scholarly journals People Analytics and Individual Autonomy: Employing Predictive Algorithms as Omniscient Gatekeepers in the Digital Age Workplace

2021 ◽  
Vol 2020 (3) ◽  
Author(s):  
Erica Pedersen
Keyword(s):  
Predictive Analytics ◽  
Common Law ◽  
Digital Age ◽  
Employment Outcomes ◽  
Behavioral Data ◽  
Employee Voice ◽  
Scientific Management ◽  
Individual Autonomy ◽  
Organizational Benefits ◽  
Predictive Algorithms

People Analytics is a powerful tool with immense promise for enhancing organizational insights. However, this Note argues that employers’ unfettered use of opaque predictive algorithms, which are trained on behavioral data to profile workers and guide employment outcomes, represents a significant threat to individual autonomy. Part II explores the emergence of People Analytics as a continuation and merger of historical approaches to scientific management in the American workplace. Part III contrasts the organizational benefits of predictive analytics against the uniquely intrusive, non-transparent, and sometimes arbitrary manner in which they are currently deployed against workers. Part IV discusses how People Analytics may hasten the erosion of employees’ normative rights in the workplace. It then discusses the insufficiency of existing regulatory and common law mechanisms to protect workers from arbitrary or discriminatory decisionmaking based on algorithmic profiling. Finally, Part V reviews some proposed solutions, emphasizing the importance of employee voice and the need for proactive regulations to enforce algorithmic transparency and protect individuals’ rights to privacy and autonomy.

scholarly journals Systematic Literature Review of Predictive Analysis Tools in Higher Education

2019 ◽  
Vol 9 (24) ◽  
pp. 5569 ◽  
Author(s):  
Martín Liz-Domínguez ◽  
Manuel Caeiro-Rodríguez ◽  
Martín Llamas-Nistal ◽  
Fernando A. Mikic-Fonte
Keyword(s):  
Higher Education ◽  
Literature Review ◽  
Teaching And Learning ◽  
Predictive Analytics ◽  
Early Warning Systems ◽  
Predictive Analysis ◽  
Dropping Out ◽  
Research Activity ◽  
Related Data ◽  
Predictive Algorithms

The topic of predictive algorithms is often regarded among the most relevant fields of study within the data analytics discipline. They have applications in multiple contexts, education being an important one of them. Focusing on higher education scenarios, most notably universities, predictive analysis techniques are present in studies that estimate academic outcomes using different kinds of student-related data. Furthermore, predictive algorithms are the basis of tools such as early warning systems (EWS): applications able to foresee future risks, such as the likelihood of students failing or dropping out of a course, and alert of such risks so that corrective measures can be taken. The purpose of this literature review is to provide an overview of the current state of research activity regarding predictive analytics in higher education, highlighting the most relevant instances of predictors and EWS that have been used in practice. The PRISMA guidelines for systematic literature reviews were followed in this study. The document search process yielded 1382 results, out of which 26 applications were selected as relevant examples of predictors and EWS, each of them defined by the contexts where they were applied and the data that they used. However, one common shortcoming is that they are usually applied in limited scenarios, such as a single course, evidencing that building a predictive application able to work well under different teaching and learning methodologies is an arduous task.

scholarly journals Predictive analytics in health care: how can we know it works?

2019 ◽  
Vol 26 (12) ◽  
pp. 1651-1654 ◽  
Author(s):  
Ben Van Calster ◽  
Laure Wynants ◽  
Dirk Timmerman ◽  
Ewout W Steyerberg ◽  
Gary S Collins
Keyword(s):  
Predictive Analytics ◽  
External Validation ◽  
Black Box ◽  
Sufficient Information ◽  
Commercial Exploitation ◽  
Machine Learning Methods ◽  
Predictive Algorithms ◽  
Performance Heterogeneity ◽  
Algorithm Implementation ◽  
Over Time

Abstract There is increasing awareness that the methodology and findings of research should be transparent. This includes studies using artificial intelligence to develop predictive algorithms that make individualized diagnostic or prognostic risk predictions. We argue that it is paramount to make the algorithm behind any prediction publicly available. This allows independent external validation, assessment of performance heterogeneity across settings and over time, and algorithm refinement or updating. Online calculators and apps may aid uptake if accompanied with sufficient information. For algorithms based on “black box” machine learning methods, software for algorithm implementation is a must. Hiding algorithms for commercial exploitation is unethical, because there is no possibility to assess whether algorithms work as advertised or to monitor when and how algorithms are updated. Journals and funders should demand maximal transparency for publications on predictive algorithms, and clinical guidelines should only recommend publicly available algorithms.

Learning about Democracy at Work: Cross-National Evidence on Individual Employee Voice Influencing Political Participation in Civil Society

ILR Review ◽  
2017 ◽  
Vol 71 (4) ◽  
pp. 956-985 ◽  
Author(s):  
John W. Budd ◽  
J. Ryan Lamare ◽  
Andrew R. Timming
Keyword(s):  
Civil Society ◽  
Spillover Effect ◽  
Electoral System ◽  
European Social Survey ◽  
Small Samples ◽  
Employee Voice ◽  
Individual Autonomy ◽  
Social Survey ◽  
Institutional Contexts ◽  
Cross National

Using European Social Survey data, this article analyzes the extent to which individual autonomy and participation in decision making at the workplace are linked empirically to individual political behaviors in civil society. The results, which are consistent with the hypothesis of a positive outward democratic spillover from the workplace to the political arena, point to the possibility of a learning effect. Much of the literature studies small samples in a single country, whereas we analyze more than 14,000 workers across 27 countries. The results do not appear to be driven by specific countries, which suggests that this spillover effect is a general phenomenon across a variety of institutional contexts, although some features of a country’s electoral system moderate some of the results.

scholarly journals Technocracy as a challenge to political freedom in the digital age

2021 ◽  
Vol 24 (4) ◽  
pp. 31-37
Author(s):  
Yevhen Laniuk
Keyword(s):  
Political Representation ◽  
Digital Age ◽  
Well Being ◽  
Political Freedom ◽  
Modern World ◽  
Scientific Management ◽  
Industrial Age ◽  
The Board Of Directors ◽  
The Republic ◽  
The Government

The government of the former Prime-Minister of Ukraine Olexiy Honcharuk named itself “the government of technocrats”. This shows that the concept of technocracy becomes attractive in Ukraine. Technocracy is the form of government, which attempts to distance itself from political representation or affiliation with a particular ideology. Technocrats derive their legitimacy from their skills and expertise, and focus primarily on problem-solving and optimizing the society’s useful functions. Technocracy has always been a promising political concept. The Republic by Plato can be regarded as the first attempt to substantiate a technocratic society, in which power proceeds from the expertise of its dominant elite. Technocracy was very appealing in the industrial age, when scientific management of factories inspired the idea that society at large could be governed by similar methods. Today, digital technologies and Big Data reinvigorate the technocratic project. In this article it has been shown that technocracy, if taken too far, can be antithetical to liberal democracy and its core value – political freedom. Technocratic society resembles a corporation run by the board of directors rather than a republic of citizens. We have pointed out the factors, which make it appealing in the modern world. We then have analyzed the ideas of Howard Scott, the founder of the movement Technocracy Inc., who advocated this political model in the industrial age, and Parag Khanna, who has made similar claims about the benefits of technocracy in the digital age. It has been proven that both these thinkers share the same illiberal mindset including the common faith in the applicability of scientific methods of social management without regard for popular votes and opinions, admiration of autocratic powers of the day, and disregard for democratic procedures, which they see as hurdles on the path toward economic well-being and political domination. Finally, we asked the question: if the challenge to political freedom in Ukraine proceeds from technocracy, will it be defended in the same way as during the three Ukrainian Maidans (1990, 2004, 2014)? We deliberately leave this question unanswered, hoping that the answer will be investigated in future publications.

scholarly journals Descriptive and Predictive Analytics on Adventure Works Cycle: A Corporate Decision Making

2020 ◽  
Vol 8 (6) ◽  
pp. 2041-2048
Keyword(s):  
High Performance ◽  
Predictive Analytics ◽  
Analytical Techniques ◽  
Business World ◽  
Computing Power ◽  
Predictive Algorithms ◽  
Business Analysis ◽  
Different Types ◽  
Business Condition ◽  
Intuitive Grasp

Business Analysis has become one of the crucial elements of any business in this data-driven business world. This is at the frontline where the data analytics support the strategic management to make effective decisions with immense computing power. This paper investigates the big data problems of Adventure Works Cycles (AWC) by using analytical techniques and integrate different methods of knowledge discovery and data mining via descriptive and predicative analytics. The descriptive analytics revealed the prevailing business condition which could aid to make effective decisions. Consequently, an empirical study was performed to explore different types of predictive models to predict the future occurrences. Furthermore, a comparative analysis using different predictive algorithms which provides evidence that High-Performance Forest algorithm is particularly operative on the prediction of future occurrences with the accuracy of 80%, ROC index 0.878 and the cumulative lift value of 1.82. This study provides an intuitive grasp of the concept to forecast, find patterns and rules to increase AWC’s overall sales performance and improve overall lead scoring more accurately.

scholarly journals Task-specific information outperforms surveillance-style big data in predictive analytics

2021 ◽  
Vol 118 (14) ◽  
pp. e2020258118
Author(s):  
Andreas Bjerre-Nielsen ◽  
Valentin Kassarnig ◽  
David Dreyer Lassen ◽  
Sune Lehmann
Keyword(s):  
Academic Performance ◽  
Online Education ◽  
Administrative Data ◽  
Predictive Analytics ◽  
Ground Truth ◽  
Predictive Performance ◽  
Third Party ◽  
Behavioral Data ◽  
Specific Information ◽  
Individual Level

Increasingly, human behavior can be monitored through the collection of data from digital devices revealing information on behaviors and locations. In the context of higher education, a growing number of schools and universities collect data on their students with the purpose of assessing or predicting behaviors and academic performance, and the COVID-19–induced move to online education dramatically increases what can be accumulated in this way, raising concerns about students’ privacy. We focus on academic performance and ask whether predictive performance for a given dataset can be achieved with less privacy-invasive, but more task-specific, data. We draw on a unique dataset on a large student population containing both highly detailed measures of behavior and personality and high-quality third-party reported individual-level administrative data. We find that models estimated using the big behavioral data are indeed able to accurately predict academic performance out of sample. However, models using only low-dimensional and arguably less privacy-invasive administrative data perform considerably better and, importantly, do not improve when we add the high-resolution, privacy-invasive behavioral data. We argue that combining big behavioral data with “ground truth” administrative registry data can ideally allow the identification of privacy-preserving task-specific features that can be employed instead of current indiscriminate troves of behavioral data, with better privacy and better prediction resulting.

Sign in / Sign up

Export Citation Format

Share Document