Computational Interpretabilism: Bridging Machine Learning and Science

The use of machine learning in science has brought about a tricky situation. Scientists want to understand what's going on, but many machine learning models are so complex that they seem like mysterious black boxes. Some people argue that we should only use models that are easy to interpret. However, there's a growing movement that believes we can still glean valuable insights from these complex models, even if we can't fully understand them. This idea is called Computational Interpretabilism.

#The Problem with Black Boxes

When scientists use machine learning models, they often get great results in predicting things like weather patterns or how proteins fold. However, the way these models work is not always clear. Think of it like having a magic box: you put in some inputs, and out comes an answer, but you have no idea how it got there. This lack of clarity can be frustrating, especially in fields where understanding is crucial.

Scientists traditionally rely on clear theories and explanations. If a model can't explain its reasoning, it poses challenges in understanding the science behind it. This tension leads to two main approaches in dealing with complex models. One side insists on using models that are easy to interpret from the start. The other side suggests looking for ways to explain already-built complex models after the fact-this is the essence of Post-hoc Interpretability.

#Post-hoc Interpretability: A Second Chance

Post-hoc interpretability methods aim to explain complex models after they have already been trained. While these methods can be useful, they have faced criticism. Some studies have highlighted their limitations and raised questions about whether they can provide real understanding. Critics argue that if the explanations are not based on solid reasoning, they might not be trustworthy.

But here’s where Computational Interpretabilism comes in. It offers a fresh perspective by saying that while we may never fully understand how a complex model functions, we can still gain meaningful insights if we approach it in the right way. This perspective is based on two key ideas: even without full access to a model's inner workings, we can still learn valuable information through careful examination of its behavior; and approximations can yield useful scientific insights if we know the limitations of those approximations.

#The Wisdom of Human Experts

Human decision-making offers a relatable example. Experts in various fields, like medicine or finance, often make decisions based on their experience rather than complete explanations of how they arrived at those decisions. Sometimes they even rationalize their decisions after the fact. This tells us that a successful outcome doesn’t always require a detailed explanation. The same principle can be applied to machine learning models. If experts can work this way, perhaps computers can too.

This leads us to some important questions about interpretability in AI. We need to think about whether explanations have to be completely transparent to be valid. Can we rely on insights generated from models even if we don't fully comprehend their mechanics? Both science and machine learning share the same goal: to seek reliable knowledge, even if the path to that knowledge isn't crystal clear.

#What Is Interpretability?

Interpretability in AI is not a single concept; it's more of a mixed bag. Different people have different expectations when it comes to understanding AI models. For example, computer scientists might want to see how inputs are processed mechanically. Meanwhile, scientists may want to know how model outputs reflect real-world phenomena.

For many scientific applications, interpretability is more than just understanding how a model works. It also involves grasping how AI can provide insights about the natural world in ways that enrich scientific understanding. This is crucial because many criticisms of post-hoc methods arise when we assess their ability to faithfully explain a model's function without considering the broader context.

#Key Assumptions to Consider

1. Accessibility of AI Systems: We focus on open black-box models, meaning those that aren’t secretive. Understanding these black boxes is challenging primarily due to their complexity, not a total lack of knowledge.

2. Scientific AI Models: We concentrate on models designed for scientific purposes, like predictive models, while sidestepping generative models, as they are different beasts altogether.

3. Imperfect But Meaningful Approximations: We assume post-hoc methods can provide approximations that aren't perfect but still capture meaningful patterns. We focus on approaches that have shown they can reveal useful insights, rather than methods that perform no better than tossing a coin.

#Reliability and Justifiability

Similar to how human experts work without fully explaining their reasoning, machine learning can also follow this path. The key lies in how we justify the insights generated by these models. Traditional epistemology speaks about two forms of justification: internalist (clear reasons are available) and externalist (focus on reliability).

Human judgment often relies on experienced-based reasoning, where experts trust their intuition even without a full grasp of their decision-making processes. If we accept expert decisions for their demonstrated reliability, then perhaps we can also accept post-hoc interpretability methods as valid when they lead to reliable scientific insights.

#Mediated Understanding: Bridging the Gap

Mediated understanding is central to Computational Interpretabilism. This concept highlights that understanding comes from the interaction between model behavior, interpretability methods, domain knowledge, and empirical validation. Rather than directly interpreting a model, we can facilitate understanding through structured interactions that mediate between the model and what we observe in the real world.

To illustrate, let's consider a medical diagnosis model. By translating model computations into testable hypotheses about biological mechanisms, we create a bridge between what the model suggests and existing scientific knowledge. When these hypotheses are validated through empirical studies, they contribute to our medical understanding.

#Bounded Factivity: The Limits of Explanation

When it comes to understanding complex systems, it's important to acknowledge that full factual correctness may not always be possible. In science, it's common to use simplified models that deviate from the truth but still provide valuable insights. This notion of bounded factivity suggests we shouldn't demand perfect correspondence between our interpretations and a model's inner mechanics.

Instead of striving for complete accuracy, we advocate for a pragmatic approach, where we recognize the truths within acknowledged limits. This is reminiscent of how people handle complex decisions: they simplify without losing sight of their goals.

#Critiquing Post-hoc Models

Critics of post-hoc interpretability often raise concerns about approximations and the fidelity of explanations. While some argue that these explanations can be misleading, it's essential to look at them as useful tools in the scientific process rather than failures.

Local explanations, for instance, can offer granular insights that complement broader understanding. Rather than disqualifying them due to their localized nature, we should see how they can contribute to our overall scientific knowledge. Every piece of information has its place, even if it doesn't form a complete picture all by itself.

#Fear of Confirmation Bias

Another valid concern about post-hoc models is confirmation bias, which can lead to overconfidence in interpretations that may not truly capture the model's reliability. It's crucial to recognize that both human experts and AI systems are susceptible to this bias. Instead of abandoning post-hoc explanations, we should work to refine them and create strategies to ensure they provide reliable insights.

By systematically validating these interpretations, we can bridge the gap between human understanding and machine output. The goal is not to eliminate all uncertainties but to acknowledge them while still generating valid scientific knowledge.

#Comparing Different Models

When we look at machine learning models in science, we can categorize them into intrinsically interpretable models and post-hoc explainable models. Intrinsically interpretable models are structured to be understandable right from the start, whereas post-hoc models require additional methods to make sense of their output.

The key takeaway is that while both approaches have their merits, they offer different paths to human comprehension. Intrinsically interpretable models maintain a direct link to human understanding, while post-hoc methods introduce complexity, yet can capture intricate relationships that may be overlooked.

#Broadening the Scope of Interpretability

Computational Interpretabilism doesn't just apply to theory-rich situations. It also has relevance in theory-poor contexts, where machine learning is employed with minimal theoretical grounding. In these cases, interpretability methods can still provide valuable insights and help researchers uncover hidden assumptions in the data.

Through structured mediation, these methods assist researchers in validating existing theories or even constructing new ones. This unifying approach represents a significant advancement in understanding how machine learning can contribute to scientific knowledge, regardless of the level of theory involved.

#The Philosophy Behind AI and Interpretability

Various philosophical perspectives relate to the challenges faced in understanding machine learning models. These perspectives highlight how the relationship between explanation and understanding is influenced by concepts like link uncertainty, theory-ladenness, and factivity dilemmas.

Link Uncertainty: This concept emphasizes that understanding comes from how well we can connect a model's predictions to real-world phenomena, rather than understanding the model itself. The better the empirical evidence, the more valid our understanding becomes.

Theory-ladenness: This perspective illustrates that all scientific data is rooted in theoretical assumptions, reinforcing the idea that machine learning can't be entirely “theory-free.” The impact of these assumptions must be acknowledged and addressed in any scientific inquiry.

Factivity Dilemma: This topic deals with the tension between accuracy and comprehensibility in machine learning. While models strive for factual explanations, simplifications often lead to a loss of transparency. Yet, it's proposed that simplified models can still provide valid insights.

#Conclusion: A New Approach to Understanding

Ultimately, the case for post-hoc interpretability is about recognizing the value of approximations and the structured interactions between complex models and real-world knowledge. Just as experts rely on their experience and intuition, we can learn to trust the insights generated by AI, even when we can't see every step of the reasoning process.

The journey toward understanding may be filled with uncertainties, but through carefully crafted methods, we can bridge the gap between machine learning models and scientific knowledge, leading to meaningful advancements in our understanding of the world around us. After all, even the most complex puzzles can have pieces that fit together, even if we can’t see the whole picture right away!