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Found 24 papers, 15 papers with code
Prediction-Powered Ranking of Large Language Models
Our framework is computationally efficient, easy to use, and does not make any assumption about the distribution of human preferences nor about the degree of alignment between the pairwise comparisons by the humans and the strong large language model.
Designing Decision Support Systems Using Counterfactual Prediction Sets
In this context, it has been recently argued that an alternative type of decision support systems may circumvent this challenge.
Human-Aligned Calibration for AI-Assisted Decision Making
Then, the decision maker is supposed to use the confidence value to calibrate how much to trust the prediction.
On the Within-Group Fairness of Screening Classifiers
Screening classifiers are increasingly used to identify qualified candidates in a variety of selection processes.
Counterfactual Inference of Second Opinions
This motivates the design of a set invariant Gumbel-Max structural causal model where the structure of the noise governing the sub-mechanisms underpinning the model depends on an intuitive notion of similarity between experts which can be estimated from data.
Improving Screening Processes via Calibrated Subset Selection
Many selection processes such as finding patients qualifying for a medical trial or retrieval pipelines in search engines consist of multiple stages, where an initial screening stage focuses the resources on shortlisting the most promising candidates.
Improving Expert Predictions with Conformal Prediction
In this work, we develop an automated decision support system that, by design, does not require experts to understand when to trust the system to improve performance.
Counterfactual Temporal Point Processes
This model satisfies a desirable counterfactual monotonicity condition, which is sufficient to identify counterfactual dynamics in the process of thinning.
Consequential Ranking Algorithms and Long-term Welfare
Can we design ranking models that understand the consequences of their proposed rankings and, more importantly, are able to avoid the undesirable ones?
Enhancing the Accuracy and Fairness of Human Decision Making
Societies often rely on human experts to take a wide variety of decisions affecting their members, from jail-or-release decisions taken by judges and stop-and-frisk decisions taken by police officers to accept-or-reject decisions taken by academics.
Teaching Multiple Concepts to a Forgetful Learner
Our framework is both generic, allowing the design of teaching schedules for different memory models, and also interactive, allowing the teacher to adapt the schedule to the underlying forgetting mechanisms of the learner.
Fake News Detection in Social Networks via Crowd Signals
The main objective of our work is to minimize the spread of misinformation by stopping the propagation of fake news in the network.
Social and Information Networks
From Parity to Preference-based Notions of Fairness in Classification
The adoption of automated, data-driven decision making in an ever expanding range of applications has raised concerns about its potential unfairness towards certain social groups.
Cheshire: An Online Algorithm for Activity Maximization in Social Networks
Can we design an algorithm that finds when to incentivize users to take actions to maximize the overall activity in a social network?
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment
To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates.
Modeling the Dynamics of Online Learning Activity
People are increasingly relying on the Web and social media to find solutions to their problems in a wide range of domains.
RedQueen: An Online Algorithm for Smart Broadcasting in Social Networks
Users in social networks whose posts stay at the top of their followers'{} feeds the longest time are more likely to be noticed.
Fairness Constraints: Mechanisms for Fair Classification
Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services.
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it.
Shaping Social Activity by Incentivizing Users
Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network.
Scalable Influence Estimation in Continuous-Time Diffusion Networks
If a piece of information is released from a media site, can it spread, in 1 month, to a million web pages?
Modeling Information Propagation with Survival Theory
Networks provide a skeleton for the spread of contagions, like, information, ideas, behaviors and diseases.
Structure and Dynamics of Information Pathways in Online Media
We assume there is an unobserved dynamic network that changes over time, while we observe the results of a dynamic process spreading over the edges of the network.
Uncovering the Temporal Dynamics of Diffusion Networks
Time plays an essential role in the diffusion of information, influence and disease over networks.
