インシデント 61の引用情報

Description: In the “The Nature Conservancy Fisheries Monitoring” competition on the data science competition website Kaggle, a number of competitors overfit their image classifier models to a poorly representative validation data set.
推定: Individual Kaggle Competitorsが開発し提供したAIシステムで、Individual Kaggle Competitorsに影響を与えた

インシデントのステータス

インシデントID
61
レポート数
1
インシデント発生日
2017-05-01
エディタ
Sean McGregor

CSETv0 分類法のクラス

分類法の詳細

Full Description

On the data science competition website Kaggle, a number of competitors in the “The Nature Conservancy Fisheries Monitoring” competition overfit their image classifier models to a poorly representative validation data set. This resulted in intermediate competition rankings that were misleading and discouraged other data scientists from competing. Outside of the competition environment it would not have been clear that this error had taken place.

Short Description

In the “The Nature Conservancy Fisheries Monitoring” competition on the data science competition website Kaggle, a number of competitors overfit their image classifier models to a poorly representative validation data set.

Severity

Negligible

Harm Distribution Basis

Religion

AI System Description

Image classifer models designed by individual competitors on Kaggle.

System Developer

Individual Kaggle Competitors

Sector of Deployment

Public administration and defence

Relevant AI functions

Perception

AI Techniques

supervised learning, machine learning, DNN, VGG, open-source

AI Applications

Feature detection, Image classification, Decision support

Location

Global

Named Entities

Kaggle, The Nature Conservancy

Technology Purveyor

Kaggle Competitors

Beginning Date

2016-11-14T08:00:00.000Z

Ending Date

2017-04-12T07:00:00.000Z

Near Miss

Near miss

Intent

Accident

Lives Lost

No

Data Inputs

Images captured on fishing boats

インシデントレポート

What I’ve learned from Kaggle’s fisheries competition
medium.com · 2017

What I’ve learned from Kaggle’s fisheries competition

Gidi Shperber Blocked Unblock Follow Following May 1, 2017

TLDR:

Me and my Kaggle partner, have recently participated in “The Nature Conservancy Fisheries Monitoring” (hereby: “fisheries…

バリアント

「バリアント」は既存のAIインシデントと同じ原因要素を共有し、同様な被害を引き起こし、同じ知的システムを含んだインシデントです。バリアントは完全に独立したインシデントとしてインデックスするのではなく、データベースに最初に投稿された同様なインシデントの元にインシデントのバリエーションとして一覧します。インシデントデータベースの他の投稿タイプとは違い、バリアントではインシデントデータベース以外の根拠のレポートは要求されません。詳細についてはこの研究論文を参照してください

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