Automation and Autonomy of Big Data-driven Algorithmic Decision-Making.

Author:Furnham, Philipp
 
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  1. Introduction

    The adoption of big data is thoroughly connected with the application of algorithms. (Wiesenberg et al., 2017) Algorithms that are created to identify and capitalize on models in big data discern social categories and detect data related to them. (Williams et al., 2018) Regulating the gathering and utilization of personally classifiable statistics is a difficult task, as there is an inscrutableness concerning what evidence is stored, how it is employed, who obtains value from their use, and the specific algorithms implemented to make decisions. (Sunrise Winter, 2018)

  2. Conceptual Framework and Literature Review

    Algorithmic decision-makers comprise epistemic and regularized conventions. As algorithmic accountability entails supplying clarifications for their decisions, such conjectures should make up a significant component of the substance of a decision-maker's interpretation. (Binns, 2018) Accountability is the capacity to supply satisfactory grounds for the purpose of making clear and establishing operations, assessments and approaches for a platform of individuals or organizations. As deciders, both in the private and in the public realm, gradually more depend on algorithms employing big data for their decision-making, clear-cut procedures of responsibility as regards the producing and use of algorithms in a context become steadily more pressing. (Vedder and Naudts, 2017) The heterogeneity of human behavior entails substantial amounts of data to be comprehended and replicated reliably in algorithmic contexts. (Olhede and Wolfe, 2018) When algorithms employ big data for relevant decisions, it is ineffective to remove social category information. (Williams et al., 2018)

  3. Methodology and Empirical Analysis

    Using and replicating data from Deloitte and Pew Research Center, I performed analyses and made estimates regarding % of Facebook users who have not/have intentionally tried to influence the content that appears on their news feed (by age group), % of U.S. adults who say the content on social media does/does not provide an accurate picture of how society feels about important issues, % of social media users who say it is not at all acceptable/not very acceptable/somewhat acceptable/very 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, and among U.S. social media users, the % of who say it would be hard to give up/not hard to give up social media (by age group). Data were analyzed using structural equation modeling.

  4. Results and Discussion

    Algorithms can assess based on greatly intricate data, but individuals are...

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