scholarly journals Towards Data Justice Unionism? A Labour Perspective on AI Governance

2021 ◽  
pp. 267-284
Author(s):  
Lina Dencik
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Autonomous Systems ◽  
Learning Systems ◽  
Worker Productivity ◽  
Labour Movement ◽  
Profound Impact ◽  
Data Infrastructures

The dual occurrences of constant data collection and use of artificial and autonomous systems in the workplace are having a profound impact on workers’ lives. Workers are subjected to constant surveillance that not only monitor worker productivity but factors unrelated to work. At the same time, machine learning systems are using these data to transform how work is being allocated, assessed and completed and as a result, worker lives and value in the workplace and beyond. Yet governance frameworks for AI have thus far been advanced with a noticeable absence of worker voice, unions, and labour perspectives. In this chapter I will discuss how concerns about data and data infrastructures need to be situated as part of a workers’ rights agenda, the role of the labour movement in advancing alternative governance frameworks, and the potential for data justice unionism.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

Behavior Morphologies of Machine Learning Agents in Media Artworks

Leonardo ◽  
2019 ◽  
pp. 1-10
Author(s):  
Sofian Audry
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Artificial Life ◽  
Adaptive Systems ◽  
Learning Systems ◽  
Artificial Agents ◽  
Adaptive Agents ◽  
Learning Agents ◽  
The Aesthetic

Since the 1950s, a range of artists have used artificial agents in their work, in parallel with scientific research in cybernetics, artificial intelligence (AI) and artificial life (AL). In particular, an increasing number of artists work with machine learning and other adaptive systems. Through my own engagement with such systems, I analyze adaptive agents within the broader context of the aesthetic of behavior. As a result, I propose an aesthetic framework for understanding behaviors which considers the role of the observer as an adaptive perceiving agent, the unfathomable character of machine learning systems, and the morphology of behaviors as time-based phenomenon.

Cyber Gremlin: social networking, machine learning and the global war on Al-Qaida-and IS-inspired terrorism

2019 ◽  
Vol 27 (3) ◽  
pp. 238-265
Author(s):  
Jamil Ammar
Keyword(s):  
Machine Learning ◽  
Social Networking ◽  
Learning Technologies ◽  
Learning Systems ◽  
Cross Border ◽  
Law And Policy ◽  
Practical Reality ◽  
Global War ◽  
Automated Decision Making

Abstract Among our mundane and technical concepts, machine learning is currently one of the most important and widely used, but least understood. To date, legal scholars have conducted comparatively little work on its cognate concepts. This article critically examines the use of machine learning technologies to suppress or block access to al-Qaida and IS-inspired propaganda. It will: (i) demonstrate that, insofar as law and policy dictate that machine learning systems comply with desired constitutional norms, automated-decision making systems are not as effective as critics would like; (ii) emphasize that, under the current envisaged ‘proactive’ role of networking sites, equating radical and extreme ideas and ideology with ‘violence’ is a practical reality; and (iii) outline a workable strategy for cross-border legal and technical counterterrorism that satisfies the requirements for algorithmic fairness.

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

AI Magazine ◽  
2014 ◽  
Vol 35 (4) ◽  
pp. 105-120 ◽  
Author(s):  
Saleema Amershi ◽  
Maya Cakmak ◽  
William Bradley Knox ◽  
Todd Kulesza
Keyword(s):  
Machine Learning ◽  
Intelligent Systems ◽  
Learning Systems ◽  
Human Interaction ◽  
Interaction Patterns ◽  
Interactive Machine Learning ◽  
Effective Learning ◽  
The People ◽  
The Impact

Intelligent systems that learn interactively from their end-users are quickly becoming widespread. Until recently, this progress has been fueled mostly by advances in machine learning; however, more and more researchers are realizing the importance of studying users of these systems. In this article we promote this approach and demonstrate how it can result in better user experiences and more effective learning systems. We present a number of case studies that characterize the impact of interactivity, demonstrate ways in which some existing systems fail to account for the user, and explore new ways for learning systems to interact with their users. We argue 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. After giving a glimpse of the progress that has been made so far, we discuss the challenges that we face in moving the field forward.

Analisis Pengembalian Kredit Terhadap Profitabilitas Pada PT Bank Sumut Cabang Gunungsitoli

Owner ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 160
Author(s):  
Victorinus Laoli
Keyword(s):  
Data Collection ◽  
Positive Influence ◽  
Business Continuity ◽  
Working Capital ◽  
The People ◽  
Data Collection Technique

One manifestation of the important role of banking in a region, as implemented by PT Bank Sumut, Gunungsitoli Branch, is to distribute loans for investment, consumption and working capital for the people in the area. The purpose of providing credit for banks is the return of credit that earns interest and can increase income to finance activities and business continuity. From the results of research conducted with this data collection technique, it shows that PT Bank Sumut has a number of loans from 2009 to 2014 which each year rises. From this study, it is also known that the rate of credit repayment has a positive influence on the level of profitability.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Participant recruitment and data collection through Facebook: the role of personality factors

2017 ◽  
Author(s):  
Sean Chandler Rife ◽  
Kelly L. Cate ◽  
Michal Kosinski ◽  
David Stillwell
Keyword(s):  
Data Collection ◽  
Social Research ◽  
Personality Factors ◽  
The Internet ◽  
Data Sets ◽  
Social Networking Service ◽  
Participant Recruitment ◽  
Conducting Research ◽  
Promising Avenue

As participant recruitment and data collection over the Internet have become more common, numerous observers have expressed concern regarding the validity of research conducted in this fashion. One growing method of conducting research over the Internet involves recruiting participants and administering questionnaires over Facebook, the world’s largest social networking service. If Facebook is to be considered a viable platform for social research, it is necessary to demonstrate that Facebook users are sufficiently heterogeneous and that research conducted through Facebook is likely to produce results that can be generalized to a larger population. The present study examines these questions by comparing demographic and personality data collected over Facebook with data collected through a standalone website, and data collected from college undergraduates at two universities. Results indicate that statistically significant differences exist between Facebook data and the comparison data-sets, but since 80% of analyses exhibited partial η2 < .05, such differences are small or practically nonsignificant in magnitude. We conclude that Facebook is a viable research platform, and that recruiting Facebook users for research purposes is a promising avenue that offers numerous advantages over traditional samples.

Changing Role of Agriculture in Income and Employment, and Trends of Agricultural Worker Productivity in Indian States

2020 ◽  
pp. 183-189
Keyword(s):  
Green Revolution ◽  
Positive Trend ◽  
Worker Productivity ◽  
Indian States ◽  
Agricultural Worker ◽  
The Sixties ◽  
High Level ◽  
The Cost ◽  
Changing Role

The productivity of land has been often discussed and deliberated by the academia and policymakers to understand agriculture, however, very few studies have focused on the agriculture worker productivity to analyze this sector. This study concentrates on the productivity of agricultural workers from across the states taking two-time points into consideration. The agriculture worker productivity needs to be dealt with seriously and on a time series basis so that the marginal productivity of worker can be ascertained but also the dependency of worker on agriculture gets revealed. There is still disguised unemployment in all the states and high level of labour migration, yet most of the states showed the dependency has gone down. Although a state like Madhya Pradesh is doing very well in terms of income earned but that is at the cost of increased worker power in agriculture as a result of which, the productivity of worker has gone down. States like Mizoram, Meghalaya, Nagaland and Tripura, though small in size showed remarkable growth in productivity and all these states showed a positive trend in terms of worker shifting away from agriculture. The traditional states which gained the most from Green Revolution of the sixties are performing decently well, but they need to have the next major policy push so that they move to the next orbit of growth.

Sign in / Sign up

Export Citation Format

Share Document