How long is a project cycle?

Typical project cycles last 2-3 months, with the next cycle starting in mid-January, lasting til early April. However, in special cases it may be possible for a project to span two cycles.

How does RAIL handle data privacy?

RAIL prioritizes projects with open-source data, that can be handled without privacy issues. However, if you have an interesting project involving private data, we still want to work with you. A framework for ensuring data privacy will be individually discussed with each partner, specialised to the needs of the project.

Who owns the IP of a project?

The partner retains all ownership of their data, knowledge, and proprietary systems used to solve the problem. The partner also owns the deliverable. As a baseline, RAIL owns any other processes used to create the deliverable – which is most often the algorithm – and which can be licensed non-exclusively from RAIL. This is negotiable on a case-by-case basis.

Who will I be working with?

Each team of Rhodes Scholars consists of approximately 50% technical (e.g. DPhil/MSc students in AI, Computer Science, Statistics, Mathematics) and 50% non-technical consultants or domain experts.* For example, a team working on a public health project may have an epidemiologist, a junior doctor, and a public policy student.

* Please see disclaimer below

What is expected of me?

Our project partners are required to appoint one liaison (normally the Senior Scholar) who is expected to hold once-weekly Skype calls with the team, and respond promptly by email. In addition, you will work with RAIL before the project launch to scope the engagement and agree on terms.

Why are you doing this?

Our motivations are to improve our skills – both in Artificial Intelligence, and in problem-solving – while making an impact at the same time. We are particularly interested in supporting Senior Scholars do more good in the world.

How do I know working with RAIL is right for my organization?

If you are not sure we can help but believe there may be a way, please contact us. We are always happy to discuss potential partnerships.

How do I know if my data is mature enough?

We are seeking projects with matured datasets, so that machine learning techniques can effectively be applied to them. This means we put an emphasis on projects with data that is:

  1. Clean: with few missing values, formatted in an easily manipulatable way.
  2. Large: ideally hundreds or thousands of observations, ML works best on big data.
  3. Concise: Few erroneous variables lowers the chance of finding patterns that are non-persistent in application.
  4. Repeatable: Has your data been collected in one go, or over many? Datasets with repetitive collection allow for stronger insight.

What if the project cycle timeframes don’t suit my needs?

If you are interested in collaborating with RAIL, but the project does not match to our cycle timeframes, please contact us.

What form does a deliverable take?

The precise nature of a deliverable is worked out individually with each partner. For example, a detailed report, a slide deck, data visualisation, or a presentation of key results. The partner can license non-exclusive use of proprietary algorithms or statistical processes from RAIL.

Do partnerships need to be exclusive?

RAIL does not require that projects be exclusively worked on by our organisation.

An Important Disclaimer

The Rhodes Artificial Intelligence Lab (RAIL) is a group of Rhodes Scholars interested in harnessing advanced technology for good. However, RAIL is not a part or affiliate of the Rhodes Trust or the Rhodes Scholarship Programme, and any information, material or views published by or available via RAIL does not necessarily represent the views or values of the Rhodes Trust, the Rhodes Scholarship Programme or the Rhodes Scholar community.