Implementing artificial intelligence ethics in trustworthy systems development : extending ECCOLA to cover information governance principles
This Master's thesis assesses how to extend a higher-level developmental method
for trustworthy artificial intelligent systems, ECCOLA, by evaluating it with
Information Governance principles. Artificial intelligent systems are ubiquitous,
with their application prevalent in virtually all sectors. In addition, Artificial
intelligent systems rely on data and information they collect from users for their
development. These issues have prompted ethical concerns, especially as their
usage crosses boundaries in sensitive areas such as health, transportation, and
security, calling for better governance. As such, there is a need for developing
ethical artificial intelligent systems with effective governance that users can
trust with their information. Several guidelines exist to help facilitate these developments;
however, very few transition into methods with virtually no method
existing for higher-level development methods. ECCOLA is proposed as a
solution in transitioning from guidelines to development methods at higher
levels. The study extends ECCOLA by evaluating its ethical tenets with Information
Governance principles (Generally Accepted Recordkeeping Principles,
GARP®) as a governance framework to improve its robustness in line with ethical
guidelines. This was accomplished by following the Design Science Research
methodology approach using a conceptual framework based on ethical
guidelines of the European Commission and content analysis. The findings reveal
a vulnerability of the GARP® principles of Retention and Disposition in
ECCOLA. A possible solution artifact has been developed, which remains to be
tested.
...
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Samankaltainen aineisto
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Governance of Ethical and Trustworthy Al Systems : Research Gaps in the ECCOLA Method
Agbese, Mamia; Alanen, Hanna-Kaisa; Antikainen, Jani; Halme, Erika; Isomäki, Hannakaisa; Jantunen, Marianna; Kemell, Kai-Kristian; Rousi, Rebekah; Vainio-Pekka, Heidi; Vakkuri, Ville (IEEE, 2021)Advances in machine learning (ML) technologies have greatly improved Artificial Intelligence (Al) systems. As a result, Al systems have become ubiquitous, with their application prevalent in virtually all sectors. However, ... -
ECCOLA : a Method for Implementing Ethically Aligned AI Systems
Vakkuri, Ville; Kemell, Kai-Kristian; Abrahamsson, Pekka (IEEE, 2020)Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that ... -
ECCOLA : a method for implementing ethically aligned AI systems
Vakkuri, Ville; Kemell, Kai-Kristian; Jantunen, Marianna; Halme, Erika; Abrahamsson, Pekka (Elsevier, 2021)Artificial Intelligence (AI) systems are becoming increasingly widespread and exert a growing influence on society at large. The growing impact of these systems has also highlighted potential issues that may arise from ... -
Adversarial Attack’s Impact on Machine Learning Model in Cyber-Physical Systems
Vähäkainu, Petri; Lehto, Martti; Kariluoto, Antti (Peregrine Technical Solutions, 2020)Deficiency of correctly implemented and robust defence leaves Internet of Things devices vulnerable to cyber threats, such as adversarial attacks. A perpetrator can utilize adversarial examples when attacking Machine ... -
Practices and Infrastructures for Machine Learning Systems : An Interview Study in Finnish Organizations
Muiruri, Dennis; Lwakatare, Lucy Ellen; Nurminen, Jukka K.; Mikkonen, Tommi (Institute of Electrical and Electronics Engineers (IEEE), 2022)Using interviews, we investigated the practices and toolchains for machine learning (ML)-enabled systems from 16 organizations across various domains in Finland. We observed some well-established artificial intelligence ...
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