The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems

Dellermann, Dominik; Calma, Adrian; Lipusch, Nikolaus; Weber, Thorsten; Weigel, Sascha; Ebel, Philipp

doi:10.48550/arxiv.2105.03354

Cited by 12 publications

(20 citation statements)

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“…It is well known in the AI community that the tasks amenable for artificial and human intelligence may be quite divergent. For instance, the Moravec Paradox states that it is quite easy for computers to match or overshadow humans in intelligence tests or in logical games such as playing checkers but very difficult for AI to have the dexterity of toddlers when it comes to perception and mobility (Dellerman et al, 2021). The strength of human intelligence is in the intuition and common sense and the strength of machine intelligence is in analysis and computation.…”

Section: Hybrid Intelligence: Imagining Human and Artificial Intellig...mentioning

confidence: 99%

“…Human beings have a clear advance in general purpose intelligence and the case for developing hybrid intelligence (human intelligence augmented by AI) is strong as argued by Kamar (2016) and Dellerman et al (2021). Keeping humans in the loop is also important since human beings, and not the AI algorithms, are morally responsible.…”

Section: Hybrid Intelligence: Imagining Human and Artificial Intellig...mentioning

confidence: 99%

“…AI can help improve human intelligence in two main ways: it can be used to automate tasks for instance, the through machines; it can provide decision support to humans who can act inconsistently and sub optimally by violating probability rules. As discussed by Dellerman et al (2021), we can better achieve complex goals by combining human and AI, thereby returning improved performance compared to what each could have managed.…”

Section: Hybrid Intelligence: Imagining Human and Artificial Intellig...mentioning

confidence: 99%

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Toward accountable human-centered AI: rationale and promising directions

Qadir

Islam

Al‐Fuqaha

2022

JICES

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Purpose Along with the various beneficial uses of artificial intelligence (AI), there are various unsavory concomitants including the inscrutability of AI tools (and the opaqueness of their mechanisms), the fragility of AI models under adversarial settings, the vulnerability of AI models to bias throughout their pipeline, the high planetary cost of running large AI models and the emergence of exploitative surveillance capitalism-based economic logic built on AI technology. This study aims to document these harms of AI technology and study how these technologies and their developers and users can be made more accountable. Design/methodology/approach Due to the nature of the problem, a holistic, multi-pronged approach is required to understand and counter these potential harms. This paper identifies the rationale for urgently focusing on human-centered AI and provide an outlook of promising directions including technical proposals. Findings AI has the potential to benefit the entire society, but there remains an increased risk for vulnerable segments of society. This paper provides a general survey of the various approaches proposed in the literature to make AI technology more accountable. This paper reports that the development of ethical accountable AI design requires the confluence and collaboration of many fields (ethical, philosophical, legal, political and technical) and that lack of diversity is a problem plaguing the state of the art in AI. Originality/value This paper provides a timely synthesis of the various technosocial proposals in the literature spanning technical areas such as interpretable and explainable AI; algorithmic auditability; as well as policy-making challenges and efforts that can operationalize ethical AI and help in making AI accountable. This paper also identifies and shares promising future directions of research.

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Section: Hybrid Intelligence: Imagining Human and Artificial Intellig...mentioning

confidence: 99%

Section: Hybrid Intelligence: Imagining Human and Artificial Intellig...mentioning

confidence: 99%

Section: Hybrid Intelligence: Imagining Human and Artificial Intellig...mentioning

confidence: 99%

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Toward accountable human-centered AI: rationale and promising directions

Qadir

Islam

Al‐Fuqaha

2022

JICES

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“…The coordination between groups of artificial agents and humans will become one of the most challenging tasks for improving AI's ability to support human endeavours [1,2]. In order to do this, we first need to understand how intelligent agents make choices in the context of other intelligent agents, a complex task for which humans have specific and highly developed cognitive processes that are detectable even in early childhood [3].…”

Section: Introductionmentioning

confidence: 99%

What Can Game Theory Tell Us about an AI ‘Theory of Mind’?

Harré

2022

Games

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Game theory includes a rich source of methods for analysing strategic interactions where there are a small number of agents, each having only a few choices. In more complex settings though, where there are many choices over indefinite time horizons involving large social groups, these methods are unlikely to fully capture the causes of agent behaviour. If agents are able to simplify the task of understanding what others might do by modelling the constraints of others, particularly unobservable cognitive constraints, then the possible behavioural outcomes can be similarly restricted, thereby reducing the complexity of a social interaction. Having a cognitive representation of the unobserved causal states of others is an aspect of a ‘Theory of Mind’ and it plays a central role in the psychology of social interactions. In this article I examine a selection of results on the theory of mind and connect these with the ‘game theory of mind’ to draw conclusions regarding the complexity of one-on-one and large-scale social coordination. To make this explicit, I will illustrate the relationship between the two psychological terms ‘introspection’ and ‘theory of mind’ and the economic analysis of game theory, while retaining as much as possible of the richness of the psychological concepts. It will be shown that game theory plays an important role in modelling interpersonal relationships for both biological and artificial agents, but it is not yet the whole story, and some psychological refinements to game theory are discussed.

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“…This can take multiple forms [41,49], but leads to cases in which the human and the AI are expected to operate in a synergistic manner. Some examples include [26]: Co-evolution over time, Human-in-the-loop learning, Interactive or Active Learning, Socio-technological ensemble. Given these paradigms, the human and AI can be paired in multiple configurations depending on technical, social, and other considerations.…”

Section: Introductionmentioning

confidence: 99%

Modeling Human Behavior Part I -- Learning and Belief Approaches

Fuchs¹,

Passarella²,

Conti³

2022

Preprint

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There is a clear desire to model and comprehend human behavior. Trends in research covering this topic show a clear assumption that many view human reasoning as the presupposed standard in artificial reasoning. As such, topics such as game theory, theory of mind, machine learning, etc. all integrate concepts which are assumed components of human reasoning. These serve as techniques to attempt to both replicate and understand the behaviors of humans. In addition, next generation autonomous and adaptive systems will largely include AI agents and humans working together as teams. To make this possible, autonomous agents will require the ability to embed practical models of human behavior, which allow them not only to replicate human models as a technique to "learn", but to to understand the actions of users and anticipate their behavior, so as to truly operate in symbiosis with them. The main objective of this paper it to provide a succinct yet systematic review of the most important approaches in two areas dealing with quantitative models of human behaviors. Specifically, we focus on (i) techniques which learn a model or policy of behavior through exploration and feedback, such as Reinforcement Learning, and (ii) directly model mechanisms of human reasoning, such as beliefs and bias, without going necessarily learning via trial-and-error.CCS Concepts: • Human-centered computing; • General and reference → Surveys and overviews;

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The future of human-AI collaboration: a taxonomy of design knowledge for hybrid intelligence systems

Cited by 12 publications

References 0 publications

Toward accountable human-centered AI: rationale and promising directions

Toward accountable human-centered AI: rationale and promising directions

What Can Game Theory Tell Us about an AI ‘Theory of Mind’?

Modeling Human Behavior Part I -- Learning and Belief Approaches

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