Overcoming Machine Learning RisksOvercoming Machine Learning Risks
An opinion piece by the innovation analyst at itransition, a global software engineering and consulting services company
July 8, 2022
Machine learning (ML) is a backbone of many organizations’ operations today. But while some companies are reaping the benefits of this technology, many of them are discovering that ML implementation also comes with a number of risks and are often forced to turn to a machine learning consulting firm to mitigate them. This article will determine what exactly makes ML implementation a risky endeavor and discuss methods for minimizing these factors.
What makes ML implementation risky
In essence, conventional methods for implementing digital technologies do not apply to ML, because ML algorithms autonomously make important decisions that can have a dramatic impact on both business outcomes and peoples’ lives. Whether it is detecting cancer, deciding how to react to a dangerous road situation, or choosing a candidate for a job interview, ML models often carry the weight of the world on their proverbial shoulders. Unsurprisingly, it’s not uncommon for ML engines to make decisions that are flat-out wrong, biased, or unethical.
So why exactly is it that ML-based decision-making can be unreliable? It is crucial to realize that the output of the ML algorithm is a result of an informed prediction rather than a definitive answer to what is going to happen. ML-based tools and applications make decisions based on probability rather than certainty. And predictions may and may not come true.
1. Dirty data
The likelihood that predictions will be wrong largely depends on the quality and amount of data used for algorithm training. Incomplete, outdated, inconsistent, or duplicate data will always result in an inaccurate ML algorithm output. However, data cleansing, a practice that should be a standard procedure regardless if a company uses ML or not, is the solution to keeping data accurate.
2. Biased data
Bias is definitely one of the most controversial, nuanced and complex problems that organizations face on the path to ML implementation. Given that algorithms now make hiring decisions and predict which defendants are more likely to become recidivists, gender and racial discrimination have become significant concerns. The presence of bias in ML algorithms is expected as ML algorithms generally are built to mimic human decision-making, which is inherently biased. Unsurprisingly, the biggest reason for a biased ML algorithm is biased training data.
3. Inappropriate modeling
Conditions in which ML systems are trained can differ from real-world environments. For example, a car manufacturer wants to implement a computer vision system to detect vehicle body dents and defects. If such an ML model is trained in a highly controlled environment with perfect lighting and camera placement, it may put out inaccurate results in a real manufacturing setting. Similarly, a stocks-trading algorithm that was trained with data derived solely during an economic upturn will most likely make inaccurate decisions in a recession.
4. Lack of transparency
In many cases, when ML-based systems are used to their full potential, it becomes increasingly hard to understand the algorithm’s decision-making. This lack of transparency, which is commonly referred to as the ‘black box’ problem, is a serious issue both from legal and business perspectives. With the ever-strengthening regulatory policies, it becomes increasingly hard to use ML model output that companies can’t explain.
Besides data-related issues, ML implementation also introduces a number of privacy and regulatory concerns.
Managing machine learning implementation risks
ML implementation calls for a comprehensive update of conventional risk-management strategies. ML risk-management frameworks can have substantial differences depending on the industry and context. However, regardless of the purpose of the ML system, there are some fundamental methods that every company relying on machine learning should employ.
Standardization is among the most important conditions for minimizing ML risks. In essence, risk probability is correlated to the degree of understanding possessed by all departments across the enterprise. With concise standards for assembling training datasets, labeling data, model training and evaluation, and for every other step of ML system design, it becomes much easier for legal and risk-management teams to assess risks as they have a transparent map of the ML system design.
Documentation is an extremely powerful tool for minimizing ML implementation risks. Comprehensive documentation lets engineers and data scientists streamline model reviews, which are paramount to risk mitigation. Importantly, in the case of current data scientist replacement, documentation is the only way a new data scientist can adequately assess, control and monitor the ML model.
3. Rigorous testing
While testing is a standard phase of any software development project, ML systems require companies to adopt novel testing approaches. While all the technical intricacies regarding ML testing are out of the scope of this article, it is critical that companies employ the right mindset.
A final word
It is important to treat an ML system as a human being. We can draw a parallel with Formula 1 drivers’ training. Regardless of how a real F1 VR booth feels, it will never completely and accurately reflect the drivers’ skills on the real track during a competition. That is why it is paramount to expose algorithms to random and extreme conditions as much as possible prior to rollout.
There is no doubt that ML has terrific potential in a myriad of industries. However, companies are just starting to realize how much of a risk this technology may entail if they will not employ ML-specific approaches. Establishing comprehensive standardization, documentation, and monitoring frameworks is the backbone of risk-free ML models.
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