Machine learning has recently enabled large advances in artificial intelligence, but these results can be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and maintain them. Published proposals to provide models and data for free for certain tasks include Microsoft Research's Decentralized and Collaborative AI on Blockchain. The framework allows participants to collaboratively build a dataset and use smart contracts to share a continuously updated model on a public blockchain. The initial proposal gave an overview of the framework omitting many details of the models used and the incentive mechanisms in real world scenarios. In this work, we evaluate the use of several models and configurations in order to propose best practices when using the Self-Assessment incentive mechanism so that models can remain accurate and well-intended participants that submit correct data have the chance to profit. We have analyzed simulations for each of three models: Perceptron, Na\"ive Bayes, and a Nearest Centroid Classifier, with three different datasets: predicting a sport with user activity from Endomondo, sentiment analysis on movie reviews from IMDB, and determining if a news article is fake. We compare several factors for each dataset when models are hosted in smart contracts on a public blockchain: their accuracy over time, balances of a good and bad user, and transaction costs (or gas) for deploying, updating, collecting refunds, and collecting rewards. A free and open source implementation for the Ethereum blockchain and simulations written in Python is provided at https://github.com/microsoft/0xDeCA10B. This version has updated gas costs using newer optimizations written after the original publication.

中文翻译：

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区块链去中心化协同人工智能模型分析

机器学习最近使人工智能取得了巨大进步，但这些结果可能是高度集中的。所需的大型数据集通常是专有的；预测通常按查询出售；并且已发布的模型很快就会过时，而无需努力获取更多数据并对其进行维护。已发布的为某些任务免费提供模型和数据的提案包括微软研究院的区块链上的分散和协作人工智能。该框架允许参与者协作构建数据集并使用智能合约在公共区块链上共享不断更新的模型。最初的提案概述了框架，省略了所用模型的许多细节以及现实世界场景中的激励机制。在这项工作中，我们评估了几种模型和配置的使用，以便在使用自我评估激励机制时提出最佳实践，以便模型可以保持准确，并且提交正确数据的善意参与者有机会获利。我们分析了三个模型中每一个模型的模拟：感知器、朴素贝叶斯和最近质心分类器，以及三个不同的数据集：使用来自 Endomondo 的用户活动预测一项运动，来自 IMDB 的电影评论情感分析，以及确定是否新闻文章是假的。当模型托管在公共区块链上的智能合约中时，我们比较每个数据集的几个因素：它们随时间推移的准确性、好坏用户的余额以及部署、更新、收集的交易成本（或gas）退款，并收集奖励。https://github.com/microsoft/0xDeCA10B 提供了以太坊区块链的免费开源实现和用 Python 编写的模拟。此版本使用原始发布后编写的较新优化更新了 gas 成本。