How Open Source Machine Learning Software Shapes AI

被引:10
作者
Langenkamp, Max [1 ]
Yue, Daniel N. [2 ]
机构
[1] MIT, Cambridge, MA 02139 USA
[2] Harvard Sch Business, Cambridge, MA USA
来源
PROCEEDINGS OF THE 2022 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, AIES 2022 | 2022年
关键词
open source; machine learning;
D O I
10.1145/3514094.3534167
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
If we want a future where AI serves a plurality of interests, then we should pay attention to the factors that drive its success. While others have studied the importance of data, hardware, and models in directing the trajectory of AI, we argue that open source software is a neglected factor shaping AI as a discipline. We start with the observation that almost all AI research and applications are built on machine learning open source software (MLOSS). This paper presents three contributions. First, it quantifies the outsized impact of MLOSS by using Github contributions data. By contrasting the costs of MLOSS and its economic benefits, we find that the average dollar of MLOSS investment corresponds to at least $100 of global economic value created, corresponding to $30B of economic value created this year. Second, we leverage interviews with AI researchers and developers to develop a causal model of the effect of open sourcing on economic value. We argue that open sourcing creates value through three primary mechanisms: standardization of MLOSS tools, increased experimentation in AI research, and creation of communities. Finally, we consider the incentives for developing MLOSS and the broader implications of these effects. We intend this paper to be useful for technologists and academics who want to analyze and critique AI, and policymakers who want to better understand and regulate AI systems.
引用
收藏
页码:385 / 395
页数:11
相关论文
共 48 条
[1]  
[Anonymous], 2022, Machine Learning Open Source Software
[2]  
[Anonymous], 2022, INT PLANN C
[3]  
BCG, 2020, Artificial Intelligence and AI at Scale.
[4]  
Brundage M, 2018, Arxiv, DOI arXiv:1802.07228
[5]  
Charmaz K, 2014, Constructing grounded theory., V2nd
[6]  
Dafoe A., 2018, GOVERNANCE RES AGEND
[7]  
Davis R., 1984, The Origin of Rule-Based Systems in AI. Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project
[8]   Co-design and Ethical Artificial Intelligence for Health: Myths and Misconceptions [J].
Donia, Joseph ;
Shaw, Jay .
AIES '21: PROCEEDINGS OF THE 2021 AAAI/ACM CONFERENCE ON AI, ETHICS, AND SOCIETY, 2021, :77-77
[9]  
Dotan R, 2019, Arxiv, DOI arXiv:1912.01172
[10]  
Edge AI + Vision, 2016, The Caffe Deep Learning Framework: An Interview with the Core Developers