Despite its widespread popularity, AI still has yet to pay off for more than 10% of companies that have adopted it. According to the MIT Sloan study, to properly reap benefits from AI - organizations must find ways to bring humans and machines closer together, a concept they call "organizational learning." Concretely, when most enterprises start adopting AI strategies, the first set of use cases targeted are the lowest hanging fruits. These are typically problems where the data is readily available and in a form where a model can be easily trained (e.g. customer support triage, social media sentiment analysis, customer segmentation for marketing), but are rarely the highest value use case in the organization. When thinking about where AI can provide the most value, typically the most successful enterprises focus on augmenting subject matter experts.
This poses a problem for most organizations, as applying AI to augment domain experts requires those very same experts to be involved in training those models. For example, a large healthcare organization might target one of their highest value use cases: building an AI to help diagnose a few specific cancer strains. For the AI to be able to complete the task, it must be trained on hundreds of thousands of well-labeled examples produced by experts like oncologists and radiologists. This process is cost-prohibitive for most organizations, as experts are scarce resources and can't be diverted to labeling data full time for months on end. "Organizational learning" makes room for experts and machines to not only work together but also learn from each other over time, and the team at MIT that conducted the survey indicate that this mutual learning between human and machine is essential to success with AI. Doing this right, however, is difficult as it increases the demand on already scarce subject matter experts without ever addressing the core issue: there are not enough experts in an organization to have them both label data and perform their typical day to day tasks.
Machine Teaching, a field of study that has been gaining popularity recently, is focused on addressing the bottleneck of domain expertise in AI. While classical Machine Learning (ML) research focuses on optimizing the learning algorithm or network architecture, Machine Teaching (MT) focuses on making the human more effective at teaching the model. While a "smarter student" (new innovation in model architectures) could learn the expert's context faster and with fewer examples than the "average student", these types of innovations are rare and unpredictable. On the other hand, a more effective "teacher" (a single domain expert doing the work of hundreds) can have an outsized impact in practically any AI/ML context regardless of how sophisticated the "student" is.
While the expertise bottleneck is by far the biggest limiting factor currently facing AI/ML implementations today, there are other major obstacles in the current ML workflow that prevent organizations from seeing value come back from their investments. Productivity for ML scientists is lower than it could be as the ML workflow is inherently disjointed and often rife with debt. It is near impossible to stay agile in the face of model drift, as drifting models require fresh data for retraining - constantly draining time from expert labelers to maintain legacy pipelines. Finally, current ML processes don't lend themselves naturally to explainability - what happens when your training set is biased but you can't ask your labelers why their labels were biased (because there are too many labelers, no record of which labelers produced a set of labels, or they are no longer part of the organization)?
Software Engineers have long touted the importance of “flow state” when programming, but this concept is missing in data science workflows. For instance, before you’re even able to start building a model, you first need labeled data to dig into. Once a project is defined, you may have to wait weeks for data to be annotated. This labeling process is the slowest part of the workflow, yet you can do nothing until it’s completed.
“You’re never done labeling” is a common expression muttered angrily by ML experts - even after your model is built and deployed, the labeling never ends. Models don’t stay static forever - their performance degrades over time due to changes in the environment (which is called drift). Typically, models are retrained periodically to counteract the effects of drift - but how do you measure the model drift itself? You could track statistical measures (e.g. Kullback-Leibler divergence, Jensen-Shannon divergence, or the Kolmogorov-Smirnov test) for your input and output spaces, but these statistics are difficult to interpret without having concrete labeled data to reference. Ideally, you would assess model performance in production the same way you would during development - by looking at metrics like precision, accuracy, recall, etc. but all of these measures are comparisons between predictions of the model and a labeled baseline. In your development environment you would use your validation set as the baseline, but in production the only way you can generate a baseline is by labeling a sample of your production traffic periodically. This process is difficult to scale, as each production model would require a constant ongoing investment of human effort for maintenance.
Simply put, labeling is the most frequently re-visited step in the workflow and causes compounding slowdowns throughout the process. Since labeling takes time when performed manually, the current ML workflow is discontinuous and doesn’t lend itself well to flow states.
What happens when the concept of what you are trying to predict changes? For example, if you’re building a classifier for personally identifiable information (PII) based on rules or regulations, what happens when those regulations change and include a brand new type of PII?
What about when the input space shifts? For example, let’s imagine you work in the data science team of a popular email service, and your team manages a model that detects spam for all of your users. Your team worked diligently to build a model that takes many features into consideration, and it did a great job of finding spammers. Over time, however, you notice that the model’s performance degrades and false positives/negatives start piling up. It’s possible that your users’ behaviors have changed - maybe your users like your email client so much that they’re emailing much more frequently than they did before. Maybe spammers are making changes to their tactics to make prediction much harder.
To overcome both of these issues, you would need to relabel data and retrain your models, but how often should that be done? How early do you need to start creating training data? The relabeling and retraining process takes weeks of work, delaying the speed with which organizations can adapt to changes. For acute changes in the world (like when COVID-19 became widespread), being able to react quickly is key and is not currently something that ML workflows are currently well adapted for.
Explainability in model development pipelines is something that most Machine Learning practitioners can likely appreciate. There are various ways to provide explainability at the model level - for instance, by using Shapley Values or by using an inherently interpretable model to begin with, but bias is typically introduced in the training data itself. If labelers hold biases or if the way the data was sampled introduces some skew, models tend to pick these up. Using the plethora of methods to achieve model explainability, we can diagnose model biases and find issues in the training data - but how do we explain the labels in the data?
As useful as it is to point to bias and skew in the data, it's more helpful to be able to get to the why - why did the labelers indicate that this particularly hateful tweet isn't toxic? Why did the labelers mislabel this credit application from a minority? Currently, the only way to get this level of explainability is to physically ask the labelers why they applied a particular label - but it's impossible to do this well. Labeling teams see churn, labelers may not be paying full attention because they're trying to get through the task quickly, and labelers sometimes just make mistakes. All of these factors make it difficult to nail down the root causes of poor labels, and ultimately prevent data teams from quickly fixing problems with their models.
As mentioned earlier, the biggest reason why most enterprises don't see value come back from their investments in AI is that their highest value use cases are massive time sinks for their subject matter experts. Today, the most impactful ML/AI solutions are typically considered too difficult or expensive to even attempt due to labeling the data. For example, if you wanted to build a model to detect a specific type of cancer in MRI images - you would need an army of radiologists and oncologists on staff to provide their expertise in the form of labeled data. There's no doubt that this model would be incredibly valuable if built, so the argument for building the model is straightforward from an impact standpoint. However, an organization might only have a handful of these experts, and their time is too valuable to spend on data labeling. As a result, the business case is dead in the water despite the apparent value the project would bring to the table should it be successful.
Every vertical suffers from this issue - subject matter expertise is expensive, therefore the most valuable models are difficult or impossible to justify building. The easiest to build models are rarely the most helpful, and so many organizations end up spinning their wheels trying to get AI to provide meaningful impact.
By shifting focus to making Machine Teachers more effective, we can more effectively target the human bottleneck in the workflow and solve for all four of the above problems. An effective machine-teaching solution requires the following attributes to lower the costs introduced by human effort significantly:
2. Expressive interfaces to capture knowledge and context
3. Tight feedback loops
4. Automation as a core pillar
5. Explanations and interpretations
6. Separation of duties between ML experts and domain experts
Applying this framework to the problem statements above paints us a good picture of the impact Machine Teaching can have on our existing workflow.