arXiv research

New algorithms improve hiring decisions by optimizing tiered interview processes.

New algorithm mitigates affinity bias in hiring feedback loops.

The study uses a multi-armed bandit model to analyze and mitigate hiring discrimination.

The persistence of racial inequality in the U.S. labor market against a general backdrop of formal equality of opportunity is a troubling phenomenon that has significant ramifications on the design of hiring policies. In this paper, we show that current group disparate outcomes may be immovable even when hiring decisio…

Study compares employers with and without anticipating strategic labor force responses.

In this paper, we consider a problem of contract theory in which several Principals hire a common Agent and we study the model in the continuous time setting. We show that optimal contracts should satisfy some equilibrium conditions and we reduce the optimisation problem of the Principals to a system of coupled Hamilto…

The paper offers algorithms for managing freelancers and in-house workers in online labor markets.

Modeling bias in evaluation processes using optimization.

A method selects candidates based on predictions with statistical control.

New job recommendation system improves job seekers' welfare through field experiments.

This work facilitates ensuring fairness of machine learning in the real world by decoupling fairness considerations in compound decisions. In particular, this work studies how fairness propagates through a compound decision-making processes, which we call a pipeline. Prior work in algorithmic fairness only focuses on f…

Over the past two decades, the notion of implicit bias has come to serve as an important component in our understanding of discrimination in activities such as hiring, promotion, and school admissions. Research on implicit bias posits that when people evaluate others -- for example, in a hiring context -- their unconsc…

Investigates how FDI and R&D affect host countries' growth.

Strategic Workforce Planning is a company process providing best in class, economically sound, workforce management policies and goals. Despite the abundance of literature on the subject, this is a notorious challenge in terms of implementation. Reasons span from the youth of the field itself to broader data integratio…

A fair policy for hiring candidates from different groups is proposed in a linear contextual bandit problem.

Crowdsourcing provides a popular paradigm for data collection at scale. We study the problem of selecting subsets of workers from a given worker pool to maximize the accuracy under a budget constraint. One natural question is whether we should hire as many workers as the budget allows, or restrict on a small number of …

Talent search and recommendation systems at LinkedIn strive to match the potential candidates to the hiring needs of a recruiter or a hiring manager expressed in terms of a search query or a job posting. Recent work in this domain has mainly focused on linear models, which do not take complex relationships between feat…

New algorithm reduces regret and constraint violation in constrained bandit problems.

User strategization undermines algorithmic trustworthiness.

Study optimizes scoring rules for incentivizing agent's information gathering in online settings.

Model proposes how regulators should oversee complex algorithms in high-stakes applications.

DEEPLY improves cloud service partitioning across multiple datasets and goals.

Detects ghostwriting in high school assignments.

We analyze and quantify, in a financial market with parameter uncertainty and for a Constant Relative Risk Aversion investor, the utility effects of two different boundedly rational (i.e., sub-optimal) investment strategies (namely, myopic and unconditional strategies) and compare them between each other and with the u…

We present a dialogue on Counterparty Credit Risk touching on Credit Value at Risk (Credit VaR), Potential Future Exposure (PFE), Expected Exposure (EE), Expected Positive Exposure (EPE), Credit Valuation Adjustment (CVA), Debit Valuation Adjustment (DVA), DVA Hedging, Closeout conventions, Netting clauses, Collateral …

We consider a contracting problem in which a principal hires an agent to manage a risky project. When the agent chooses volatility components of the output process and the principal observes the output continuously, the principal can compute the quadratic variation of the output, but not the individual components. This…

New framework for fair online allocation in continuous time with deadlines.

This paper optimizes driver repositioning using MARL and reward design for better service and traffic management.

Improvement-aware algorithms can achieve zero error in learning tasks.

The study examines how hyperparameters affect prediction discrepancies in machine learning models.

The paper shows how demographic data can lead to biased predictions, proposing 'Affirmative Information' as a solution.

Large language models correlate in errors, even with different architectures and providers.

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing. In many of these scenarios, previous decisions have been made that are unfairly biased against certain subpopulations, for example those of a …

Threshold tests have recently been proposed as a useful method for detecting bias in lending, hiring, and policing decisions. For example, in the case of credit extensions, these tests aim to estimate the bar for granting loans to white and minority applicants, with a higher inferred threshold for minorities indicative…

New approach to fairness in machine learning using contrastive questions.

Designs algorithms to assist humans without affecting their decisions.

New method reduces labeler costs by aggregating predictions from local classifiers.

Proposes a method to generate counterfactuals for ensemble models using entropic risk measures.

Study evaluates fairness of machine learning models on Kaggle and finds some optimization techniques can induce unfairness.

This paper improves QoS metric prediction in DTNs using diffusion models.

Paper tackles fairness in algorithms by predicting protected class from auxiliary data.

AdaFair boosts fairness in ML models by balancing accuracy and class imbalance.

Rank aggregation based on pairwise comparisons over a set of items has a wide range of applications. Although considerable research has been devoted to the development of rank aggregation algorithms, one basic question is how to efficiently collect a large amount of high-quality pairwise comparisons for the ranking pur…

Measures consistency of tabular LLM predictions under fine-tuning multiplicity.

How do regions acquire the knowledge they need to diversify their economic activities? How does the migration of workers among firms and industries contribute to the diffusion of that knowledge? Here we measure the industry, occupation, and location-specific knowledge carried by workers from one establishment to the ne…

The paper tackles online learning with two types of losses and shows it's impossible without certain assumptions.

Investigates fairness in pipeline models where individuals may drop out.

This paper presents an approach for automation of interpretable feature selection for Internet Of Things Analytics (IoTA) using machine learning (ML) techniques. Authors have conducted a survey over different people involved in different IoTA based application development tasks. The survey reveals that feature selectio…