Beyond Near- and Long-Term: Towards a Clearer Account of Research Priorities in AI Ethics and Society
This is an incremental contribution aimed at improving communication and agenda-setting for researchers in AI ethics and society.
The paper critiques the near/long-term distinction in AI ethics research for being ambiguous and inconsistent, proposing a four-dimensional framework to clarify research priorities and foster collaboration.
One way of carving up the broad "AI ethics and society" research space that has emerged in recent years is to distinguish between "near-term" and "long-term" research. While such ways of breaking down the research space can be useful, we put forward several concerns about the near/long-term distinction gaining too much prominence in how research questions and priorities are framed. We highlight some ambiguities and inconsistencies in how the distinction is used, and argue that while there are differing priorities within this broad research community, these differences are not well-captured by the near/long-term distinction. We unpack the near/long-term distinction into four different dimensions, and propose some ways that researchers can communicate more clearly about their work and priorities using these dimensions. We suggest that moving towards a more nuanced conversation about research priorities can help establish new opportunities for collaboration, aid the development of more consistent and coherent research agendas, and enable identification of previously neglected research areas.