8 research outputs found
Fair Adaptive Experiments
Randomized experiments have been the gold standard for assessing the
effectiveness of a treatment or policy. The classical complete randomization
approach assigns treatments based on a prespecified probability and may lead to
inefficient use of data. Adaptive experiments improve upon complete
randomization by sequentially learning and updating treatment assignment
probabilities. However, their application can also raise fairness and equity
concerns, as assignment probabilities may vary drastically across groups of
participants. Furthermore, when treatment is expected to be extremely
beneficial to certain groups of participants, it is more appropriate to expose
many of these participants to favorable treatment. In response to these
challenges, we propose a fair adaptive experiment strategy that simultaneously
enhances data use efficiency, achieves an envy-free treatment assignment
guarantee, and improves the overall welfare of participants. An important
feature of our proposed strategy is that we do not impose parametric modeling
assumptions on the outcome variables, making it more versatile and applicable
to a wider array of applications. Through our theoretical investigation, we
characterize the convergence rate of the estimated treatment effects and the
associated standard deviations at the group level and further prove that our
adaptive treatment assignment algorithm, despite not having a closed-form
expression, approaches the optimal allocation rule asymptotically. Our proof
strategy takes into account the fact that the allocation decisions in our
design depend on sequentially accumulated data, which poses a significant
challenge in characterizing the properties and conducting statistical inference
of our method. We further provide simulation evidence to showcase the
performance of our fair adaptive experiment strategy
Learning Fair Representations with High-Confidence Guarantees
Representation learning is increasingly employed to generate representations
that are predictive across multiple downstream tasks. The development of
representation learning algorithms that provide strong fairness guarantees is
thus important because it can prevent unfairness towards disadvantaged groups
for all downstream prediction tasks. To prevent unfairness towards
disadvantaged groups in all downstream tasks, it is crucial to provide
representation learning algorithms that provide fairness guarantees. In this
paper, we formally define the problem of learning representations that are fair
with high confidence. We then introduce the Fair Representation learning with
high-confidence Guarantees (FRG) framework, which provides high-confidence
guarantees for limiting unfairness across all downstream models and tasks, with
user-defined upper bounds. After proving that FRG ensures fairness for all
downstream models and tasks with high probability, we present empirical
evaluations that demonstrate FRG's effectiveness at upper bounding unfairness
for multiple downstream models and tasks
Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning
Fairness plays a crucial role in various multi-agent systems (e.g.,
communication networks, financial markets, etc.). Many multi-agent dynamical
interactions can be cast as Markov Decision Processes (MDPs). While existing
research has focused on studying fairness in known environments, the
exploration of fairness in such systems for unknown environments remains open.
In this paper, we propose a Reinforcement Learning (RL) approach to achieve
fairness in multi-agent finite-horizon episodic MDPs. Instead of maximizing the
sum of individual agents' value functions, we introduce a fairness function
that ensures equitable rewards across agents. Since the classical Bellman's
equation does not hold when the sum of individual value functions is not
maximized, we cannot use traditional approaches. Instead, in order to explore,
we maintain a confidence bound of the unknown environment and then propose an
online convex optimization based approach to obtain a policy constrained to
this confidence region. We show that such an approach achieves sub-linear
regret in terms of the number of episodes. Additionally, we provide a probably
approximately correct (PAC) guarantee based on the obtained regret bound. We
also propose an offline RL algorithm and bound the optimality gap with respect
to the optimal fair solution. To mitigate computational complexity, we
introduce a policy-gradient type method for the fair objective. Simulation
experiments also demonstrate the efficacy of our approach
Achieving Causal Fairness in Recommendation
Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed systems utilize user-item interaction data to train models and then generate new data by online recommendation. This feedback loop in recommendation often results in various biases in observational data. The goal of this dissertation is to address challenging issues in achieving causal fairness in recommender systems: achieving user-side fairness and counterfactual fairness in bandit-based recommendation, mitigating confounding and sample selection bias simultaneously in recommendation and robustly improving bandit learning process with biased offline data. In this dissertation, we developed the following algorithms and frameworks for research problems related to causal fairness in recommendation. • We developed a contextual bandit algorithm to achieve group level user-side fairness and two UCB-based causal bandit algorithms to achieve counterfactual individual fairness for personalized recommendation; • We derived sufficient and necessary graphical conditions for identifying and estimating three causal quantities under the presence of confounding and sample selection biases and proposed a framework for leveraging the causal bound derived from the confounded and selection biased offline data to robustly improve online bandit learning process; • We developed a framework for discrimination analysis with the benefit of multiple causes of the outcome variable to deal with hidden confounding; • We proposed a new causal-based fairness notion and developed algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort
Achieving Causal Fairness in Recommendation
Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed systems utilize user-item interaction data to train models and then generate new data by online recommendation. This feedback loop in recommendation often results in various biases in observational data. The goal of this dissertation is to address challenging issues in achieving causal fairness in recommender systems: achieving user-side fairness and counterfactual fairness in bandit-based recommendation, mitigating confounding and sample selection bias simultaneously in recommendation and robustly improving bandit learning process with biased offline data. In this dissertation, we developed the following algorithms and frameworks for research problems related to causal fairness in recommendation. • We developed a contextual bandit algorithm to achieve group level user-side fairness and two UCB-based causal bandit algorithms to achieve counterfactual individual fairness for personalized recommendation; • We derived sufficient and necessary graphical conditions for identifying and estimating three causal quantities under the presence of confounding and sample selection biases and proposed a framework for leveraging the causal bound derived from the confounded and selection biased offline data to robustly improve online bandit learning process; • We developed a framework for discrimination analysis with the benefit of multiple causes of the outcome variable to deal with hidden confounding; • We proposed a new causal-based fairness notion and developed algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort
AI augmented Edge and Fog computing: trends and challenges
In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The frontiers of these computing technologies have been boosted by shift from manually encoded algorithms to Artificial Intelligence (AI)-driven autonomous systems for optimum and reliable management of distributed computing resources. Prior work focuses on improving existing systems using AI across a wide range of domains, such as efficient resource provisioning, application deployment, task placement, and service management. This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality of Service (QoS) in the presence of a continuum of resources. We present the latest trends and impact areas such as optimizing AI models that are deployed on or for computing systems. We layout a roadmap for future research directions in areas such as resource management for QoS optimization and service reliability. Finally, we discuss blue-sky ideas and envision this work as an anchor point for future research on AI-driven computing systems