Conceptualization of the Human-Machine Symbiosis – A Literature Review

Gerber, Alina; Derckx, Patrick; Döppner, Daniel A.; Schoder, Detlef

doi:10.24251/hicss.2020.036

Cited by 23 publications

(19 citation statements)

References 44 publications

(120 reference statements)

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“…Machine learning tools need to be transparent to the human teammates to facilitate understanding 194,207 . The idea of trans parency is opposite to that of "blackbox" machine learning al gorithms, in which the patterns the algorithm follows to make a decision for a given patient are opaque to the person and even to the developer, making very challenging (if not impossible) for the affected person to understand how the system worked out an output for him/her 190 .…”

Section: Ethical Challengesmentioning

confidence: 99%

The promise of machine learning in predicting treatment outcomes in psychiatry

Bondar²,

et al. 2021

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For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real‐world clinical practice. Relatively few retrospective studies to‐date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.

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Section: Ethical Challengesmentioning

confidence: 99%

The promise of machine learning in predicting treatment outcomes in psychiatry

Bondar²,

et al. 2021

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“…While AI-based systems may approximate humans in their behavior and appearance, humans and AI-based systems still possess different yet complementary capabilities that can be combined to create mutual value [11,43]. For AI-based systems to be effective team members rather than just supportive tools, it is important that they take on suitable and consistent tasks and thus fulfill a certain role within the team [44].…”

Section: Human-ai Collaborationmentioning

confidence: 99%

“…Hybrid intelligence occurs when "humans and computers have complementary capabilities that can be combined to augment each other" [12, p. 3]. The concept of hybrid intelligence suggests that optimal results are achieved when the capabilities of humans and AI-based systems are combined to create mutual value by reinforcing and complementing each other [11,27,43]. The main aspect of this concept is that tasks are performed jointly by humans and AI, and dependent activities are coordinated.…”

Section: Human-ai Collaborationmentioning

confidence: 99%

Requirements for AI-based Teammates: A Qualitative Inquiry in the Context of Creative Workshops

Elshan¹,

Siemon²,

Vreede³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Innovation requires organizations to tap into the knowledge and creativity of teams. However, teams are confronted with massive amounts of data and information, necessitating a broad set of knowledge, methodologies, and approaches to solve problems and innovate. Consequently, team composition has become a critical challenge. Recent advances in artificial intelligence (AI) may assist in addressing this challenge. As AI is permeating both business and private sectors, organizational teams may be augmented with AI team members. However, given the nascent nature of this phenomenon, little is known about the specific roles and requirements for such AI teammates. Within an interview study we discover common challenges in teams and identify recurring capability gaps of participants and behaviors that negatively impact the team's collective performance. Based on our findings, we propose requirements for AI-based teammates to address these gaps and support beneficial collaboration between humans and AI in teams.

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“…We draw inspiration from this argument through the consideration of a specific type of humanmachine collaboration: human-machine symbiosis. Current conceptualizations of human-machine symbiosis are grounded in Licklider's "Man-Computer Symbiosis" [28], which imagined close cooperative relationships between humans and computers in order to achieve types of thought and data processing not possible outside the pairing [19]. Recent work by Cho and Rader contends that continuous learning is essential for mutually beneficial human-machine partnerships (e.g., such as where a machine is able to continuously learn from human activity and evaluation) [13,34].…”

Section: Designing For Human-ai Collaborationmentioning

confidence: 99%

Putting Tools in Their Place: The Role of Time and Perspective in Human-AI Collaboration for Qualitative Analysis

Feuston

Brubaker

2021

Proc. ACM Hum.-Comput. Interact.

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Large datasets or 'big data' corpora are typically the domain of quantitative scholars, who work with computational tools to derive numerical and descriptive insights. However, recent work asks how computational tools and other technologies, such as AI, can support qualitative scholars in developing deep and complex insights from large amounts of data. Addressing this question, Jiang et al. found that qualitative scholars are generally opposed to incorporating AI in their practices of data analysis. In this paper, we provide nuance to these earlier findings, showing that the stage of qualitative analysis matters for how scholars believe AI can and should be used. Through interviews with 15 CSCW and HCI qualitative researchers, we explore how AI can be included throughout different stages of qualitative analysis. We find that qualitative scholars are amenable to working with AI in diverse ways, such as for data exploration and coding, as long as it assists rather than automates their analytic work practice. Based on our analysis, we discuss how incorporating AI into qualitative research can shift some analytic practices, and how designing for human-AI collaboration in qualitative analysis necessitates considering tradeoffs in scale, abstraction, and task delegation.CCS Concepts: • Human-centered computing → Collaborative and social computing theory, concepts and paradigms; Collaborative and social computing design and evaluation methods.

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Conceptualization of the Human-Machine Symbiosis – A Literature Review

Cited by 23 publications

References 44 publications

The promise of machine learning in predicting treatment outcomes in psychiatry

The promise of machine learning in predicting treatment outcomes in psychiatry

Requirements for AI-based Teammates: A Qualitative Inquiry in the Context of Creative Workshops

Putting Tools in Their Place: The Role of Time and Perspective in Human-AI Collaboration for Qualitative Analysis

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