Shared Prosperity: An "Inclusion Rider" to Hire Those Whose Jobs Will be Most Impacted by the Adoption of AVs
Evidence suggests that marginalized groups like non-English speakers in the U.S. will be hit hardest by the adoption of autonomous vehicles, since they are often employed to a disproportionate degree in the taxi and trucking industries, which each face considerable risk of disruption. As good community members, the AV industry could employ an "inclusion rider," which is roughly (in this context) a promise to hire those who have been impacted most by the incoming technology. Using the system outlined above, marginalized groups could be re-employed to participate in the development of the safety-infrastructure we have just proposed. Little training will be needed, for instance, to help propagate the Shared Semantic Space that enables "Community Mode" with useful, common-sense tags and categories that help to predict and prevent accidents. In NYC, no one would know this data better than cab drivers. The resulting data-set could be organized as a data-trust, which would ensure that contributors participated in revenue gained for its exploitation via a co-operative license.
