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Found 56 papers, 24 papers with code
RecRec: Algorithmic Recourse for Recommender Systems
To the best of our knowledge, this work is the first to conceptualize and empirically test a generalized framework for generating recourses for recommender systems.
Who's Thinking? A Push for Human-Centered Evaluation of LLMs using the XAI Playbook
Deployed artificial intelligence (AI) often impacts humans, and there is no one-size-fits-all metric to evaluate these tools.
Tensions Between the Proxies of Human Values in AI
Motivated by mitigating potentially harmful impacts of technologies, the AI community has formulated and accepted mathematical definitions for certain pillars of accountability: e. g. privacy, fairness, and model transparency.
Networked Restless Bandits with Positive Externalities
Restless multi-armed bandits are often used to model budget-constrained resource allocation tasks where receipt of the resource is associated with an increased probability of a favorable state transition.
Robustness Disparities in Face Detection
Many existing algorithmic audits examine the performance of these systems on later stage elements of facial analysis systems like facial recognition and age, emotion, or perceived gender prediction; however, a core component to these systems has been vastly understudied from a fairness perspective: face detection, sometimes called face localization.
RecXplainer: Amortized Attribute-based Personalized Explanations for Recommender Systems
We evaluate RecXplainer on five real-world and large-scale recommendation datasets using five different kinds of recommender systems to demonstrate the efficacy of RecXplainer in capturing users' preferences over item attributes and using them to explain recommendations.
Interpretable Deep Reinforcement Learning for Green Security Games with Real-Time Information
Green Security Games with real-time information (GSG-I) add the real-time information about the agents' movement to the typical GSG formulation.
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
Our search outputs a suite of models which Pareto-dominate all other high-performance architectures and existing bias mitigation methods in terms of accuracy and fairness, often by large margins, on the two most widely used datasets for face identification, CelebA and VGGFace2.
Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation
Credit is an essential component of financial wellbeing in America, and unequal access to it is a large factor in the economic disparities between demographic groups that exist today.
Measuring Representational Robustness of Neural Networks Through Shared Invariances
Our work offers a new view on robustness by using another reference NN to define the set of perturbations a given NN should be invariant to, thus generalizing the reliance on a reference ``human NN'' to any NN.
On the Generalizability and Predictability of Recommender Systems
By using far more meta-training data than prior work, RecZilla is able to substantially reduce the level of human involvement when faced with a new recommender system application.
Fair Labeled Clustering
To ensure group fairness in such a setting, we would desire proportional group representation in every label but not necessarily in every cluster as is done in group fair clustering.
Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost
We evaluate our results using Dasgupta's cost function, perhaps one of the most prevalent theoretical metrics for hierarchical clustering evaluation.
Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments
Visualizing optimization landscapes has led to many fundamental insights in numeric optimization, and novel improvements to optimization techniques.
The Dichotomous Affiliate Stable Matching Problem: Approval-Based Matching with Applicant-Employer Relations
We also assume the agent's preferences over entire matchings are determined by a general weighted valuation function of their (and their affiliates') matches.
Are Commercial Face Detection Models as Biased as Academic Models?
When we compare the size of these disparities to that of commercial models, we conclude that commercial models - in contrast to their relatively larger development budget and industry-level fairness commitments - are always as biased or more biased than an academic model.
Rawlsian Fairness in Online Bipartite Matching: Two-sided, Group, and Individual
Since fairness has become an important consideration that was ignored in the existing algorithms a collection of online matching algorithms have been developed that give a fair treatment guarantee for one side of the market at the expense of a drop in the operator's profit.
Do Invariances in Deep Neural Networks Align with Human Perception?
One necessary criterion for a network's invariances to align with human perception is for its IRIs look 'similar' to humans.
Robustness Disparities in Commercial Face Detection
Facial detection and analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade.
Fair Clustering Under a Bounded Cost
We derive fundamental lower bounds on the approximation of the utilitarian and egalitarian objectives and introduce algorithms with provable guarantees for them.
Planning to Fairly Allocate: Probabilistic Fairness in the Restless Bandit Setting
Restless and collapsing bandits are often used to model budget-constrained resource allocation in settings where arms have action-dependent transition probabilities, such as the allocation of health interventions among patients.
Pitfalls of Explainable ML: An Industry Perspective
The goal of explainable ML is to intuitively explain the predictions of a ML system, while adhering to the needs to various stakeholders.
A New Notion of Individually Fair Clustering: $α$-Equitable $k$-Center
Clustering is a fundamental problem in unsupervised machine learning, and fair variants of it have recently received significant attention due to its societal implications.
Amortized Generation of Sequential Algorithmic Recourses for Black-box Models
We propose a novel stochastic-control-based approach that generates sequential ARs, that is, ARs that allow x to move stochastically and sequentially across intermediate states to a final state x'.
PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning
In addition, we introduce a new metric to evaluate an auction allocations' adherence to such socially desirable constraints and demonstrate that our proposed method is competitive with current state-of-the-art neural-network based auction designs.
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints
Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning and Operations Research.
Using Inverse Optimization to Learn Cost Functions in Generalized Nash Games
As demonstrated by Ratliff et al. (2014), inverse optimization can be used to recover the objective function parameters of players in multi-player Nash games.
Technical Challenges for Training Fair Neural Networks
As machine learning algorithms have been widely deployed across applications, many concerns have been raised over the fairness of their predictions, especially in high stakes settings (such as facial recognition and medical imaging).
How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?
Our framework measures a network's robustness via the predictive power in its representations -- the test performance of a linear model trained on the learned representations using a small set of clean labels.
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders.
ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning
Inspired by these advances, in this paper, we extend techniques for approximating auctions using deep learning to address concerns of fairness while maintaining high revenue and strong incentive guarantees.
The Affiliate Matching Problem: On Labor Markets where Firms are Also Interested in the Placement of Previous Workers
We model this affiliate matching problem in a generalization of the classic stable marriage setting by permitting firms to state preferences over not just which workers to whom they are matched, but also to which firms their affiliated workers are matched.
A Pairwise Fair and Community-preserving Approach to k-Center Clustering
Clustering is a foundational problem in machine learning with numerous applications.
Kidney Exchange with Inhomogeneous Edge Existence Uncertainty
To the best of our knowledge, the state-of-the-art approaches are only tractable when failure probabilities are identical.
Unifying Model Explainability and Robustness via Machine-Checkable Concepts
Our framework defines a large number of concepts that the DNN explanations could be based on and performs the explanation-conformity check at test time to assess prediction robustness.
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks
Data poisoning and backdoor attacks manipulate training data in order to cause models to fail during inference.
Probabilistic Fair Clustering
In fair clustering problems, vertices are endowed with a color (e. g., membership in a group), and the features of a valid clustering might also include the representation of colors in that clustering.
Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning
In this paper, we argue that traditional notions of fairness that are only based on models' outputs are not sufficient when the model is vulnerable to adversarial attacks.
Adapting a Kidney Exchange Algorithm to Align with Human Values
In kidney exchanges, a central market maker allocates living kidney donors to patients in need of an organ.
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours
Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e. g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver.
Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics
Bias in machine learning has manifested injustice in several areas, such as medicine, hiring, and criminal justice.
Group Fairness in Bandit Arm Selection
We propose a novel formulation of group fairness with biased feedback in the contextual multi-armed bandit (CMAB) setting.
Mix and Match: Markov Chains & Mixing Times for Matching in Rideshare
Rideshare platforms such as Uber and Lyft dynamically dispatch drivers to match riders' requests.
Deep k-NN Defense against Clean-label Data Poisoning Attacks
Targeted clean-label data poisoning is a type of adversarial attack on machine learning systems in which an adversary injects a few correctly-labeled, minimally-perturbed samples into the training data, causing a model to misclassify a particular test sample during inference.
An Algorithm for Multi-Attribute Diverse Matching
Bipartite b-matching, where agents on one side of a market are matched to one or more agents or items on the other, is a classical model that is used in myriad application areas such as healthcare, advertising, education, and general resource allocation.
Making the Cut: A Bandit-based Approach to Tiered Interviewing
Given a huge set of applicants, how should a firm allocate sequential resume screenings, phone interviews, and in-person site visits?
Scalable Robust Kidney Exchange
Transactions in barter exchanges are often facilitated via a central clearinghouse that must match participants even in the face of uncertainty---over participants, existence and quality of potential trades, and so on.
Allocation Problems in Ride-Sharing Platforms: Online Matching with Offline Reusable Resources
Prior work addresses online bipartite matching markets, where agents arrive over time and are dynamically matched to a known set of disposable resources.
The Diverse Cohort Selection Problem
We apply our general algorithm to a real-world problem with combinatorial structure: incorporating diversity into university admissions.
Learning to Schedule Deadline- and Operator-Sensitive Tasks
The use of semi-autonomous and autonomous robotic assistants to aid in care of the elderly is expected to ease the burden on human caretakers, with small-stage testing already occurring in a variety of countries.
Operation Frames and Clubs in Kidney Exchange
A kidney exchange is a centrally-administered barter market where patients swap their willing yet incompatible donors.
Balancing Lexicographic Fairness and a Utilitarian Objective with Application to Kidney Exchange
Balancing fairness and efficiency in resource allocation is a classical economic and computational problem.
Diverse Weighted Bipartite b-Matching
Bipartite matching, where agents on one side of a market are matched to agents or items on the other, is a classical problem in computer science and economics, with widespread application in healthcare, education, advertising, and general resource allocation.
Position-Indexed Formulations for Kidney Exchange
The recent introduction of chains, where a donor without a paired patient triggers a sequence of donations without requiring a kidney in return, increased the efficacy of fielded kidney exchanges---while also dramatically raising the empirical computational hardness of clearing the market in practice.
Hardness of the Pricing Problem for Chains in Barter Exchanges
One of the leading techniques has been branch and price, where column generation is used to incrementally bring cycles and chains into the optimization model on an as-needed basis.
Small Representations of Big Kidney Exchange Graphs
This growth results in more lives saved, but exacerbates the empirical hardness of the $\mathcal{NP}$-complete problem of optimally matching patients to donors.
