Mission Statement

This post introduces approximatelycorrect.com. The aspiration for this blog is to offer a critical perspective on machine learning. We intend to cover both technical issues and the fuzzier problems that emerge when machine learning intersects with society.

For explaining the technical details of machine learning, we enter a lively field. As recent breakthroughs in machine learning have attracted mainstream interest, many blogs have stepped up to provide high quality tutorial content. But at present, critical discussions on the broader effects of machine learning lag behind technical progress.

On one hand, this seems natural. First a technology must exist before it can have an effect. Consider the use of machine learning for face recognition. For many decades, the field has accumulated extensive empirical knowledge. But until recently, with the technology reduced to practice, any consideration of how it might be used could only be speculative.

But the precarious state of the critical discussion owes to more than chronology. It also owes to culture, and to the rarity of the relevant interdisciplinary expertise. The machine learning community traditionally investigates scientific questions. Papers address well-defined theoretical problems, or empirically compare methods with well-defined objectives. Unfortunately, many pressing issues at the intersection of machine learning and society do not admit such crisp formulations. But, with notable exceptions, consideration of social issues within the machine learning community remains too rare.

Conversely, those academics and journalists best equipped to consider economic and social issues rarely possess the requisite understanding of machine learning to anticipate the plausible ways the two arenas might intersect. As a result, coverage in the mainstream consistently misrepresents the state of research, misses many important problems, and hallucinates others. Too many articles address Terminator scenarios, overstating the near-term plausibility of human-like machine consciousness, assume the existence (at present) of self-motivated machines with their own desiderata, etc. Too few consider the precise ways that machine learning may amplify biases or perturb the job market.

In short, we see this troubling scenario:

1. Machine learning models increasingly find industrial use, assisting in credit decisions, recognizing faces in police databases, curating the news on social networks, and enabling self-driving cars. 
2. The majority of knowledgeable people in the machine learning community, with notable exceptions, are not in the habit of considering the relevant economic, social, and other philosophical issues.
3. Those in the habit of considering the relevant issues rarely possess the relevant machine learning expertise.

Complicating matters, mainstream discussion of AI-related technologies introduces speculative or spurious ideas alongside sober ones without communicating uncertainty clearly. For example, the likelihood of a machine learning classifier making a mistake on a new example, and the likelihood of a machine learning causing massive unemployment and the likelihood of the entire universe being a simulation run by agents in some meta-universe are all discussed as though they can be a assigned some common form of probability.

Compounding the lack of rigor, we also observe a hype cycle in the media. PR machines actively promote sensational views of the work currently done in machine learning, even as the researchers doing that work view the hype with suspicion. The press also has an incentive to run with the hype: sensational news sells papers. The New York Times has referenced “Terminator” and “artificial intelligence” in the same story 5,000 times. It’s referenced “Terminator” and “machine learning” together in roughly 750 stories.

In this blog, we plan to bridge the gap between technical and critical discussions, treating both methodology and consequences as first-class concerns. 

One aspiration of this blog will be to communicate honestly about certainty. We hope to maintain a sober, academic voice, even when writing informally or about issues that aren’t strictly technical. While many posts will express opinions, we aspire to clearly indicate which statements are theoretical facts, which may be speculative but reflect a consensus of experts, and which are wild thought experiments. We also plan to discuss both immediate issues such as employment alongside more speculative consideration of what future technology we might anticipate. In all cases we hope to clearly indicate scope. In service of this goal, and in reference to the theory of learning, we adopt the name Approximately Correct. We hope to be as correct as possible as often as possible, and to honestly convey our confidence.

Author: Zachary C. Lipton

Zachary Chase Lipton is an assistant professor at Carnegie Mellon University. He is interested in both core machine learning methodology and applications to healthcare and dialogue systems. He is also a visiting scientist at Amazon AI, and has worked with Amazon Core Machine Learning, Microsoft Research Redmond, & Microsoft Research Bangalore. View all posts by Zachary C. Lipton