The awesome potential and promise of Machine Learning and Artificial Intelligence is coming to fruition in the financial services industry, 与 80% of enterprises already investing in AI technology as of 2017. 该行业预计将增长到57美元.到2021年将达到60亿美元, 与 the pledge to provide more accurate and objective judgments to help forecast, 预测和做决定.
As we have seen, data is not objective, but is a product of human design. 我之前的文章 discussed how unconscious biases can creep into the data well before it is fed into a Machine Learning programme. The next logical step is to investigate some of the hidden biases that are amplified by the AI algorithms themselves.
1. 外推法
假设一个系统得到收入数据并决定, 基于那个特定的样本, 男性通常比女性赚得多. If another programme uses this determination to make an eligibility assessment for a small business loan, the algorithm could incorrectly extrapolate that being male is a primary characteristic of succeeding in small business and could disadvantage female loan applicants.
从数据中推断一直都在发生. 根据一篇荒谬的文章, 机器人将取代950人,000 of the 1 million ground and maintenance workers in the US – despite the fact that there is little automation in this space today and certainly no “robots” operationally ready to replace the heavily manual physical tasks. In this case the conclusion was drawn from incorrectly extrapolating data from a University of Oxford employment report, and it also failed to account for the rate at which new technologies create jobs that may not exist. If human analysts can make extrapolation mistakes even when provided 与 context, it’s inevitable that these issues will exist 与in AI programmes.
2. 蝴蝶效应
也被称为“混沌系统”, this is where one small tweak in the data can cause a significant change in the output. 最好的例子就是天气预报, 哪一个有太多不可分割的因素需要考虑, making it nearly impossible to make accurate predictions beyond a few days into the future.
想象一个用于创建经济预测的系统. 即使有大量的整体数据, it will always be difficult for the machine to accurately predict what will happen in the future because unrelated and often subtle events can have a large and unexpected impact on the economy. It would be very easy to take action based on the prediction of a seemingly omnipotent machine, 但在这样做的时候，我们当然应该有所犹豫. Banks need to be able to operate 与 a degree of uncertainty, 因为一个小事件可能会引发巨大的连锁反应. 像Brexit.
Whilst some may suggest that a larger initial training data set will mitigate against this, Nate Silver argues that if there is “an exponential increase in the amount of available information, 同样也有 exponential increase in the number of hypotheses to investigate.” With a minuscule data element having the potential to alter the entire Big Data system, it hinders the ability for machine learning systems to correctly pinpoint the answers they seek – and humans to correctly interpret the output.
3. 相关性vs因果关系
Correlation is merely a relationship between two sets of variables, and this relationship can be caused by three potential factors; pure coincidence, 相互的第三外部因素的影响, 或者一个变量对另一个变量的影响. The big problem occurs when a machine incorrectly interprets correlation as genuine causation, 创建有偏见 反馈回路.
Take a bank that uses its historical data to create an AI program to identify which customers are likely to commit credit fraud. 使用结果, the bank channels more of its funds into investigating these customers, 这样做的时候, 发现更多犯罪. 如果这些数据被反馈到深度学习程序中, it will reinforce its finding that these customers are the ones most likely to cause crime, even though it is quite possible that the higher rates of crime identified are caused by the increased scrutiny. The machine will learn from this feedback in a vicious loop to the detriment of its ability to accurately detect fraud in the future.
机器学习的成功应用
With so much of the focus on the amazing capabilities of AI, it is critical to develop better habits and understanding of data and how deep learning works, 这样算法就能得到适当的准备和训练. Triangulating machine learning outputs 与 customer insight, commonsense and historical data can help mitigate the fallibility of AI. 数据应该用来为决策提供信息, but we need to be conscious that checks and balances are implemented to monitor the success of automation programmes.
In the Information Revolution era, the digitisation of every aspect of industry is only increasing. 无论你是从事农业工作, 医学, 银行, 运输, 建筑或社交媒体, 人工智能和机器学习无处不在. Embracing these digital opportunities is a lynchpin to accelerate progress, but it is important to understand and minimize the data biases that can sour deep learning programmes.
