Big Data Governance of Automated Algorithmic Decision-Making Processes.
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)
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.
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.
Ananny, M, and K. Crawford (2018). "Seeing without Knowing: Limitations of the Transparency Ideal and Its Application to Algorithmic Accountability," New Media & Society 20(3): 973-989.
Badgett, L. M. V., and P. Crehan (2017). "Developing Actionable Research Priorities for LGBTI Inclusion," Journal of Research in Gender Studies 7(1): 218-247.
Balica, R. (2018). "Big Data Learning Analytics and Algorithmic Decision-Making in Digital Education Governance," Analysis and Metaphysics 17: 128-133.
Bratu, S. (2018). "The Ethics of Algorithmic Sociality, Big Data Analytics, and Data-Driven Research Patterns," Review of Contemporary Philosophy 17: 100-106.
Caplan, R., and d. boyd (2018). "Isomorphism through Algorithms: Institutional Dependencies in the Case of Facebook," Big Data & Society 5(1): 1-12.
Ciobanu, A., and A. Androniceanu (2018). "Integrated Human Resources Activities--The Solution for Performance Improvement in Romanian Public Sector Institutions," Management Research and Practice 10(3): 60-79.
Ciobanu, A., A. Androniceanu, and G. Lazaroiu (2019). "An Integrated Psycho-Sociological Perspective on Public Employees' Motivation and Performance," Frontiers in Psychology 10: 36.
Cruciani, M. (2018). "Explicit Communication: An Interest and Belief-based Model," Linguistic and Philosophical Investigations 17: 50-70.
Fourcade, M., and K. Healy (2017). "Categories All the Way Down," Historical Social Research 42(1): 286-296.
Gray-Hawkins, M. (2018). "Collective Movements, Digital Activism, and Protest Events: The Effectiveness of Social Media Concerning the Organization of Large-Scale Political Participation," Geopolitics, History, and International Relations 10(2): 64-69.
Hoffman, S. F., and H. H. Friedman (2018). "Machine Learning and Meaningful Careers: Increasing the Number of Women in STEM," Journal of Research in Gender Studies 8(2): 11-27.
Kemper, J., and D. Kolkman (2018). "Transparent to Whom? No Algorithmic Accountability without a Critical Audience," Information, Communication & Society. doi: 10.1080/1369118X.2018.1477967
Klinger, U., and J. Svensson (2018). "The End of Media Logics? On Algorithms and Agency," New Media & Society 20(12): 4653-4670.
Lazaroiu, G. (2017). "The Routine Fabric of Understandable and Contemptible Lies," Educational Philosophy and Theory 49(6): 573-574.
Lazaroiu, G., M. Kovacova, J. Kliestikova, P. Kubala, K. Valaskova, and V. V. Dengov (2018). "Data Governance and Automated Individual Decision-Making in the Digital Privacy General Data Protection Regulation," Administratie si Management Public 31: 132-142.
Life, J. J. (2017). "An Analysis of Linguistic Normativity and Communication as a Response to Objections to a Biopsychological Foundation for Linguistics," Linguistic and Philosophical Investigations 16: 49-79.
Madudova, E. (2017). "The Importance of Supporting the Business Activities of Creative Industries," Ekonomicko-manazerske spektrum 11(1): 37-47.
Martin, K. (2018). "Ethical Implications and Accountability of Algorithms," Journal of Business Ethics. doi: 10.1007/s10551-018-3921-3
Moghtader, B. (2018). "Pastorate Power, Market Liberalism and a Knowing without Knowing," Knowledge Cultures 6(1): 18-35.
Nelson, C. (2018). "Beyond Prometheus: Creativity, Discourse, Ideology, and the Anthropocene," Knowledge Cultures 6(2): 111-131.
Neyland, D., and N. Mollers (2017). "Algorithmic IF... THEN Rules and the Conditions and Consequences of Power," Information, Communication & Society 20(1): 45-62.
Nica, E. (2017). "Political Mendacity and Social Trust," Educational Philosophy and Theory 49(6): 571-572.
Nica, E., M. Comanescu, and C. Manole (2017a). "Digital Reputation and Economic Trust in the Knowledge Labor Market," Journal of Self-Governance and Management Economics 5(3): 83-88.
Nica, E., A.-M. Potcovaru, and C.-O. Mirica (Dumitrescu) (2017b). "A Question of Trust: Cognitive Capitalism, Digital Reputation Economy, and Online Labor Markets," Economics, Management, and Financial Markets 12(3): 64-69.
Nica, E. (2018). "Will Robots Take the Jobs of Human Workers? Disruptive Technologies that May Bring About Jobless Growth and Enduring Mass Unemployment," Psychosociological Issues in Human Resource Management 6(2): 56-61.
Nica, E., C. Manole, and C. I. Stan (2018). "A Laborless Society? How Highly Automated Environments and Breakthroughs in Artificial Intelligence Bring About Innovative Kinds of Skills and Employment Disruptions, Altering the Nature of Business Process and Affecting the Path of Economic Growth," Journal of Self-Governance and Management Economics 6(4): 25-30.
Peters, M. A. (2017). "Disciplinary Technologies and the School in the Epoch of Digital Reason: Revisiting Discipline and Punish after 40 Years," Contemporary Readings in Law and Social Justice 9(1): 28-46.
Popescu Ljungholm, D. (2017a). "Performance Appraisal Satisfaction in Organizations," Review of Contemporary Philosophy 16: 136-142.
Popescu Ljungholm, D. (2017b). "Global Policy Mechanisms, Intergovernmental Power Politics, and Democratic Decision-Making Modes of Transnational Public Administration," Geopolitics, History, and International Relations 9(2): 199-205.
Popescu, G. H., I. E. Petrescu, and O. M. Sabie (2018a). "Algorithmic Labor in the Platform Economy: Digital Infrastructures, Job Quality, and Workplace Surveillance," Economics, Management, and Financial Markets 13(3): 74-79.
Popescu, G. H., I. E. Petrescu, O. M. Sabie, and M. Musat (2018b). "Labor-Displacing Technological Change and Worldwide Economic Insecurity: How Automation and the Creation of Innovative Tasks Shape Inequality," Psychosociological Issues in Human Resource Management 6(2): 80-85.
Roberts, T., and G. Marchais (2018). "Assessing the Role of Social Media and Digital Technology in Violence Reporting," Contemporary Readings in Law and Social Justice 10(2): 9-42.
Sanda, M., and J. Krupka (2018). "Quality of Life Evaluation as Decision Support in Public Administration for Innovation and Regions Development," Administratie si Management Public 30: 51-66.
Stroe, M. A. (2018a). "Henri Coanda and Constantin Brancusi: Unveiling the Future," Creativity 1(1): 3-80.
Stroe M. A. (2018b). "Harold Bloom and the Brain-Wave Theory of Creativity," Creativity 1(2): 3-111.
Faculty of Operation and Economics of Transport and Communications,
University of Zilina, Zilina, Slovak Republic
Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic
University of Craiova, Romania
Spiru Haret University, Constanta, Romania
Spiru Haret University, Constanta, Romania
Received 11 March 2019
Accepted 14 July 2019
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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|Author:||Kovacova, Maria; Kliestik, Tomas; Pera, Aurel; Grecu, Iulia; Grecu, Gheorghe|
|Publication:||Review of Contemporary Philosophy|
|Date:||Jan 1, 2019|
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