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Naslov:Transferring black-box decision making to a white-box model
Avtorji:ID Žlahtič, Bojan (Avtor)
ID Završnik, Jernej (Avtor)
ID Blažun Vošner, Helena (Avtor)
ID Kokol, Peter (Avtor)
Datoteke:.pdf electronics-13-01895.pdf (12,25 MB)
MD5: D497C4F8DA07EB2DD861681D62816259
 
URL https://www.mdpi.com/2079-9292/13/10/1895
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
FNM - Fakulteta za naravoslovje in matematiko
Opis:In the rapidly evolving realm of artificial intelligence (AI), black-box algorithms have exhibited outstanding performance. However, their opaque nature poses challenges in fields like medicine, where the clarity of the decision-making processes is crucial for ensuring trust. Addressing this need, the study aimed to augment these algorithms with explainable AI (XAI) features to enhance transparency. A novel approach was employed, contrasting the decision-making patterns of black-box and white-box models. Where discrepancies were noted, training data were refined to align a white-box model’s decisions closer to its black-box counterpart. Testing this methodology on three distinct medical datasets revealed consistent correlations between the adapted white-box models and their black-box analogs. Notably, integrating this strategy with established methods like local interpretable model-agnostic explanations (LIMEs) and SHapley Additive exPlanations (SHAPs) further enhanced transparency, underscoring the potential value of decision trees as a favored white-box algorithm in medicine due to its inherent explanatory capabilities. The findings highlight a promising path for the integration of the performance of black-box algorithms with the necessity for transparency in critical decision-making domains.
Ključne besede:explainable artificial intelligence, XAI, machine learning, deep learning, white box, black box
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:05.04.2024
Datum sprejetja članka:10.05.2024
Datum objave:12.05.2024
Založnik:MDPI
Leto izida:2024
Št. strani:16 str.
Številčenje:Vol. 13, iss. 10, [article no.] 1895
PID:20.500.12556/DKUM-96749 Novo okno
UDK:004.5
COBISS.SI-ID:195146755 Novo okno
DOI:10.3390/electronics13101895 Novo okno
ISSN pri članku:2079-9292
Avtorske pravice:© 2024 by the authors
Datum objave v DKUM:27.01.2026
Število ogledov:301
Število prenosov:3
Metapodatki:XML DC-XML DC-RDF
Področja:Ostalo
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Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.
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Gradivo je del revije

Naslov:Electronics
Skrajšan naslov:Electronics
Založnik:MDPI
ISSN:2079-9292
COBISS.SI-ID:523068953 Novo okno

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:umetna inteligenca, globoko učenje, strojno učenje


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