Toward Practices for Human-Centered Machine Learning

被引：9

作者：

Chancellor, Stevie ^{[1
]}

机构：

[1] Univ Minnesota, Dept Comp Sci & Engn, Minneapolis, MN 55455 USA

来源：

COMMUNICATIONS OF THE ACM | 2022年 / 66卷 / 03期

关键词：

All Open Access; Bronze;

D O I：

10.1145/3530987

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

MACHINE LEARNING (ML) has been described as a modern Oracle of Delphi-A way to quickly solve problems in different domains, whether auto-completing email messages or predicting the presence of malignant tumors. Computer scientists are theorizing and designing ML technology into our social and personal lives. ML has justifiably stirred tremendous excitement in research, industry, and the popular zeitgeist of artificial intelligence (AI). However, the enthusiastic adoption of ML has also had negative consequences. ML is being used for unsavory and controversial purposes, such as generating "deep fake"videos and reproducing facial discrimination.8,41 On the research side, new research points to worrisome trends of chasing metrics over more principled approaches and questionable gains in deep learning's performance compared to linear models.10,17,30 As a researcher working in applied machine learning for mental health, I have seen. © 2023 ACM.

引用

页码：78 / 85

页数：8

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