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Big Data Governance of Automated Algorithmic Decision-Making Processes.

1. Introduction

Algorithms depend on a range of characteristics (the particularities of the data set considered relevant to the decision) as information. Which attributes of the data set are carefully chosen as significant may be either pertinent or unsuitable for the imminent decision. (Martin, 2018) As the skills necessitated for individuals to comprehend the internal fabric of algorithms become more challenging, classificatory and interpretation devices are less effortlessly intelligible. (Fourcade and Healy, 2017) What algorithms do and the way they achieve outcomes is indissolubly bound to the circumstances in which they produce a result and which they assist in replication. (Neyland and Mollers, 2017)

2. Conceptual Framework and Literature Review

An algorithmic system is constituted of protocols, statistics, and a cluster of human and nonhuman participants. (Ananny and Crawford, 2018) Instead of interpreting algorithms as having hegemony, an agency via which they generate an impact, ascendancy stems from algorithmic association, i.e. the collection of individuals, objects, and resources forming a whole by practice and process. (Neyland and Mollers, 2017) Algorithms that determine what data reaches individuals online are relevant for the creation of media content (Badgett and Crehan, 2017; Ciobanu and Androniceanu, 2018; Gray-Hawkins, 2018; Lazaroiu et al., 2018; Moghtader, 2018; Nica et al., 2017a, b; Peters, 2017; Roberts and Marchais, 2018), being partly responsible for favoring the popular and setting up links between like-minded. (Klinger and Svensson, 2018) The intrinsic mechanics of the tremendous majority of ratings, rankings and algorithms presently in operation (Stroe, 2018a, b; Balica, 2018; Ciobanu et al., 2019; Hoffman and Friedman, 2018; Life, 2017; Nelson, 2018; Nica, 2018; Popescu Ljungholm, 2017a, b) are purposely very little known (Fourcade and Healy, 2017).

3. Methodology and Empirical Analysis

Using and replicating data from Deloitte, Pew Research Center, and Statista, we performed analyses and made estimates regarding % of social media users who say it is acceptable for social media sites to use data about them and their online activities to recommend events in their area/recommend someone they might want to know/show them ads for products and services/show them messages from political campaigns (by age group), % of Internet users worldwide who believe that selected algorithms are unbiased, and % of U.S. adults who say that it is possible for computer programs to make decisions without human bias/computer programs will always reflect bias of designers (by age group). The results of a study based on data collected from 5,600 respondents provide support for our research model. Using the structural equation modeling and employing the probability sampling technique, we gathered and analyzed data through a self-administrated questionnaire.

4. Results and Discussion

Algorithms are discriminatory, do not substitute media logic, and are unsuccessful as regards the analytical and reflexive component of agency. (Klinger and Svensson, 2018) algorithmic patterns may increase in size, mature, reconstruct and accomplish their operations at formidable speed, but individuals' cognitive resources are still inevitably bounded. (Kemper and Kolkman, 2018) When algorithmic calculations fail, have unplanned or unwanted ripples, algorithms cannot be held responsible as they plausibly have no premeditations and are deprived of the reflexive and analytical component of agency. (Klinger and Svensson, 2018) Algorithmic systems seem right via their positioning, wherein dissimilar constituents are customized and remodeled (Bratu, 2018; Cruciani, 2018; Lazaroiu, 2017; Madudova, 2017; Nica, 2017; Nica et al., 2018; Popescu et al., 2018a, b; Sanda and Krupka, 2018), cohering with procedures, persons, processes and certain types of relationships. (Neyland and Mollers, 2017) (Tables 1-6)

5. Conclusions and Implications

Admitting an algorithm's source code, its entire training data set, and its assessment information were made self-evident, it would nevertheless offer only a certain glimpse of its performance. (Ananny and Crawford, 2018) Algorithms are incapable of altering their agentic biases without assistance and their operations are consequently less elaborate than human actions. (Klinger and Svensson, 2018) In a society where dataveillance is the standard, simply operating in this realm indicates that individuals are designed into the technologies and systems of input gathering, production, and evaluation that configure social life chiefly in which both the mechanisms and the discourse of algorithms have become deep-seated. (Caplan and boyd, 2018)

Funding

This paper was supported by Grant GE-1875231 from the Big Data Analytics Research Unit, Dublin, and is an output of the scientific project VEGA 1/0210/19: Research of innovative attributes of quantitative and qualitative fundaments of the opportunistic earnings modelling.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Maria Kovacova

maria.kovacova@fpedas.uniza.sk

Faculty of Operation and Economics of Transport and Communications,

University of Zilina, Zilina, Slovak Republic

Tomas Kliestik

tomas.kliestik@fpedas.uniza.sk

Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic

Aurel Pera

aurelpera@yahoo.com

University of Craiova, Romania

(corresponding author)

Iulia Grecu

iulia.grecu@spiruharet.ro

Spiru Haret University, Constanta, Romania

Gheorghe Grecu

gheorghe.grecu@spiruharet.ro

Spiru Haret University, Constanta, Romania

Received 11 March 2019

Accepted 14 July 2019

doi:10.22381/RCP1820196
Table 1 % of U.S. adults who say the following examples of algorithmic
decision-making are...

                                                           Unacceptable

Criminal risk assessment for people up for parole          54
Automated resume screening of job applicants               53
Automated video analysis of job interviews                 68
Personal finance score using many types of consumer data   72

                                                           Acceptable

Criminal risk assessment for people up for parole          46
Automated resume screening of job applicants               47
Automated video analysis of job interviews                 32
Personal finance score using many types of consumer data   28

Sources: Pew Research Center; our survey among 5,600 individuals
conducted January 2019.

Table 2 % of U.S. adults who say that... (by age group)

                                                          18-29   30-49

It is possible for computer programs to make decisions    42      51
without human bias
Computer programs will always reflect bias of designers   58      49

                                                          50+

It is possible for computer programs to make decisions    33
without human bias
Computer programs will always reflect bias of designers   67

Sources: Pew Research Center; our survey among 5,600 individuals
conducted January 2019.

Table 3 Algorithmic efficiency across business functions (%)

Operations
Automate production and other operational processes    97
Predict quality issues and failures                    94
Monitor flow across supply chain                       93
Enable predictive asset maintenance                    92
Information technology
Automate testing of systems                            95
Monitor cyber threats                                  94
Automate system maintenance                            92
Support cyber incident response                        91
Human resources
Support workforce planning                             93
Source, recruit, and hire talent                       94
Manage performance of employees                        92
Increase employee engagement and retention             93
Finance
Advise on investment decisions                         94
Execute automated trades and deals                     95
Develop, analyze, and execute contracts                92
Generate automated reports                             95
Sales and marketing
Develop targeted marketing campaigns                   93
Measure effectiveness of marketing campaigns           94
Monitor social media for consumer insights             95
Calculate discounts based on customer data             95
Risk management
Identify, prioritize, and monitor risks                94
Spot fraud and conduct investigations                  96
Analyze business ecosystems                            93
Enforce regulatory compliance                          92

Sources: Deloitte; our survey among 5,600 individuals conducted January
2019

Table 4 % of social media users who say it is... for social media
sites to use data about them and their online activities to...

                             Not at all   Not very     Somewhat
                             acceptable   acceptable   acceptable

Recommend events in          14           19           46
their area
Recommend someone they       21           24           43
might want to know
Show them ads for products   22           32           33
and services
Show them messages from      32           33           28
political campaigns

                             Very
                             acceptable

Recommend events in          21
their area
Recommend someone they       12
might want to know
Show them ads for products   13
and services
Show them messages from       7
political campaigns

Sources: Pew Research Center; our survey among 5,600 individuals
conducted January 2019.

Table 5 % of social media users who say it is acceptable for social
media sites to use data about them and their online activities to...
(by age group)

                                              18-29  30-49  50-64  65+

Recommend events in their area                32     29     25     14
Recommend someone they might want to know     33     28     26     13
Show them ads for products and services       35     32     28      5
Show them messages from political campaigns   24     25     25     26

Sources: Pew Research Center; our survey among 5,600 individuals
conducted January 2019.

Table 6 % of Internet users worldwide who believe that selected
algorithms are unbiased

                                              Very confident

Facial recognition systems                    14
Search engines                                17
E-commerce platforms                          16
Credit score calculations                     11
Job application screenings                    13
Risk assessments used in judicial decisions   12
Predictive policing                           10
Social media news feeds                       12

                                              Somewhat confident

Facial recognition systems                    39
Search engines                                32
E-commerce platforms                          28
Credit score calculations                     33
Job application screenings                    32
Risk assessments used in judicial decisions   28
Predictive policing                           31
Social media news feeds                       27

Sources: Statista; our survey among 5,600 individuals conducted January
2019.
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Article Details
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Author:Kovacova, Maria; Kliestik, Tomas; Pera, Aurel; Grecu, Iulia; Grecu, Gheorghe
Publication:Review of Contemporary Philosophy
Geographic Code:4EXRO
Date:Jan 1, 2019
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