人工智能不完美，算法歧视横行Algorithmic bias is on

AI人工智能的重要分支--机器学习，在利用算法帮助软件使用者更好做出正确决定的同时，也产生了算法歧视，人工智能并非完美。

As the most popular part of artifical Intelligence,machine learning increasingly refers to computer-aided decision making based on statistical algorithms generating data-driven insights.

Companies are moving quickly to applymachine learningto businessdecision making. New programs are constantly being launched, setting complex algorithms to work on large, frequently refreshed data sets. The speed at which this is taking place attests to the attractiveness of the technology, but the lack of experience createsreal risks.

Algorithmic biasis one of the biggest risks because it compromises the very purpose of machine learning. This often-overlooked defect can trigger costly errors and, left unchecked, can pull projects and organizations in entirely wrong directions. Effective efforts to confront this problem at the outset will repay handsomely, allowing the true potential of machine learning to be realized most efficiently.(Source:Mickinsey)

人工智能小知识补充：

KEY WORDS 关键词：

Artificial Intelligence:Enable computers to thinkg 人工智能

Machine Learning:Statistics tools fo learn from data 机器学习

Deep Learning:A technique for implementing Machine Learning, use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. 深度学习

Algorithmic bias：occurs when a computer system behaves in ways that reflects the implicit values of humans involved in that data collection, selection, or use. Algorithmic bias has been identified and critiqued for its impact on search engine results, social media platforms, privacy, and racial profiling. In search results, this bias can create results reflecting racist, sexist, or other social biases, despite the presumed neutrality of the data. The study of algorithmic bias is most concerned with algorithms that reflect"systematic and unfair" discrimination. 算法歧视

(Souce:Wikipedia)

评：关于算法歧视的研究，国内文献相对比较少，要深入了解和控制算法风险，也许只能选择啃英文原版文献！

更深入学习可参考以下报告：

https://www.mckinsey.com/business-functions/risk/our-insights/controlling-machine-learning-algorithms-and-their-biases