Book Image

Interpretable Machine Learning with Python

By : Serg Masís
Book Image

Interpretable Machine Learning with Python

By: Serg Masís

Overview of this book

Do you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.
Table of Contents (19 chapters)
Section 1: Introduction to Machine Learning Interpretation
Section 2: Mastering Interpretation Methods
Section 3:Tuning for Interpretability

A business case for interpretability

This section describes several practical business benefits for machine learning interpretability, such as better decisions, as well as being more trusted, ethical, and profitable.

Better decisions

Typically, machine learning models are trained and then evaluated against the desired metrics. If they pass quality control against a hold-out dataset, they are deployed. However, once tested in the real world, that's when things can get wild, as in the following hypothetical scenarios:

  • A high-frequency trading algorithm could single-handedly crash the stock market.
  • Hundreds of smart home devices might inexplicably burst into unprompted laughter, terrifying their users.
  • License-plate recognition systems could incorrectly read a new kind of license plate and fine the wrong drivers.
  • A racially biased surveillance system could incorrectly detect an intruder, and because of this guards shoot an innocent office worker.
  • A self-driving car could mistake snow for a pavement, crash into a cliff, and injure passengers.

Any system is prone to error, so this is not to say that interpretability is a cure-all. However, focusing on just optimizing metrics can be a recipe for disaster. In the lab, the model might generalize well, but if you don't know why the model is making the decisions, then you can miss on an opportunity for improvement. For instance, knowing what the self-driving car thinks is a road is not enough, but knowing why could help improve the model. If, say, one of the reasons was that road is light-colored like the snow, this could be dangerous. Checking the model's assumptions and conclusions can lead to an improvement in the model by introducing winter road images into the dataset or feeding real-time weather data into the model. Also, if this doesn't work, maybe an algorithmic fail-safe can stop it from acting on a decision that it's not entirely confident about.

One of the main reasons why a focus on machine learning interpretability leads to better decision-making was mentioned earlier when we talked about completeness. If you think a model is complete, what is the point of making it better? Furthermore, if you don't question the model's reasoning, then your understanding of the problem must be complete. If this is the case, perhaps you shouldn't be using machine learning to solve the problem in the first place! Machine learning creates an algorithm that would otherwise be too complicated to program in if-else statements, precisely to be used for cases where our understanding of the problem is incomplete!

It turns out that when we predict or estimate something, especially with a high level of accuracy, we think we control it. This is what is called the illusion of control bias. We can't underestimate the complexity of a problem just because, in aggregate, the model gets it right almost all the time. Even for a human, the difference between snow and concrete pavement can be blurry and difficult to explain. How would you even begin to describe this difference in such a way that it is always accurate? A model can learn these differences, but it doesn't make it any less complex. Examining a model for points of failure and continuously being vigilant for outliers requires a different outlook, whereby we admit that we can't control the model but we can try to understand it through interpretation.

The following are some additional decision biases that can adversely impact a model, and serve as reasons why interpretability can lead to better decision-making:

  • Conservatism bias: When we get new information, we don't change our prior beliefs. With this bias, entrenched pre-existing information trumps new information, but models ought to evolve. Hence, an attitude that values questioning prior assumptions is a healthy one to have.
  • Salience bias: Some prominent or more visible things may stand out more than others, but statistically speaking, they should get equal attention to others. This bias could inform our choice of features, so an interpretability mindset can expand our understanding of a problem to include other less perceived features.
  • Fundamental attribution error: This bias causes us to attribute outcomes to behavior rather than circumstances, character rather than situations, nature rather than nurture. Interpretability asks us to explore deeper and look for the less obvious relationships between our variables or those that could be missing.

One crucial benefit of model interpretation is locating outliers. These outliers could be a potential new source of revenue or a liability waiting to happen. Knowing this can help us to prepare and strategize accordingly.

More trusted brands

Trust is defined as a belief in the reliability, ability, or credibility of something or someone. In the context of organizations, trust is their reputation; and in the unforgiving court of public opinion, all it takes is one accident, controversy, or fiasco to lose substantial amounts of public confidence. This, in turn, can cause investor confidence to wane.

Let's consider what happened to Boeing after the 737 MAX debacle or Facebook after the 2016 presidential election scandal. In both cases, there were short-sighted decisions solely made to optimize a single metric, be it forecasted plane sales or digital ad sales. These underestimated known potential points of failure and missed out entirely on very big ones. From there, it can often get worse when organizations resort to fallacies to justify their reasoning, confuse the public, or distract the media narrative. This behavior might result in additional public relations blunders. Not only do they lose credibility with what they do with their first mistake but they attempt to fool people, losing credibility with what they say.

And these were examples of, for the most part, decisions made by people. With decisions made exclusively by machine learning models, this could get worse because it is easy to drop the ball and keep the accountability in the model's corner. For instance, if you started to see offensive material in your Facebook feed, Facebook could say it's because its model was trained with your data such as your comments and likes, so it's really a reflection of what you want to see. Not their fault—your fault. If the police targeted your neighborhood for aggressive policing because it uses PredPol, an algorithm that predicts where and when crimes will occur, it could blame the algorithm. On the other hand, the makers of this algorithm could blame the police because the software is trained on their police reports. This generates a potentially troubling feedback loop, not to mention an accountability gap. And if some pranksters or hackers eliminate lane markings, this could cause a Tesla self-driving car to veer into the wrong lane. Is this Tesla's fault that they didn't anticipate this possibility, or the hackers', for throwing a monkey wrench into their model? This is what is called an adversarial attack, and we discuss this in Chapter 13, Adversarial Robustness.

It is undoubtedly one of the goals of machine learning interpretability to make models better at making decisions. But even when they fail, you can show that you tried. Trust is not lost entirely because of the failure itself but because of the lack of accountability, and even in cases where it is not fair to accept all the blame, some accountability is better than none. For instance, in the previous set of examples, Facebook could look for clues as to why offensive material is shown more often, then commit to finding ways to make it happen less even if this means making less money. PredPol could find other sources of crime-rate datasets that are potentially less biased, even if they are smaller. They could also use techniques to mitigate bias in existing datasets (these are covered in Chapter 11, Bias Mitigation and Causal Inference Methods). And Tesla could audit its systems for adversarial attacks, even if this delays shipment of its cars. All of these are interpretability solutions. Once a common practice, they can lead to an increase in not only public trust—be it from users and customers, but also internal stakeholders such as employees and investors.

The following screenshot shows some public relation AI blunders that have occurred over the past couple of years:

Figure 1.4 – AI Now Institute's infographic with AI's public relation blunders for 2019

Figure 1.4 – AI Now Institute's infographic with AI's public relation blunders for 2019

Due to trust issues, many AI-driven technologies are losing public support, to the detriment of both companies that monetize AI and users that could benefit from them (see Figure 1.4). This, in part, requires a legal framework at a national or global level and, at the organizational end, for those that deploy these technologies, more accountability.

More ethical

There are three schools of thought for ethics: utilitarians focus on consequences, deontologists are concerned with duty, and teleologicalists are more interested in overall moral character. So, this means that there are different ways to examine ethical problems. For instance, they are useful lessons to draw from all of them. There are cases in which you want to produce the greatest amount of "good", despite some harm being produced in the process. Other times, ethical boundaries must be treated as lines in the sand you mustn't cross. And at other times, it's about developing a righteous disposition, much like many religions aspire to do. Regardless of the school of ethics we align with, our notion of what it is evolves with time because it mirrors our current values. At this moment, in Western cultures, these values include the following:

  • Human welfare
  • Ownership and property
  • Privacy
  • Freedom from bias
  • Universal usability
  • Trust
  • Autonomy
  • Informed consent
  • Accountability
  • Courtesy
  • Environmental sustainability

Ethical transgressions are cases whereby you cross the moral boundaries that these values seek to uphold, be it by discriminating against someone or polluting their environment, whether it's against the law or not. Ethical dilemmas occur when you have a choice between options that lead to transgressions, so you have to choose between one and another.

The first reason machine learning is related to ethics is because technologies and ethical dilemmas have an intrinsically linked history.

Since the first widely adopted tool made by humans, it brought progress but also caused harm, such as accidents, war, and job losses. This is not to say that technology is always bad but that we lack the foresight to measure and control its consequences over time. In AI's case, it is not clear what the harmful long-term effects are. What we can anticipate is that there will be a major loss of jobs and an immense demand for energy to power our data centers, which could put stress on the environment. There's speculation that AI could create an "algocratic" surveillance state run by algorithms, infringing on values such as privacy, autonomy, and ownership.

The second reason is even more consequential than the first. It's that machine learning is a technological first for humanity: machine learning is a technology that can make decisions for us, and these decisions can produce individual ethical transgressions that are hard to trace. The problem with this is that accountability is essential to morality because you have to know who to blame for human dignity, atonement, closure, or criminal prosecution. However, many technologies have accountability issues to begin with, because moral responsibility is often shared in any case. For instance, maybe the reason for a car crash was partly due to the driver and mechanic and car manufacturer. The same can happen with a machine learning model, except it gets trickier. After all, a model's programming has no programmer because the "programming" was learned from data, and there are things a model can learn from data that can result in ethical transgressions. Top among them are biases such as the following:

  • Sample bias: When your data, the sample, doesn't represent the environment accurately, also known as the population
  • Exclusion bias: When you omit features or groups that could otherwise explain a critical phenomenon with the data
  • Prejudice bias: When stereotypes influence your data, either directly or indirectly
  • Measurement bias: When faulty measurements distort your data

Interpretability comes in handy to mitigate bias, as seen in Chapter 11, Bias Mitigation and Causal Inference Methods, or even place guardrails on the right features, which may be a source of bias. This is covered in Chapter 12, Monotonic Constraints and Model Tuning for Interpretability. As explained in this chapter, explanations go a long way in establishing accountability, which is a moral imperative. Also, by explaining the reasoning behind models, you can find ethical issues before they cause any harm. But there are even more ways in which models' potentially worrisome ethical ramifications can be controlled for, and this has less to do with interpretability and more to do with design. There are frameworks such as human-centered design, value-sensitive design, and techno moral virtue ethics that can be used to incorporate ethical considerations into every technological design choice. An article by Kirsten Martin ( also proposes a specific framework for algorithms. This book won't delve into algorithm design aspects too much, but for those readers interested in the larger umbrella of ethical AI, this article is an excellent place to start. You can see Martin's algorithm morality model in Figure 1.5 here:

Figure 1.5 – Martin's algorithm morality model

Figure 1.5 – Martin's algorithm morality model

Organizations should take the ethics of algorithmic decision-making seriously because ethical transgressions have monetary and reputation costs. But also, AI left to its own devices could undermine the very values that sustain democracy and the economy that allows businesses to thrive.

More profitable

As seen already in this section, interpretability improves algorithmic decisions, boosting trust and mitigating ethical transgressions.

When you leverage previously unknown opportunities and mitigate threats such as accidental failures through better decision-making, you can only improve the bottom line; and if you increase trust in an AI-powered technology, you can only increase its use and enhance overall brand reputation, which also has a beneficial impact on profits. On the other hand, as for ethical transgressions, they can be there by design or by accident, but when they are discovered, they adversely impact both profits and reputation.

When businesses incorporate interpretability into their machine learning workflows, it's a virtuous cycle, and it results in higher profitability. In the case of a non-profit or governments, profits might not be a motive. Still, finances are undoubtedly involved because lawsuits, lousy decision-making, and tarnished reputations are expensive. Ultimately, technological progress is contingent not only on the engineering and scientific skills and materials that make it possible but its voluntary adoption by the general public.