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Found 11 papers, 1 papers with code
Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform Action
We show that the risk scores are, in fact, useful, enabling a theoretical team of caseworkers to reach more eviction-prone properties in the same amount of time, compared to outreach policies that are either neighborhood-based or focus on buildings with a recent history of evictions.
Discretionary Trees: Understanding Street-Level Bureaucracy via Machine Learning
When they do apply discretion to assign households to more intensive interventions, the marginal benefits to those households are significantly higher than would be expected if the households were chosen at random; there is no similar reduction in marginal benefit to households that are discretionarily allocated less intensive interventions, suggesting that caseworkers are improving outcomes using their knowledge.
Clinical Risk Prediction Using Language Models: Benefits And Considerations
Previous research has addressed this data limitation by incorporating medical ontologies and employing transfer learning methods.
GenSyn: A Multi-stage Framework for Generating Synthetic Microdata using Macro Data Sources
Our method combines the estimation of a dependency graph and conditional probabilities from the target location with the use of a Gaussian copula to leverage the available information from the auxiliary locations.
Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents
These conditions are related to the the way in which the fair classifier remedies unfairness on the original unmanipulated data: fair classifiers which remedy unfairness by becoming more selective than their conventional counterparts are the ones that become less fair than their counterparts when agents are strategic.
Local Justice and the Algorithmic Allocation of Societal Resources
AI is increasingly used to aid decision-making about the allocation of scarce societal resources, for example housing for homeless people, organs for transplantation, and food donations.
Incentivizing Truthfulness Through Audits in Strategic Classification
While this policy can, in general, be hard to compute because of the difficulty of identifying the set of agents who could benefit from lying given a complete set of reported types, we also present necessary and sufficient conditions under which it is tractable.
Multiagent Systems Computer Science and Game Theory
Deception through Half-Truths
Deception is a fundamental issue across a diverse array of settings, from cybersecurity, where decoys (e. g., honeypots) are an important tool, to politics that can feature politically motivated "leaks" and fake news about candidates. Typical considerations of deception view it as providing false information. However, just as important but less frequently studied is a more tacit form where information is strategically hidden or leaked. We consider the problem of how much an adversary can affect a principal's decision by "half-truths", that is, by masking or hiding bits of information, when the principal is oblivious to the presence of the adversary.
Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents
A market scoring rule (MSR) – a popular tool for designing algorithmic prediction markets – is an incentive-compatible mechanism for the aggregation of probabilistic beliefs from myopic risk-neutral agents.
How to show a probabilistic model is better
We present a simple theoretical framework, and corresponding practical procedures, for comparing probabilistic models on real data in a traditional machine learning setting.
Adapting to a Market Shock: Optimal Sequential Market-Making
We study the profit-maximization problem of a monopolistic market-maker who sets two-sided prices in an asset market.
