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Found 20 papers, 5 papers with code
Fairness Incentives in Response to Unfair Dynamic Pricing
We find that, upon deploying a learned tax and redistribution policy, social welfare improves on that of the fairness-agnostic baseline, and approaches that of the analytically optimal fairness-aware baseline for the multi-armed and contextual bandit settings, and surpassing it by 13. 19% in the full RL setting.
From Representational Harms to Quality-of-Service Harms: A Case Study on Llama 2 Safety Safeguards
Despite growing mitigation efforts to develop safety safeguards, such as supervised safety-oriented fine-tuning and leveraging safe reinforcement learning from human feedback, multiple concerns regarding the safety and ingrained biases in these models remain.
Balancing Act: Constraining Disparate Impact in Sparse Models
Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or storage capacities.
Causal Fair Metric: Bridging Causality, Individual Fairness, and Adversarial Robustness
Despite the essential need for comprehensive considerations in responsible AI, factors like robustness, fairness, and causality are often studied in isolation.
Tidying Up the Conversational Recommender Systems' Biases
The growing popularity of language models has sparked interest in conversational recommender systems (CRS) within both industry and research circles.
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces
In this paper, we propose a novel approach that examines the relationship between individual fairness, adversarial robustness, and structural causal models in heterogeneous data spaces, particularly when dealing with discrete sensitive attributes.
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements
The growing utilization of machine learning (ML) in decision-making processes raises questions about its benefits to society.
Privacy-Preserving Fair Item Ranking
Users worldwide access massive amounts of curated data in the form of rankings on a daily basis.
Analyzing the Effect of Sampling in GNNs on Individual Fairness
We evaluate two popular GNN methods: Graph Convolutional Network (GCN), which trains on the entire graph, and GraphSAGE, which uses probabilistic random walks to create subgraphs for mini-batch training, and assess the effects of sub-sampling on individual fairness.
FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks
Empirical evaluation on real-world datasets indicates that FETA is not only able to guarantee fairness on-the-fly at prediction time but also is able to train accurate models exhibiting a much higher degree of individual fairness.
PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning
Group fairness ensures that the outcome of machine learning (ML) based decision making systems are not biased towards a certain group of people defined by a sensitive attribute such as gender or ethnicity.
PrivFair: a Library for Privacy-Preserving Fairness Auditing
Machine learning (ML) has become prominent in applications that directly affect people's quality of life, including in healthcare, justice, and finance.
Post-processing Counterexample-guided Fairness Guarantees in Neural Networks
There is an increasing interest in adopting high-capacity machine learning models such as deep neural networks to semi-automate human decisions.
Counterexample-Guided Learning of Monotonic Neural Networks
Additionally, we propose a technique to use monotonicity as an inductive bias for deep learning.
User Profiling Using Hinge-loss Markov Random Fields
In this paper, we propose a mechanism to infer a variety of user characteristics, such as, age, gender and personality traits, which can then be compiled into a user profile.
Compiling Stochastic Constraint Programs to And-Or Decision Diagrams
In this paper we show how identifying and exploiting these identical subproblems can simplify solving them and leads to a compact representation of the solution.
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
As machine learning is increasingly used to make real-world decisions, recent research efforts aim to define and ensure fairness in algorithmic decision making.
VirtualIdentity: Privacy-Preserving User Profiling
User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies.
Scalable Structure Learning for Probabilistic Soft Logic
We introduce a greedy search-based algorithm and a novel optimization method that trade-off scalability and approximations to the structure learning problem in varying ways.
