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Found 16 papers, 2 papers with code
From Predictive Algorithms to Automatic Generation of Anomalies
Facing a similar problem -- how to extract theoretical insights from their intuitions -- researchers often turned to ``anomalies:'' constructed examples that highlight flaws in an existing theory and spur the development of new ones.
Language Generation in the Limit
A computational agent is trying to learn to generate from this language; we say that the agent generates from L in the limit if after some finite point in the enumeration of L, the agent is able to produce new elements that come exclusively from L and that have not yet been presented by the adversary.
Characterizing the Value of Information in Medical Notes
Machine learning models depend on the quality of input data.
Quantifying the Causal Effects of Conversational Tendencies
Understanding what leads to effective conversations can aid the design of better computer-mediated communication platforms.
Measuring the Completeness of Theories
We use machine learning to provide a tractable measure of the amount of predictable variation in the data that a theory captures, which we call its "completeness."
The Algorithmic Automation Problem: Prediction, Triage, and Human Effort
In a wide array of areas, algorithms are matching and surpassing the performance of human experts, leading to consideration of the roles of human judgment and algorithmic prediction in these domains.
Discrimination in the Age of Algorithms
But with appropriate requirements in place, the use of algorithms will make it possible to more easily examine and interrogate the entire decision process, thereby making it far easier to know whether discrimination has occurred.
Measuring the Stability of EHR- and EKG-based Predictive Models
In a predictive task, we show that EKG-based models can be more stable than EHR-based models across different patient populations.
A Probabilistic Model of Cardiac Physiology and Electrocardiograms
An electrocardiogram (EKG) is a common, non-invasive test that measures the electrical activity of a patient's heart.
Simplicity Creates Inequity: Implications for Fairness, Stereotypes, and Interpretability
Thus, simplicity transforms disadvantage into bias against the disadvantaged group.
Direct Uncertainty Prediction for Medical Second Opinions
Our central methodological finding is that Direct Uncertainty Prediction (DUP), training a model to predict an uncertainty score directly from the raw patient features, works better than Uncertainty Via Classification, the two-step process of training a classifier and postprocessing the output distribution to give an uncertainty score.
Machine-Learning Tests for Effects on Multiple Outcomes
In this paper we present tools for applied researchers that re-purpose off-the-shelf methods from the computer-science field of machine learning to create a "discovery engine" for data from randomized controlled trials (RCTs).
The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness
Overall, our results indicate that (i) there is a significant amount of structure in this problem that existing models have yet to capture and (ii) there are rich domains in which machine learning may provide a viable approach to testing completeness.
Comparison-Based Choices
In this work we study comparison-based choice functions, a simple but surprisingly rich class of functions capable of exhibiting so-called choice-set effects.
Inherent Trade-Offs in the Fair Determination of Risk Scores
Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for a probabilistic classification to be fair to different groups.
Assessing Human Error Against a Benchmark of Perfection
An increasing number of domains are providing us with detailed trace data on human decisions in settings where we can evaluate the quality of these decisions via an algorithm.
