Conference paper Open Access
Fernandes, Pedro M.; Santos, Francisco C.; Lopes, Manuel
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="041" ind1=" " ind2=" "> <subfield code="a">eng</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">AI ethics</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">Game theoretical analysis</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">AI regulation</subfield> </datafield> <controlfield tag="005">20201026122659.0</controlfield> <controlfield tag="001">4133505</controlfield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">INESC-ID and Instituto Superior Técnico, Univ. de Lisboa Lisbon, Portugal</subfield> <subfield code="a">Santos, Francisco C.</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">INESC-ID and Instituto Superior Técnico, Univ. de Lisboa Lisbon, Portugal</subfield> <subfield code="a">Lopes, Manuel</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">786331</subfield> <subfield code="z">md5:2a299ceedd67e532ea9b0e2e7199ff24</subfield> <subfield code="u">https://zenodo.org/record/4133505/files/AIES_2020_Adoption_Dynamics_and_Societal_Impact_of_AI_Systems_in_Complex_Networks_paper.pdf</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2020-02-01</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire</subfield> <subfield code="p">user-iv4xr-project</subfield> <subfield code="o">oai:zenodo.org:4133505</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">INESC-ID and Instituto Superior Técnico, Univ. de Lisboa Lisbon, Portugal</subfield> <subfield code="a">Fernandes, Pedro M.</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Adoption Dynamics and Societal Impact of AI Systems in Complex Networks</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-iv4xr-project</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution 4.0 International</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><p>We propose a game-theoretical model to simulate the dynamics of AI adoption in adaptive networks. This formalism allows us to understand the impact of the adoption of AI systems for society as a whole, addressing some of the concerns on the need for regulation. Using this model we study the adoption of AI systems, the distri- bution of the different types of AI (from selfish to utilitarian), the appearance of clusters of specific AI types, and the impact on the fitness of each individual. We suggest that the entangled evolution of individual strategy and network structure constitutes a key mech- anism for the sustainability of utilitarian and human-conscious AI. Differently, in the absence of rewiring, a minority of the population can easily foster the adoption of selfish AI and gains a benefit at the expense of the remaining majority.</p></subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.1145/3375627.3375847</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">conferencepaper</subfield> </datafield> </record>
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