The "given data" paradigm undermines both cultures
- URL: http://arxiv.org/abs/2105.12478v1
- Date: Wed, 26 May 2021 11:22:06 GMT
- Title: The "given data" paradigm undermines both cultures
- Authors: Tyler McCormick
- Abstract summary: Data is compelled into a "black box" with an arrow and then catapulted left by a second arrow, having been transformed into an output.
Breiman posits two interpretations of this visual as encapsulating a distinction between two cultures in statistics.
In this comment, I argue for a broader perspective on statistics and, in doing so, elevate questions from "before" and "after" the box as fruitful areas for statistical innovation and practice.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Breiman organizes "Statistical modeling: The two cultures" around a simple
visual. Data, to the far right, are compelled into a "black box" with an arrow
and then catapulted left by a second arrow, having been transformed into an
output. Breiman then posits two interpretations of this visual as encapsulating
a distinction between two cultures in statistics. The divide, he argues is
about what happens in the "black box." In this comment, I argue for a broader
perspective on statistics and, in doing so, elevate questions from "before" and
"after" the box as fruitful areas for statistical innovation and practice.
Related papers
- Does It Make Sense to Explain a Black Box With Another Black Box? [5.377278489623063]
Two main families of counterfactual explanation methods in the literature, namely, (a) emphtransparent methods that perturb the target by adding, removing, or replacing words, and (b) emphopaque approaches that project the target document into a latent, non-interpretable space where the perturbation is carried out subsequently.
Our empirical evidence shows that opaque approaches can be an overkill for downstream applications such as fake news detection or sentiment analysis since they add an additional level of complexity with no significant performance gain.
arXiv Detail & Related papers (2024-04-23T11:40:30Z) - Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in
Large Language Models [89.94270049334479]
This paper identifies a cultural dominance issue within large language models (LLMs)
LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages.
arXiv Detail & Related papers (2023-10-19T05:38:23Z) - Bursting the Burden Bubble? An Assessment of Sharma et al.'s
Counterfactual-based Fairness Metric [0.0]
We show that Burden can show unfairness where statistical parity can not, and that the two metrics can even disagree on which group is treated unfairly.
We conclude that Burden is a valuable metric, but does not replace statistical parity: it rather is valuable to use both.
arXiv Detail & Related papers (2022-11-21T14:54:45Z) - Label-Free Explainability for Unsupervised Models [95.94432031144716]
Unsupervised black-box models are challenging to interpret.
Most existing explainability methods require labels to select which component(s) of the black-box's output to interpret.
We introduce two crucial extensions of post-hoc explanation techniques: (1) label-free feature importance and (2) label-free example importance.
arXiv Detail & Related papers (2022-03-03T18:59:03Z) - The World of an Octopus: How Reporting Bias Influences a Language
Model's Perception of Color [73.70233477125781]
We show that reporting bias negatively impacts and inherently limits text-only training.
We then demonstrate that multimodal models can leverage their visual training to mitigate these effects.
arXiv Detail & Related papers (2021-10-15T16:28:17Z) - Revisiting Rashomon: A Comment on "The Two Cultures" [95.81740983484471]
Breiman dubbed the "Rashomon Effect", describing the situation in which there are many models that satisfy predictive accuracy criteria equally well, but process information in substantially different ways.
This phenomenon can make it difficult to draw conclusions or automate decisions based on a model fit to data.
I make connections to recent work in the Machine Learning literature that explore the implications of this issue.
arXiv Detail & Related papers (2021-04-05T20:51:58Z) - Contrastive Explanations for Model Interpretability [77.92370750072831]
We propose a methodology to produce contrastive explanations for classification models.
Our method is based on projecting model representation to a latent space.
Our findings shed light on the ability of label-contrastive explanations to provide a more accurate and finer-grained interpretability of a model's decision.
arXiv Detail & Related papers (2021-03-02T00:36:45Z) - Bridging Breiman's Brook: From Algorithmic Modeling to Statistical
Learning [6.837936479339647]
In 2001, Leo Breiman wrote of a divide between "data modeling" and "algorithmic modeling" cultures.
Twenty years later this division feels far more ephemeral, both in terms of assigning individuals to camps, and in terms of intellectual boundaries.
We argue that this is largely due to the "data modelers" incorporating algorithmic methods into their toolbox.
arXiv Detail & Related papers (2021-02-23T03:38:41Z) - Local Black-box Adversarial Attacks: A Query Efficient Approach [64.98246858117476]
Adrial attacks have threatened the application of deep neural networks in security-sensitive scenarios.
We propose a novel framework to perturb the discriminative areas of clean examples only within limited queries in black-box attacks.
We conduct extensive experiments to show that our framework can significantly improve the query efficiency during black-box perturbing with a high attack success rate.
arXiv Detail & Related papers (2021-01-04T15:32:16Z) - Breiman's "Two Cultures" Revisited and Reconciled [0.0]
Two cultures of data modeling: parametric statistical and algorithmic machine learning.
The widening gap between "the two cultures" cannot be averted unless we find a way to blend them into a coherent whole.
This article presents a solution by establishing a link between the two cultures.
arXiv Detail & Related papers (2020-05-27T19:02:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.