Resources: AI standards and regulations

Exercises: AI standards and regulations

Resources: Introduction to Machine Learning

Exercises: Introduction to Machine Learning

Resources: Career Advice (AIG)

Exercises: Career Advice (AIG)

Resources: Closing regulatory gaps through international agreements

Exercises: Closing regulatory gaps through international agreements

Resources: Projects  (AIG)

Exercises: Projects  (AIG)

Next steps: Programs

Resources: Challenges in achieving AI safety

Exercises: Challenges in achieving AI safety

Resources: Additional proposals

Exercises: Additional proposals

Resources: Introduction to potential catastrophic risks from AI

Exercises: Introduction to potential catastrophic risks from AI

Resources: Closing regulatory gaps through non-proliferation

Exercises: Closing regulatory gaps through non-proliferation

This week, we’ll dive into the question: What kind of work is happening in this ecosystem, and—if you’d like to contribute—how can you do so? Keep in mind that opportunities are rapidly changing; the below resources are far from a permanent or comprehensive picture. Also, if you’re at all curious about a type of work, we’d encourage you to check out the relevant resource(s)—people sometimes rule themselves out too early by having inaccurately narrow views about what they’re good fits for.
If you already work in policy, you may find some of the resources helpful for having more impact within AI policy, but you also may wish to start focusing on the projects section of the course. (For example, many people are not aware that there are engineering roles in AI governance, that AI strategy research can be highly empirical, or that introverted, non-U.S. citizens can succeed in U.S. policy roles.)
In one taxonomy, AI governance work ranges from highly foundational work (e.g. researching the risks) to highly applied work (e.g. implementing hardware export controls), with intermediate steps converting strategic insights into shovel-ready policy proposals (and also going the other way—drawing strategic insights from applied experience).
Be aware that there are funding opportunities to move toward an AI policy career (e.g. through “graduate study, unpaid internships, self-study, career transition and exploration periods”). These include Open Philanthropy’s career development funding, the Long-Term Future Fund, and various policy fellowships. We'll also keep our opportunities board up to date with what we know of.
Out of the following career resources (all from 2022), read all of the ones that seem potentially relevant to you. Many of these areas may be more neglected, promising, and accessible than they first seem, so we encourage you to have a broad notion of what career paths are potentially relevant to you (the following links include broad context on each path).

By the end of the unit, you should be able to:
\- List 3-5 types of careers that help with AI governance, and briefly sketch out how they contribute to the problems we’ve discussed on the course so far.
\- Have thought about 1-3 options you’re most excited about for your own career over the next 2-5 years. Explain in more detail what kind of skills they require, and how they contribute to AI safety.
\- Begin to have thought about your next steps to help pursue those paths (though note this will happen in much more detail during weeks 9-12).


Planning your next career steps can be hard, because you’re dealing with uncertainty about what you want to do, _and_ what jobs are even available.
A good first step is often to list out the potential options and conduct a shallow investigation to get a little more information about each one. This can help you decide where to go next.
Whilst this is particularly useful for people early in their career, we think this could be a useful exercise for people who are part way through their career, especially for thinking about how to apply what you’ve learned during this course to your next steps.
There are no correct answers, but we hope completing this exercise will help you to make better informed decisions.

**Step 1: make a long list of potential career paths.** You could pick jobs your friends have, or people whose work impresses you. You could also use the resources from this week to help generate more options. There’s also a long list of potentially relevant jobs on [this job board](https://jobs.80000hours.org/?refinementList%5Btags_area%5D%5B0%5D=AI%20safety%20%26%20policy).

**Step 2: for each of the most promising options, try to answer:**
- **Skills: what skills does this role require?**
    - **How interested are you in practising those types of skills?**
    - What would be an easy way to start developing that skillset?
- **Impact of the job: How does this role contribute to problems you care about?**
    - How is that measured, and will you feel satisfied with this?
- **Day to day: What would you actually be doing day to day?**
    - **Do you have any evidence that you will or won’t enjoy this work, yet?**
    - Who are the customers and the audience for this type of work? Are you motivated to work with those groups?
- **Role models:** are you thinking of anyone when you think about this role? If so, what lessons can you learn from the steps they took to obtain this position for your own career? If not, how could you find a potential role model?
**You may also wish to consider personal criteria**, such as working hours, location, career and financial stability, and so on. If you’re not sure about the answers to any of the above questions, this is great - it highlights questions for further research, or questions you could ask people you know in those industries.
Note you’ll be copying your exercise responses into the session, so perhaps make the more personal parts of your responses easy to omit.


AI Governance (2023)

Exercises: Career Advice (AIG)

Exercises