I would call the cluster "AI ethics". But there's no hard cutoff, no sufficient philosophical difference: it's mostly just social clustering. Here's my short diplomatic piece about the gap. 

We should do our best to resist forming explicit competing factions; as Prunkl and Whittlestone note, it's all one space. Here's a principled argument for doing this.

(Though it is hard to avoid being factional when one group are being extremely factional at you. And we don't need to think that each point in the space is equally worrying.)

I like Jon Kleinberg, Zachary Lipton, Carolyn Ashurst, Andrew Trask, Shakir Mohamed, Hanna Wallach, Michael Kearns, Cynthia Rudin, Yonadav Shavit, Deborah Raji, Aaron Roth, Adrian Weller, McKane Andrus, Subbarao Kambhampati, Iason Gabriel, Max Langenkamp, Arvind Narayanan. Zoe Cremer is hard to classify but shares their animus. David Manheim, Andrew Critch, and Dylan Hadfield-Menell cross the hall to some extent. You can look up AIES papers for more as well as FaccT. The big names tend to be less fair (ha). (I've never seen anyone near the other cluster make such a list about safety people.)

https://facctconference.org is the major conference in the area. It's interdisciplinary -- mix of technical ML work, social/legal scholarship, and humanities-type papers.

Some big names: Moritz Hardt, Arvind Narayanan, and Solon Barocas wrote a textbook https://fairmlbook.org and they and many of their students are important contributors. Cynthia Dwork is another big name in fairness, and Cynthia Rudin in explainable/interpretable ML. That's a non-exhaustive list but I think is a decent seed for a search through coauthors.

I believe there is in fact important technical overlap in the two problem areas. For example, https://causalincentives.com is research from a group of people who see themselves as working in AI safety. Yet people in the fair ML community are also very interested in causality, and study it for similar reasons using similar tools.

I think much of the expressed animosity is only because the two research communities seem to select for people with very different preexisting political commitments (left/social justice vs. neoliberal), and they find each other threatening for that reason.

On the other hand, there are differences. An illustrative one is that fair ML people care a lot about the fairness properties of linear models, both in theory and in practice right now. Whereas it would be strange if an AI Safety person cared at all about a linear model -- they're just too small and nothing like the kind of AI that could become unsafe.

Your question seems to be both about content and interpersonal relationships / dynamics. I think it's very helpful to split out the differences between the groups along those lines.

In terms of substantive content and focus, I think the three other responders outline very well; particularly on attitudes towards AGI timelines and types of models they are concerned about.

In terms of the interpersonal dynamics, my personal take is we're seeing a clash between left / social-justice and EA / long-termism play out stronger in this content area than most others, though to date I haven't seen any animus from the EA / long-termist side. In terms of explaining the clash, I guess it depends how detailed you want to get.

Could be minimalistic and sum it up as one or both sides hold stereotypical threat models of the other, and are not investigating these models but rather attacking based on them.

Could expand and explain why EA / long-termism evokes such a strong threat response to people from the left, especially marginalised communities and individuals who have been punished for putting forward ethical views - like Gebru herself.

I think the latter is important but requires lots of careful reflection and openness to their world views, which I think requires a much longer piece. (And if anyone is interested in collaborating on this, would be delighted!)