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REGULATION OF AUTOMATED INDIVIDUAL DECISION-MAKING AND ARTIFICIALLY INTELLIGENT ALGORITHMIC SYSTEMS: IS THE GDPR A POWERFUL ENOUGH MECHANISM TO PROTECT DATA SUBJECTS?

1. Introduction

The General Data Protection Regulation (GDPR) broadens the protection against judgments made exclusively on grounds of automated processing to encompass not just summarizing data subjects but also any other kind of such activity. The data protection criteria apply to this mining, but remarkably relevant are the stipulations of the first principle, which requires that handling of personal data must be authorized, nondiscriminatory, and unambiguous. Data subjects have the option not to be subject to decision-making (Bratu, 2017; Kliestik et al., 2018a; Orlova, 2017), comprising profiling, based entirely on automated decision-making that generates legal consequences applicable to, or similarly affecting, them. (Kuner et al., 2017)

2. Literature Review

The GDPR aims to consolidate data protection law throughout all European Member States, although it legally requires a substantial right to clarification. Taking into account the expansion of computerized decision-making and automated handling of information to back human decision-making (Badgett and Crehan, 2017; Klierova and Kutik, 2017; Menendez, 2017; Smith and Kubala, 2018), this constitutes a pivotal inconsistency in transparency and responsibility. The GDPR offers robust, but possibly ineffectual, protection against automated decision-making. A substantial right of clarification to the grounds and contexts of particular automated judgments is not available. As a right, the data subject's concerns in not being exposed to automated decision-making are challenged (Balcerzak et al., 2018; Kliestik et al., 2018b; Nagel, 2016; Zurga, 2017), to the extent that relevant endeavors are necessitated from the individual to defend her interests. (Wachter, Mittelstadt, and Floridi, 2017)

3. Methodology

Using data from Ascend2, Liana Technologies, Marketo, Spiceworks, and Talend, I performed analyses and made estimates regarding the main benefits of marketing automation, the most valuable features of a marketing automation system, improvements expected in organizations following GDPR compliance, steps organizations plan to take to prepare for GDPR, the most effective tactics used to optimize marketing automation, and types of marketing technology used by companies.

4. Results and Discussion

Personal data employed for automated judgments, comprising profiling, should be gathered only for definite, unambiguous, and justifiable intentions, and ensuing processing that is conflicting with such aims is not allowed. It may not be realistic for an individual to handle a purposeful assessment of an operation that may have required third-party information and algorithms, prelearned patterns, or intrinsically nontransparent machine learning techniques. As regards impartiality, predisposition may be included into machine learning operations at diverse phases, encompassing algorithm design and choice of training information (Gutu, 2018; Lazaroiu and Rommer, 2017; Popescu and Creager, 2017), which may entrench current biases into computerized decision-making processes. Determining and double-checking such prejudices is an essential demanding in drawing up and appraising the objectivity of machine learning operations. The latter may include heterogeneous data sources, dynamic advancement, and components that are incomprehensible, whether for technological or commercial grounds. (Kuner et al., 2017) (Figures 1-6)
Figure 1 The main benefits of marketing automation

Improved targeting of messages                   71.4%
Improved customer experience                     47.6%
Better quality of leads                          39.4%
Getting more leads                               36.8%
Improved efficiency/ROI in marketing             34.7%
Higher conversion                                32.8%
Closer cooperation between marketing and sales   31.8%
Cost efficiency                                  29.9%
Ability to combine data from different channels  28.8%
Increased web traffic                            25.6%
Better profit                                    15.7%
Shortened sales cycles                            6.2%

Sources: Liana Technologies. My survey among 2,600 companies conducted
November 2017.

Note: Table made from bar graph.

Figure 2 The most valuable features of a marketing automation system

Lead nurturing             61%
Analytics and reporting    55%
List segmentation          43%
Integration capabilities   37%
Email marketing            37%
Campaign management        34%
Lead scoring               28%
Landing page creation      16%

Sources: Ascend2; Marketo. My survey among 2,600 companies conducted
November 2017.

Note: Table made from bar graph.

Figure 3 Improvements expected in organizations following GDPR
compliance

                                         Strongly agree  Somewhat agree

GDPR will definitely accelerate               38%             45%
the cleaning of our data and lead to
higher data quality
GDPR will lead to better decisions in         35%             50%
business units and controlling
because of more reliable data
GDPR makes our organisation's                 32%             49%
data rapidly actionable and valuable
GDPR will provide better customer             30%             43%
knowledge and marketing
GDPR will accelerate the development          30%             35%
of new products and services
GDPR will have no impact                       2%             28%

Sources: Talend. My survey among 2,600 companies conducted November
2017.

Note: Table made from bar graph.

Figure 4 Steps organizations plan to take to prepare for GDPR

                                               US      UK      EU

Document processes to prove compliance         58%     64%     67%
Train employees                                48%     64%     59%
Conduct data audit                             48%     67%     43%
Change data management policies                43%     48%     34%
Work with third-party consultants              28%     35%     47%
Ensure third-party vendors are GDPR-compliant  27%     38%     43%
Alocate IT budget                              24%     23%     26%
Hire more IT staff                             14%      3%      0%
Implement new hardware/software                 9%     25%     17%
Move data to the cloud                          7%     10%     15%
Reallocate IT staff resources                   6%     13%     18%
Move data on-premises                           3%      6%     11%


Sources: Spiceworks. My survey among 2,600 companies conducted November
2017.

Note: Table made from bar graph.

Figure 5 The most effective tactics used to optimize marketing
automation

Customer experience mapping      57%
Personalized/dynamic content     54%
Prospect/customer re-engagement  44%
Landing page and form CTA        38%
Al and predictive modeling       37%
A/B or multivariate testing      34%
Auto-responder and drip          34%

Sources: Ascend2. My survey among 2,600 companies conducted November
2017.

Note: Table made from bar graph.

Figure 6 Types of marketing technology used by companies

Email Marketing         86%
Social Media Marketing  70%
Marketing Analytics     64%
CRM/Sales Automation    57%
CMS/Content Management  55%
Search Marketing        53%
Marketing Automation    46%
Data Management         39%
Testing & Optimization  35%

Sources: Ascend2 and research partners. My survey among 2,600 companies
conducted November 2017.

Note: Table made from bar graph.


5. Conclusions

Individuals should be informed relevantly about the soundness presupposed in the algorithmic decision-making, both as for system performance and for particular judgments affecting citizens. Legislators are attempting to confine the employment of automated decision-making, disallowing it when it is entirely machine-controlled, considerably having an impact upon people and neither relying on an agreement nor on persons' approval. Trade secrets protection may restrict the right of admittance of data subjects, but there is a wide-ranging legal advantage for data protection rights that should decrease the effect of trade secrets protection. The GDPR brings about a "legibility-by-design" system, ensuring the self-governing capability of persons to comprehend the performance and the influence of algorithms having implications for them. Automated decision-making processing may generate products displaying an inaccurate, fragmentary, or deceiving reality. (Malgieri and Comande, 2017) Uncovering of the entire code of algorithms and precise technical specifications of machine learning operations are of no assistance. A sophisticated but self-explanatory account of the decision-making process may be worthwhile. (Kuner et al., 2017)

Acknowledgments

This paper was supported by Grant GE-1324754 from the Social Science Research Unit at CLI, Washington, DC.

Author Contributions

The author confirms being the sole contributor of this work and approved it for publication.

Conflict of Interest Statement

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

REFERENCES

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doi:10.22381/AM1720185

DOINA POPESCU LJUNGHOLM

dopopescu@yahoo.com

University of Pitesti

How to cite: Popescu Ljungholm, Doina (2018). "Regulation of Automated Individual Decision-Making and Artificially Intelligent Algorithmic Systems: Is the GDPR a Powerful Enough Mechanism to Protect Data Subjects?," Analysis and Metaphysics 17: 116-121.

Received 14 February 2018 * Received in revised form 18 March 2018

Accepted 22 March 2018 * Available online 12 April 2018
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Title Annotation:general data protection regulation
Author:Ljungholm, Doina Popescu
Publication:Analysis and Metaphysics
Article Type:Report
Geographic Code:4E
Date:Jan 1, 2018
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