The concept of responsible artificial intelligence as the future of artificial intelligence in medicine

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Abstract

Active deployment of artificial intelligence (AI) systems in medicine creates many challenges. Recently, the concept of responsible artificial intelligence (RAI) was widely discussed, which is aimed at solving the inevitable ethical, legal, and social problems. The scientific literature was analyzed and the possibility of applying the RAI concept to overcome the existing AI problems in medicine was considered. Studies of possible AI applications in medicine showed that current algorithms are unable to meet the basic enduring needs of society, particularly, fairness, transparency, and reliability. The RAI concept based on three principles — accountability for AI activities and responsibility and transparency of findings (ART) — was proposed to address ethical issues. Further evolution, without the development and application of the ART concept, turns dangerous and impossible the use of AI in such areas as medicine and public administration. The requirements for accountability and transparency of conclusions are based on the identified epistemological (erroneous, non-transparent, and incomplete conclusions) and regulatory (data confidentiality and discrimination of certain groups) problems of using AI in digital medicine [2]. Epistemological errors committed by AI are not limited to omissions related to the volume and representativeness of the original databases analyzed. In addition, these include the well-known “black box” problem, i.e. the inability to “look” into the process of forming AI outputs when processing input data. Along with epistemological errors, normative problems inevitably arise, including patient confidentiality and discrimination of some social groups due to the refusal of some patients to provide medical data for training algorithms and as part of the analyzed databases, which will lead to inaccurate AI conclusions in cases of certain gender, race, and age. Importantly, the methodology of the AI data analysis depends on the program code set by the programmer, whose epistemological and logical errors are projected onto the AI. Hence the problem of determining responsibility in the case of erroneous conclusions, i.e. its distribution between the program itself, the developer, and the executor. Numerous professional associations design ethical standards for developers and a statutory framework to regulate responsibility between the links described. However, the state must play the greatest role in the development and approval of such legislation. The use of AI in medicine, despite its advantages, is accompanied by many ethical, legal, and social challenges. The development of RAI has the potential both to solve these challenges and to further the active and secure deployment of AI systems in digital medicine and healthcare.

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Active deployment of artificial intelligence (AI) systems in medicine creates many challenges. Recently, the concept of responsible artificial intelligence (RAI) was widely discussed, which is aimed at solving the inevitable ethical, legal, and social problems. The scientific literature was analyzed and the possibility of applying the RAI concept to overcome the existing AI problems in medicine was considered. Studies of possible AI applications in medicine showed that current algorithms are unable to meet the basic enduring needs of society, particularly, fairness, transparency, and reliability. The RAI concept based on three principles — accountability for AI activities and responsibility and transparency of findings (ART) — was proposed to address ethical issues. Further evolution, without the development and application of the ART concept, turns dangerous and impossible the use of AI in such areas as medicine and public administration. The requirements for accountability and transparency of conclusions are based on the identified epistemological (erroneous, non-transparent, and incomplete conclusions) and regulatory (data confidentiality and discrimination of certain groups) problems of using AI in digital medicine [2]. Epistemological errors committed by AI are not limited to omissions related to the volume and representativeness of the original databases analyzed. In addition, these include the well-known “black box” problem, i.e. the inability to “look” into the process of forming AI outputs when processing input data. Along with epistemological errors, normative problems inevitably arise, including patient confidentiality and discrimination of some social groups due to the refusal of some patients to provide medical data for training algorithms and as part of the analyzed databases, which will lead to inaccurate AI conclusions in cases of certain gender, race, and age. Importantly, the methodology of the AI data analysis depends on the program code set by the programmer, whose epistemological and logical errors are projected onto the AI. Hence the problem of determining responsibility in the case of erroneous conclusions, i.e. its distribution between the program itself, the developer, and the executor. Numerous professional associations design ethical standards for developers and a statutory framework to regulate responsibility between the links described. However, the state must play the greatest role in the development and approval of such legislation. The use of AI in medicine, despite its advantages, is accompanied by many ethical, legal, and social challenges. The development of RAI has the potential both to solve these challenges and to further the active and secure deployment of AI systems in digital medicine and healthcare.

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About the authors

Nikolai S. Germanov

N.I. Pirogov Russian National Research Medical University

Author for correspondence.
Email: n.s.germanov@gmail.com
ORCID iD: 0000-0003-1953-8794
Russian Federation, Moscow

References

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