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Trust is believed to be a foundational cornerstone for artificial intelligence (AI). In April 2019 the European Commission High Level Expert Group on AI adopted the Ethics Guidelines for Trustworthy AI, stressing that human beings will only be able to confidently and fully reap the benefits of AI if they can trust the technology. Trustworthy AI is defined as ethical, lawful and robust AI. Three things strike me about the EC Guidelines. Firstly, though building trust in AI seems to be a shared aim, it is not explicated what trust is, and how it can be built and maintained. Secondly, the Guidelines ignore the widespread distinction made in philosophical literature between trust and reliance. Thirdly, it is not clear how the values have been selected with which AI has to align and what would happen if they came into conflict. In this paper, I shall provide a conceptual analysis of trust in contrast to reliance and ask when it is warranted to talk about trust in AI and trustworthy AI. I shall show how trust and risk are related and what benefits and risks are associated with narrow and general AI. Also, I shall point out that metaphorical talk about ethically aligned AI ignores the real disagreements we have about ethical values. [ABSTRACT FROM AUTHOR]

This paper articulates the methodology and reasoning for how biasing in the Independent Core Observer Model (ICOM) Cognitive Architecture for Artificial General Intelligence (AGI) is done. This includes the use of a forced western emotional model, the system "needs" hierarchy, fundamental biasing and the application of SSIVA theory at the high level as a basis for emotionally bound ethical and moral experience in ICOM systems and how that is manifested in system behavior and the mathematics that supports that experience or qualia in ICOM based systems. [ABSTRACT FROM AUTHOR]