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1. Causal Graph Fuzzing for Fair ML Sofware Development

   https://doi.org/10.1145/3639478.3643530  

   Monjezi, Verya; Kumar, Ashish; Tan, Gang; Trivedi, Ashutosh; Tizpaz-Niari, Saeid (April 2024, ACM)

   Not AvailableMachine learning (ML) is increasingly used in high-stakes areas like autonomous driving, finance, and criminal justice. However, it often unintentionally perpetuates biases against marginalized groups. To address this, the software engineering community has developed fairness testing and debugging methods, establishing best practices for fair ML software. These practices focus on training model design, including the selection of sensitive and non-sensitive attributes and hyperparameter configuration. However, the application of these practices across different socio-economic and cultural contexts is challenging, as societal constraints vary. Our study proposes a search-based software engineering approach to evaluate the robustness of these fairness practices. We formulate these practices as the first-order logic properties and search for two neighborhood datasets where the practice satisfies in one dataset, but fail in the other one. Our key observation is that these practices should be general and robust to various uncertainty such as noise, faulty labeling, and demographic shifts. To generate datasets, we sift to the causal graph representations of datasets and apply perturbations over the causal graphs to generate neighborhood datasets. In this short paper, we show our methodology using an example of predicting risks in the car insurance application. 

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2. Towards Characterizing Domain Counterfactuals for Invertible Latent Causal Models

   Zhou, Zeyu; Bai, Ruqi; Kulinski, Sean; Kocaoglu, Murat; Inouye, David I (May 2024, International Conference on Learning Representations)

   Answering counterfactual queries has important applications such as explainability, robustness, and fairness but is challenging when the causal variables are unobserved and the observations are non-linear mixtures of these latent variables, such as pixels in images. One approach is to recover the latent Structural Causal Model (SCM), which may be infeasible in practice due to requiring strong assumptions, e.g., linearity of the causal mechanisms or perfect atomic interventions. Meanwhile, more practical ML-based approaches using naive domain translation models to generate counterfactual samples lack theoretical grounding and may construct invalid counterfactuals. In this work, we strive to strike a balance between practicality and theoretical guarantees by analyzing a specific type of causal query called domain counterfactuals, which hypothesizes what a sample would have looked like if it had been generated in a different domain (or environment). We show that recovering the latent SCM is unnecessary for estimating domain counterfactuals, thereby sidestepping some of the theoretic challenges. By assuming invertibility and sparsity of intervention, we prove domain counterfactual estimation error can be bounded by a data fit term and intervention sparsity term. Building upon our theoretical results, we develop a theoretically grounded practical algorithm that simplifies the modeling process to generative model estimation under autoregressive and shared parameter constraints that enforce intervention sparsity. Finally, we show an improvement in counterfactual estimation over baseline methods through extensive simulated and image-based experiments. 

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3. An Intersectional Definition of Fairness

   Foulds, James R.; Islam, Rashidul; Keya, Kamrun Naher; Pan, Shimei (April 2020, IEEE International Conference on Data Engineering)

   We propose differential fairness, a multi-attribute definition of fairness in machine learning which is informed by intersectionality, a critical lens arising from the humanities literature, leveraging connections between differential privacy and legal notions of fairness. We show that our criterion behaves sensibly for any subset of the set of protected attributes, and we prove economic, privacy, and generalization guarantees. We provide a learning algorithm which respects our differential fairness criterion. Experiments on the COMPAS criminal recidivism dataset and census data demonstrate the utility of our methods. 

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4. Differential Fairness

   Foulds, James R.; Islam, Rashidul; Keya, Kamrun Naher; Pan, Shimei (December 2019, NeurIPS 2019 Workshop on Machine Learning with Guarantees)

   We propose differential fairness, a multi-attribute definition of fairness in machine learning which is informed by the framework of intersectionality, a critical lens arising from the humanities literature, leveraging connections between differential privacy and legal notions of fairness. We show that our criterion behaves sensibly for any subset of the set of protected attributes, and we prove economic, privacy, and generalization guarantees. We provide a learning algorithm which respects our differential fairness criterion. Experiments on the COMPAS criminal recidivism dataset and census data demonstrate the utility of our methods. 

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5. Robust point-process Granger causality analysis in presence of exogenous temporal modulations and trial-by-trial variability in spike trains

   https://doi.org/10.1371/journal.pcbi.1007675  

   Casile, Antonino; Faghih, Rose T.; Brown, Emery N. (January 2021, PLOS Computational Biology)

   Morrison, Abigail (Ed.)

   Assessing directional influences between neurons is instrumental to understand how brain circuits process information. To this end, Granger causality, a technique originally developed for time-continuous signals, has been extended to discrete spike trains. A fundamental assumption of this technique is that the temporal evolution of neuronal responses must be due only to endogenous interactions between recorded units, including self-interactions. This assumption is however rarely met in neurophysiological studies, where the response of each neuron is modulated by other exogenous causes such as, for example, other unobserved units or slow adaptation processes. Here, we propose a novel point-process Granger causality technique that is robust with respect to the two most common exogenous modulations observed in real neuronal responses: within-trial temporal variations in spiking rate and between-trial variability in their magnitudes. This novel method works by explicitly including both types of modulations into the generalized linear model of the neuronal conditional intensity function (CIF). We then assess the causal influence of neuron i onto neuron j by measuring the relative reduction of neuron j ’s point process likelihood obtained considering or removing neuron i . CIF’s hyper-parameters are set on a per-neuron basis by minimizing Akaike’s information criterion. In synthetic data sets, generated by means of random processes or networks of integrate-and-fire units, the proposed method recovered with high accuracy, sensitivity and robustness the underlying ground-truth connectivity pattern. Application of presently available point-process Granger causality techniques produced instead a significant number of false positive connections. In real spiking responses recorded from neurons in the monkey pre-motor cortex (area F5), our method revealed many causal relationships between neurons as well as the temporal structure of their interactions. Given its robustness our method can be effectively applied to real neuronal data. Furthermore, its explicit estimate of the effects of unobserved causes on the recorded neuronal firing patterns can help decomposing their temporal variations into endogenous and exogenous components. 

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