Hybrid Cognitive-Affective Strategies for AI Safety

Aliman, Nadisha-Marie

doi:10.33540/203

Cited by 5 publications

(3 citation statements)

References 275 publications

(569 reference statements)

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“…Given the present state of the art it may be wise to focus more on the whole system of multiple AI innovations with humans as a crucial connecting and supervising factor. This also implies the establishment and formalization of legal boundaries and proper (effective, ethical, safe) goals for AI systems (Elands et al, 2019;Aliman, 2020). So this human factor (legislator, user, "collaborator") needs to have good insight into the characteristics and capacities of biological and artificial intelligence (under all sorts of tasks and working conditions).…”

Section: Conclusion and Frameworkmentioning

confidence: 99%

Human- versus Artificial Intelligence

Korteling¹,

Boer-Visschedijk²,

Blankendaal³

et al. 2021

Front. Artif. Intell.

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AI is one of the most debated subjects of today and there seems little common understanding concerning the differences and similarities of human intelligence and artificial intelligence. Discussions on many relevant topics, such as trustworthiness, explainability, and ethics are characterized by implicit anthropocentric and anthropomorphistic conceptions and, for instance, the pursuit of human-like intelligence as the golden standard for Artificial Intelligence. In order to provide more agreement and to substantiate possible future research objectives, this paper presents three notions on the similarities and differences between human- and artificial intelligence: 1) the fundamental constraints of human (and artificial) intelligence, 2) human intelligence as one of many possible forms of general intelligence, and 3) the high potential impact of multiple (integrated) forms of narrow-hybrid AI applications. For the time being, AI systems will have fundamentally different cognitive qualities and abilities than biological systems. For this reason, a most prominent issue is how we can use (and “collaborate” with) these systems as effectively as possible? For what tasks and under what conditions, decisions are safe to leave to AI and when is human judgment required? How can we capitalize on the specific strengths of human- and artificial intelligence? How to deploy AI systems effectively to complement and compensate for the inherent constraints of human cognition (and vice versa)? Should we pursue the development of AI “partners” with human (-level) intelligence or should we focus more at supplementing human limitations? In order to answer these questions, humans working with AI systems in the workplace or in policy making have to develop an adequate mental model of the underlying ‘psychological’ mechanisms of AI. So, in order to obtain well-functioning human-AI systems, Intelligence Awareness in humans should be addressed more vigorously. For this purpose a first framework for educational content is proposed.

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“…The above is particularly true of systems that adapt and learn (i.e., machine learning) from their environmental contexts (Aliman, 2020;Ivanov, 1993;Wernaart, 2021). Similarly, any single system can both support and constrain other systems to make them more or less robust.…”

Section: Systems Theory and Systems Engineeringmentioning

confidence: 99%

“…Elands et al (2019) provide one model route for the design level in conceptualizing exactly how more in situ operations can take place and remain under MHC. Likewise, there are developments on advanced intelligent systems (e.g., in the domain of cyber physical systems, Artificial General Intelligence, and AI safety) that explore how systems can be (technically) self-aware, have hybrid AI, and be oriented towards a well-specified objective, utility function, or goal formulated by humans (Aliman, 2020). An important consequence of this research is that the objective, utility, or value function is not part of the design of the system [orthogonality between goal and intelligence] (Aliman, 2020).…”

Section: Limitations and Areas For Future Researchmentioning

confidence: 99%

Coupling levels of abstraction in understanding meaningful human control of autonomous weapons: a two-tiered approach

Umbrello

2021

Ethics Inf Technol

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The international debate on the ethics and legality of autonomous weapon systems (AWS), along with the call for a ban, primarily focus on the nebulous concept of fully autonomous AWS. These are AWS capable of target selection and engagement absent human supervision or control. This paper argues that such a conception of autonomy is divorced from both military planning and decision-making operations; it also ignores the design requirements that govern AWS engineering and the subsequent tracking and tracing of moral responsibility. To show how military operations can be coupled with design ethics, this paper marries two different kinds of meaningful human control (MHC) termed levels of abstraction. Under this two-tiered understanding of MHC, the contentious notion of ‘full’ autonomy becomes unproblematic.

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Hybrid Cognitive-Affective Strategies for AI Safety

Cited by 5 publications

References 275 publications

Human- versus Artificial Intelligence

Coupling levels of abstraction in understanding meaningful human control of autonomous weapons: a two-tiered approach

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