Source code: https://github.com/jackmedda/FA4GCF
Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems
No full textIn recommendation literature, explainability and fairness are becoming two prominent perspectives to consider. However, prior works have mostly addressed them separately, for instance by explaining to consumers why a certain item was recommended or mitigating disparate impacts in recommendation utility. None of them has leveraged explainability techniques to inform unfairness mitigation. In this paper, we propose an approach that relies on counterfactual explanations to augment the set of user-item interactions, such that using them while inferring recommendations leads to fairer outcomes. Modeling user-item interactions as a bipartite graph, our approach augments the latter by identifying new user-item edges that not only can explain the original unfairness by design, but can also mitigate it. Experiments on two public data sets show that our approach effectively leads to a better trade-off between fairness and recommendation utility compared with state-of-the-art mitigation procedures. We further analyze the characteristics of added edges to highlight key unfairness patterns. Source code available at https://github.com/jackmedda/RS-BGExplainer/tree/cikm2023
Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations
No full textEnabling non-discrimination for end-users of recommender systems by introducing consumer fairness is a key problem, widely studied in both academia and industry. Current research has led to a variety of notions, metrics, and unfairness mitigation procedures. The evaluation of each procedure has been heterogeneous and limited to a mere comparison with models not accounting for fairness. It is hence hard to contextualize the impact of each mitigation procedure w.r.t. the others. In this paper, we conduct a systematic analysis of mitigation procedures against consumer unfairness in rating prediction and top-n recommendation tasks. To this end, we collected 15 procedures proposed in recent top-tier conferences and journals. Only 8 of them could be reproduced. Under a common evaluation protocol, based on two public data sets, we then studied the extent to which recommendation utility and consumer fairness are impacted by these procedures, the interplay between two primary fairness notions based on equity and independence, and the demographic groups harmed by the disparate impact. Our study finally highlights open challenges and future directions in this field. The source code is available at https://github.com/jackmedda/C-Fairness-RecSys
Consumer Fairness Benchmark in Recommendation
Full text linkSeveral mitigation procedures have emerged to address consumer unfairness in personalized rankings. However, evaluating their performance is difficult due to variations in experimental protocols, such as differing fairness definitions, data sets, evaluation metrics, and sensitive attributes. This makes it challenging for scientists to choose a suitable procedure for their practical setting. In this paper, we summarize our previous work on investigating the properties a given mitigation procedure against consumer unfairness should be evaluated on. To this end, we defined eight technical properties and leveraged two public datasets to evaluate the extent to which existing mitigation procedures against consumer unfairness met these properties. Source code and data: https://github.com/jackmedda/Perspective-C-Fairness-RecSys
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