Many of the artificial intelligence (AI) systems used nowadays have a very high level of accuracy but fail to explain their decisions. This is critical, especially in sensitive areas such as medicine and the health area at large but also for applications of the law, finance etc., where explanations for certain decisions are needed and are often useful and valuable as the decision itself. This paper presents a review of four different methods for creating transparency in AI systems. It also suggests a list of criteria under which circumstances one should use which methods.
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NYU, Stern Sch Business, Mkt, New York, NY USABoston Univ, Questrom Sch Business, Mkt, Boston, MA 02215 USA
Bonezzi, Andrea
Morewedge, Carey K.
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Boston Univ, Questrom Sch Business, Mkt, Boston, MA 02215 USA
Boston Univ, Questrom Sch Business, Boston, MA 02215 USABoston Univ, Questrom Sch Business, Mkt, Boston, MA 02215 USA
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Rice Univ, Med Humanities Program, 6100 Main St,MS-30, Houston, TX 77005 USA
Rice Univ, Dept English, 6100 Main St,MS-30, Houston, TX 77005 USARice Univ, Med Humanities Program, 6100 Main St,MS-30, Houston, TX 77005 USA
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Univ Paris 5 Descartes, Sorbonne Paris Cite, Pl Odeon, F-75005 Paris, France
Hop Cochin, AP HP, Dept Radiol, F-75014 Paris, FranceUniv Paris 5 Descartes, Sorbonne Paris Cite, Pl Odeon, F-75005 Paris, France
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Univ Paris Saclay, Dept Radiotherapie, INSERM, Gustave Roussy Canc Campus,U1030, F-94805 Villejuif, FranceUniv Paris Saclay, Dept Radiotherapie, INSERM, Gustave Roussy Canc Campus,U1030, F-94805 Villejuif, France
Sun, Roger
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Deutsch, Eric
Fournier, Laure
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Hop Europeen Georges Pompidou, Dept Radiol, 20 Rue Leblanc, F-75015 Paris, FranceUniv Paris Saclay, Dept Radiotherapie, INSERM, Gustave Roussy Canc Campus,U1030, F-94805 Villejuif, France