In a nutshell:

To reduce the risks posed by the rise of artificial intelligence, we need to figure out how to make sure that powerful AI systems do what we want. Many potential solutions to this problem will require a lot of high-quality data from humans to train machine learning models. Building excellent pipelines so that this data can be collected more easily could be an important way to support technical research into AI alignment, as well as lay the foundation for actually building aligned AIs in the future. If not handled correctly, this work risks making things worse, so this path needs people who can and will change directions if needed.

Sometimes recommended — personal fit dependent

This career will be some people's highest-impact option if their personal fit is especially good.

Review status

Based on a shallow investigation 

Why might becoming an expert in data collection for AI alignment be high impact?

We think it’s crucial that we work to positively shape the development of AI, including through technical research on how to ensure that any potentially transformative AI we develop does what we want it to do (known as the alignment problem). If we don’t find ways to align AI with our values and goals — or worse, don’t find ways to prevent AI from actively harming us or otherwise working against our values — the development of AI could pose an existential threat to humanity.

There are lots of different proposals for building aligned AI, and it’s unclear which (if any) of these approaches will work. A sizeable subset of these approaches require humans to give data to machine learning models, including include AI safety via debate, microscope AI, and iterated amplification.

These proposals involve collecting human data on tasks like:

  • Evaluating whether a critique of an argument was good
  • Breaking a difficult question into easier subquestions
  • Examining the outputs of tools that interpret deep neural networks
  • Using one model as a tool to make a judgement on how good or bad the outputs of another model are
  • Finding ways to make models behave badly (e.g. generating adversarial examples by hand)

Collecting this data — ideally by setting up scalable systems to both contract people to carry out these sorts of tasks as well as collect and communicate the results — could be a valuable way to support alignment researchers who use it in their experiments.

But also, once we have good alignment techniques, we may need AI companies around the world to have the capacity to implement them. That means developing systems and pipelines for the collection of this data now could make it easier to implement alignment solutions that require this data in the future. And if it’s easier, it’s more likely to actually happen.

What does this path involve?

Human data collection mostly involves hiring contractors to answer relevant questions and then creating well-designed systems to collect high-quality data from them.

This includes:

  • Figuring out who will be good at actually generating this data (i.e. doing the sorts of tasks that we listed earlier, like evaluating arguments), as well as how to find and hire these people
  • Designing training materials, processes, pay levels, and incentivisation structures for contractors
  • Ensuring good communication between researchers and contractors, for example by translating researcher needs into clear instructions for contractors (as well as being able to predict and prevent people misinterpreting these instructions)
  • Designing user interfaces to make it easy for contractors to complete their tasks as well as for alignment researchers to design and update tasks for contractors to carry out
  • Scheduling workloads among contractors, for example making sure that when data needs to be moved in sequence among contractors, the entire data collection can happen reasonably quickly
  • Assessing data quality, including developing ways of rapidly detecting problems with your data or using hierarchical schemes of more and less trusted contractors

Being able to do all these things well is a pretty unique and rare skill set (similar to entrepreneurship or operations), so if you’re a good fit for this type of work, it could be the most impactful thing you could do.

Avoiding harm

If you follow this path, it’s particularly important to make sure that you are able to exercise excellent judgement about when not to provide these services.

We think it’s extremely difficult to make accurate calls about when research into AI capabilities could be harmful.

For example, it sounds pretty likely to us that work that helps make current AI systems safe and useful will be fairly different from work that is useful for making transformative AI (when we’re able to build it) safe and useful. You’ll need to be able to make judgements about whether the work you are doing is good for this future task.

We’ve written an article about whether working at a leading AI company might cause harm, and how to avoid it.

If you think you might be a good fit for this career path, but aren’t sure how to avoid doing harm, our advising team may be able to help you decide what to do.

Example people

How to predict your fit in advance

The best experts at human data collection will have:

  • Experience designing surveys and social science experiments
  • Ability to analyse the data collected from experiments
  • Some familiarity with the field of AI alignment
  • Enough knowledge about machine learning to understand what sorts of data are useful to collect and the machine learning research process
  • At least some front-end software engineering knowledge
  • Some aptitude for entrepreneurship or operations

Data collection is often considered somewhat less glamorous than research, making it especially hard to find good people. So if you have three or more of these skills, you’re likely a better candidate than most!

How to enter

If you already have experience in this area, there are two main ways you might get a job as a human data expert:

If you don’t have enough experience to work directly on this now, you can gain experience in a few ways:

  • Do academic research, for example in psychology, sociology, economics, or another social science.
  • Work in human-computer interaction or software crowdsourcing.
  • Work for machine learning companies in labelling teams — and because these roles are less popular, they can be a great way to rapidly gain experience and promotions in machine learning organisations.

The Effective Altruism Long-Term Future Fund and the Survival and Flourishing Fund may provide funding for promising individuals to learn skills relevant to helping future generations — including human data collection. As a way of learning the necessary skills (and directly helping at the same time), you could apply for a grant to build a dataset that you think could be useful for AI alignment. The Machine Intelligence Research Institute has put up a bounty for such a dataset.

Find a job in this path

If you think you might be a good fit for this path and you’re ready to start looking at job opportunities, you may find relevant roles on our job board:

    View all opportunities

    Want one-on-one advice on pursuing this path?

    If you think this path might be a great option for you, but you need help deciding or thinking about what to do next, our team might be able to help.

    We can help you compare options, make connections, and possibly even help you find jobs or funding opportunities.


    Learn more about data collection for AI alignment

    Read next:  Learn about other high-impact careers

    Want to consider more paths? See our list of the highest-impact career paths according to our research.

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