The online interactive magazine of the Association for the Advancement of Artificial Intelligence

by Mike Schaekermann, U. Waterloo
Christopher M. Homan, U. Rochester
Lora Aroyo, Google Research
Praveen Paritosh, Google Research
Kurt Bollacker, The Long Now Foundation
Chris Welty, Google Research

Some machine learning (ML) rhetoric seems to imply an assumption or expectation that, at some point, machines will outgrow the need for human labeled data. Today’s reliance on such labeling is a sort of dirty little secret of AI, and some view it as a necessary means to a larger end. This bet is an attempt to formalize that attitude into a concrete question, whose answer can be measured over time.

The process of putting together this bet, which began a year ago at The Web Conference 2019 workshop on Subjectivity, Ambiguity, and Disagreement, was quite long and involved. It goes, in part, to show why we have had such a hard time filling this column quarterly with bets, even though the support for AI bets has been unanimous. The bettors and bookies, however, found the entire process valuable – delightful even – and many potential side projects cropped up during the course of its specification. We came to believe this adversarial process outlined in the first bookie column two years ago, is an important way to move science forward in an understandable way. Scientists are all, or should all be, skeptics, and there is nothing like having to justify yourself to a skeptic – not an arbitrary skeptic, but a knowledgeable one. We should be looking to move beyond our echo chambers.

The Bet

The reliance of machine learning on human labeled data will decrease in the next five years.

To measure this, we have chosen a surrogate to quantify the reliance of ML on human labeled data: the proportion of research papers at major AI conferences, and reformulate the bet as:

The percentage of machine-learning papers at AAAI, NeurIPS, and TheWebConf that use human labeled data for training or evaluating models will be lower in 2025 than in 2020.

Pro (Mike Schaekermann and Christopher M. Homan)

We believe the reliance on human labeled data in AI will decrease, because:

Methods for collecting human data passively will improve. Over the next five years, we believe that technological advances in sensing and artificial intelligence will make it possible to collect many of the kinds of labels that now require human judgement, and do so more cheaply. For example, market research reports 1,2, forecast a steady annual growth of at least 15% for the global market on wearable technologies over the next few years. We expect that this growth will be accompanied by an emergence of novel sensor modalities and the proliferation of longitudinal datasets precisely monitoring certain objective outcomes about the human condition (e.g., cardiovascular, neural or endocrine anomalies) for which human expert judgement is nowadays often used as an imperfect proxy.

Human interpretations are less reliable for scientifically valid tests. Machine learning researchers, in order to more reliably obtain meaningful numbers from their evaluations, will avoid problems that require human labels, even if it means that the labels are in some cases less representative of human judgement. As one example, current AI systems for diagnostic support typically rely on doctors’ interpretation of raw data, e.g., assigning diagnosis codes to medical images. However, inter-rater disagreement is a widely recognized issue across various medical subspecialties, requiring expensive procedures to ensure label quality. We project a shift towards an increased use of longitudinal datasets in which objective outcomes like future patient condition are available, decreasing the need for even expert human judgement.

There are significant disciplinary boundaries between hard AI and human computation. Another pressure is that the “gearheads” who understand the inner mechanics of complex mathematical learning algorithms often lack the temperament for the messy and awkward work of collecting human annotations. And so while some may seek to reach across disciplinary boundaries for the sake of their work and take the time and trouble to collect human labels, many more will simply rely on data collection methods that are fully automated and thus much more convenient to them.

Demand for nonhuman intelligence will increase. Generally speaking, machine learning problems tend to fall into two categories: those that humans can already do, and those that are beyond the capabilities of human intelligence. The latter category would seem to be much larger than the former (though its size is hard even to contemplate, given the same limits of human intelligence). Already, machine learning has helped to discover adverse drug reactions by poring through and making sense of much more data than any human team could possibly manage. As another example, machine learning algorithms nowadays can produce accurate weather forecasts orders of magnitude faster, at greater spatial resolution and with significantly less input data, than can human-driven simulations.3 For most of these tasks human judgement will continue to be insufficient or irrelevant.

Con (Lora Aroyo and Praveen Paritosh)

We believe the reliance on human labeled data in AI will increase, because:

Passive data is not enough. Many researchers believe that passively collected data will be widely and cheaply available in the future. However, It is already widely available in many fields, and we agree it will continue to grow, but it doesn’t seem to accomplish what human labels do. For example, various types of recommender systems for movies, news and shopping are still pretty basic in terms of predicting human intent, desires and needs. Understanding the utility of passive data for different user tasks and needs will always be guided by a human labeling process. For example, search generates a lot of passive data, in the form of clicks and queries, but the need for actively generated human data keeps increasing 4,5, and there is no evidence that this is ever going to decrease as user needs keep getting more complex. The same holds for recommendation systems for shopping, movies, videos, news, social media, or decision making systems for self-driving cars, digital assistants.

Demand for understanding subjective notions will increase. While some AI researchers might see the subjectivity and nuance of human cognition as reflected in the disagreement between raters as a problem, missing that nuance is an achilles heel of today’s AI systems6,7. This shows up as lack of common sense, lack of human understandability, etc. We increasingly depend upon automatically detecting subjective things like toxicity, pornography, fake news, multi-lingual/cultural perspectives etc. We believe that modeling that nuance will enable the next set of breakthroughs in AI systems which will increasingly rely on human labeled data.

Demand for multidisciplinary approaches will increase. The argument from the pro team, that “gearheads” who do math and “fuzzies” who understand humans won’t talk to each other, is a sad but accurate state of the entire AI field “looking below the lamp.” Various research initiatives around the world have been stimulating interdisciplinary collaborations in research projects, e.g. NSF, DARPA, NIH, eScience, NWO, etc., which we believe indicates a desire for understanding and modeling the phenomena, even if it involves inter-disciplinary collaborations. AI has been a strongly multidisciplinary field, learning from and collaborating with linguists, neuroscientists, cognitive psychologists, philosophers, and we believe that this will continue to increase.

Demand for fidelity to human behavior and explanatory requirements will increase. The pro team argues that the demand for nonhuman intelligence will increase, in part because human intelligence is just one instance of many possible intelligences. Over a large spectrum of diverse tasks, these nonhuman intelligences will have to be able to engage naturally with humans, and thus be able to model human understanding and communication satisfactorily. As AI systems grow beyond mere conveniences, they will have to explain their “understanding” to human stakeholders in order to be useful. However, the state of the art in machine explanation is a far cry from what human users and regulators will ultimately want. This in turn, will rely on more human labeled data. Secondarily, we believe that human intelligence is a particularly important one, and will be driving more growth and value to humanity than all the nonhuman intelligences put together. Automating aspects of human intelligence provides utility to humans by saving us time on tasks that we do and thus know to be important.

Demand for dealing with bias will increase. Unlabeled data, such as web corpora, or other found datasets such as records of hiring decisions made in the past, have been shown to contain biases with social consequences that will become more apparent as predictive models trained on them are deployed. As we become more aware of different biases in our society and
how they come bundled with any passively collected data, we will need more human labeled data to both discover and understand those biases automatically. This means there will be a continuous demand to deal with special cases that need human interpretation and the demand for this will never cease.


When we first conceived this bet, “human labeling” meant to us the kind of conventional activity where trained researchers would annotate data with ground truth class labels for the purpose of supervised learning or qualitative coding, or delegate this task to crowd workers via microtasking websites such as Amazon Mechanical Turk. But in negotiating the terms of the bet we realized that the simultaneous emergence over the last thirty years of the Web and remote sensing means that high-quality human input can be obtained in any number of creative ways, often without the humans involved even realizing that they are generating data, e.g., games with a purpose such as reCAPTCHA, as well as user interactions such as clicks, scrolls and swipes. Moreover, the kind of straightforward machine learning problems like classification, for which
traditional labeling suffices, are increasingly less commonly studied, and more sophisticated problems, such as machine translation or autonomous driving, often require newer, less conventional data acquisition methods.

As one example, a common domain for machine learning is health, where the prediction task could be a disease given a patient’s electronic health records. Medical diagnosis, like cancer or depression, usually requires a physician’s judgement, and so this seems like a clear-cut case of human labeling. But what about hypertension? In most cases, a human takes blood pressure and enters the data, but once those numbers are known a diagnosis of hypertension is relatively
judgement-free. And blood pressure can now be measured automatically, e.g., via drug store kiosks or wearable sensors, with no human (except the patient) involved.

Edge cases such as these became more ominous as we began to think about how to adjudicate the bet. We came to realize, given that what constitutes “human labeling” is, ironically enough, itself a rather slippery concept, that the best way to determine the winners would be to survey research papers at AAAI, NeurIPS, and the WebConf, each year over the next five years (to see the trend rather than two data points), and simply count the number of papers reporting results using a human labeled dataset.

The process shall be to:

  • Extract a random sample of 60 papers from each conference each year
  • Assign each paper to one pro and one con bettor
  • Each bettor will answer one question per paper after skimming or reading it: Does this paper use human labeled data for training or evaluating machine learning models?
  • In the case of disagreement, the bettors will discuss and the bookies will adjudicate, throwing out papers with no consensus

If the difference in expected proportion of papers whose consensus answer is “Yes” between responses in 2020 and 2025 satisfies a one-sided two-proportions z-test at the 5% level, the pro side wins. Otherwise, the con side wins.

1. Wearable Technology Market Size is Expected to Reach USD $57,653 Millions by the end of 2022
2. Wearable Technology Market Growing at a CAGR of 15.5% and Expected to Reach $51.6 billion by 2022
3. Using Machine Learning to “Nowcast” Precipitation in High Resolution
4. Data Annotation: The Billion Dollar Business Behind AI Breakthroughs
5. Data Annotation Tools Market worth over $5bn by 2026
6. Opinion | Artificial Intelligence Hits the Barrier of Meaning
7. Why Understanding Ambiguity in Natural Language Processing Is A Game Changer