When AI was first introduced into business processes, it was transformative, enabling companies to leverage the vast amounts of accumulated data to improve planning and decision making. It soon became apparent, however, that integrating AI into business processes at scale required significant resources. First, companies had to recruit highly sought-after (and highly paid) data scientists to create the data models behind AI. Second, the process of building and training the machine learning models that accelerated the data analysis process required a significant expenditure of time and energy. This, in turn, led to the development of automated machine learning (AutoML), techniques that essentially automate core aspects of the machine learning process including model selection, training, and evaluation.
In effect, AutoML seeks to trade machine (processing) time for human time. This automation brings many benefits. First and foremost, it decreases labor costs. It also reduces human error, automates repetitive tasks, and enables the development of more effective models. By reducing the technical expertise required to create an ML model, AutoML also lowers the barriers to entry, enabling business analysts to leverage advanced modeling techniques — without assistance from data scientists. And by relieving data scientists from repetitive tasks of the machine learning process, AutoML frees these costly resources to pursue higher-value projects.
As data scientists ourselves, we initially thought little of AutoML. Yes, these techniques and tools could produce reasonably effective models. But that was essentially all they could do — and, of course, not without drawbacks. In the early stages, AutoML tools were far less advanced and typically no more complex than what could be implemented by a data scientist using existing tools. These barriers to acceptance were compounded by AutoML’s black box nature, which makes trained models less interpretable and meaningful, and by the difficultly in immediately finding use for it in non-academic settings. Moreover, AutoML suites of tools were far narrower in scope and solved only a portion of the problem — and with little value add.
AutoML has come a long way since then. In fact, it is now ubiquitous in most of the prevailing machine learning libraries, open-source tools and major cloud-compute platforms. Commercially available AutoML tools are making feature engineering and the development of complex machine learning models as easy as a few clicks of the button, enabling business users to deploy these models themselves in a production-ready state. As these more powerful AutoML tools proliferate, new questions arise, such as:
· Should we be using AutoML?
· If so, when should or shouldn’t we use them?
· Can we expect the results to be better than hand-crafted models?
· Can these tools take the next step and replace data scientists altogether?
As we assess AutoML, we must recognize that performance is not the complete story, and that bias can play an important role in AI. Taking human data scientists out of the process does not necessarily result in bias-free results. A computer does not, for example, know that there is anything wrong with training facial-recognition algorithms using the faces of white people only — or that the result of doing so is that a phone may fail to unlock when presented with the face of a non-white user. It is therefore the responsibility of data scientists themselves to mitigate these biases by checking and correcting models that advantage one race, gender or protected class over another.
Allowing biases to skew results can have negative consequences for businesses in virtually any industry. An example of bias in health care was recently published in Science magazine.
