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Open AccessJournal ArticleDOI

Machine learning in tutorials – Universal applicability, underinformed application, and other misconceptions:

Hendrik Heuer, +2 more
- 21 May 2021 - 
- Vol. 8, Iss: 1, pp 205395172110175
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TLDR
In this article, the authors present a machine learning approach to infer rules from data, which has become a key component of contemporary information systems, unlike prior information systems explicitly programmed in formal languages.
Abstract
Machine learning has become a key component of contemporary information systems. Unlike prior information systems explicitly programmed in formal languages, ML systems infer rules from data. This p...

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Citations
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From promise to practice: towards the realisation of AI-informed mental health care.

TL;DR: Barwick et al. as discussed by the authors explored the promises and challenges of artificial intelligence (AI)-based precision medicine tools in mental health care from clinical, ethical, and regulatory perspectives, and provided recommendations on how these challenges could be addressed from an interdisciplinary perspective to pave the way towards a framework for mental healthcare care, leveraging the combined strengths of human intelligence and AI.
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Proceedings ArticleDOI

Auditing the Biases Enacted by YouTube for Political Topics in Germany

TL;DR: In this paper, the applicability of laws that require broadcasters to give important political, ideological, and social groups adequate opportunity to express themselves in the broadcasted program of the service was explored.
Proceedings ArticleDOI

Auditing the Biases Enacted by YouTube for Political Topics in Germany

TL;DR: In this article, the applicability of laws that require broadcasters to give important political, ideological, and social groups adequate opportunity to express themselves in the broadcasted program of the service is explored.
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Unpaid labour in online freelancing platforms: between marketization strategies and self-employment regulation

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References
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Journal ArticleDOI

Some studies in machine learning using the game of checkers

TL;DR: In this article, two machine learning procedures have been investigated in some detail using the game of checkers, and enough work has been done to verify the fact that a computer can be programmed so that it will lear...
Journal ArticleDOI

Knowing in Practice: Enacting a Collective Capability in Distributed Organizing

TL;DR: In this article, the authors outline a perspective on knowing in practice which highlights the essential role of human action in knowing how to get things done in complex organizational work and suggest that the competence to do global product development is both collective and distributed, grounded in the everyday practices of organizational members.
MonographDOI

Algorithms of Oppression: How Search Engines Reinforce Racism

TL;DR: Noble's Algorithms of Oppression: How Search Engines Reinforce Racism is devastating as mentioned in this paper, which reduces to rubble the notion that technology is neutral and ideology-free.
Journal ArticleDOI

How the machine ‘thinks’: Understanding opacity in machine learning algorithms

TL;DR: In this paper, the authors consider the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news, etc.
Journal ArticleDOI

Power to the People: The Role of Humans in Interactive Machine Learning

TL;DR: It is argued that the design process for interactive machine learning systems should involve users at all stages: explorations that reveal human interaction patterns and inspire novel interaction methods, as well as refinement stages to tune details of the interface and choose among alternatives.
Trending Questions (2)
What are some common misconceptions about the ease of learning machine learning?

Common misconceptions include universal applicability of machine learning, underinformed application, and overestimating ease of learning due to inferring rules from data instead of explicit programming.

Can Machine Learning provides systems the ability to automatically learn and improve from experience without?

Our results show that machine learning is presented as being universally applicable and that the application of machine learning without special expertise is actively encouraged.