We live in digital age where computer technology has enabled people globally to access and share data online 24 hours a day, 7 days a week, 365 days in a year.
As digital communications continue to increase, people continue to share more and more data, including personal information.
The above infographic from visual capitalist website , highlights key daily statistics broken down as follows:
- 294 billion emails are sent
- 500 million tweets are sent
- 4 petabytes of data are created on Facebook
- 4 terabytes of data are created from each connected car
- 65 billion messages are sent on WhatsApp
- 5 billion searches are made .
The large amount of data shared “big data” ,is now being used by multi-national and internet corporations, to analyse for patterns, trends and associations from users who embed different digital social platforms.
Key concerns emerging from datafication is issue of personal privacy and commercialization of personal data by companies who push products/services to people through targeted advertisements from data collected and analysed to reveal certain traits/associations. Some of real life personal data mismanagement and exploitations are highlighted below.
The above examples highlight the need to incorporate Data Ethics by organizations involved in capturing personal data.
Need for Data Ethics
Tadeo and Floridi (2006) , define data ethics as the branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing and use), algorithms (including artificial intelligence, artificial agents, machine learning and robots) and corresponding practices (including responsible innovation, programming, hacking and professional codes), in order to formulate and support morally good solutions (e.g .. right conducts or right values).http://dx.doi.org/10.1098/rsta.2016.0360
Data ethics builds on the foundation provided by computer and information ethics but, at the same time, it refines the approach endorsed , by shifting the level of abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even data that never translate directly into information but can be used to support actions or generate behaviours. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations — the interactions among hardware, software and data — rather than on the variety of digital technologies that enable them (Richterich 2018)https://doi.org/10.16997/book14.
Data ethics addresses concerns relating to privacy, algorithms, transparency, protection, autonomy, or self-responsibility, when it comes to handling and processing of data. These are highlighted below .https://www.uwestminsterpress.co.uk/site/books/10.16997/book14/read/?loc=008.xhtml
Privacy — denotes individuals’ possibilities for defining and limiting access to personal information generated based on individuals’ digital traces. Incorporation of laws such as European Union’s General Data Protection Regulation (GDPR) and Australia Privacy Laws enhance data privacy.
Transparency — indicates a high degree of information disclosure. It implies openness regarding features and processes: for instance academic, governmental, corporate, or even private practices. The notion is commonly linked to accountability which ensures openness and transparency of data collection — be it for research, commercial purposes, or governmental statistics.
Security — entails respect for individuals and their rights regarding privacy and the use of information about them. Investing and implementing cybersecurity in organization ensures unwarranted proliferation of botnets which can be used for Denial-of-Service attacks, spamming, harvesting email addresses, spreading malware, and keylogging .https://link-springer-com.ezproxy.lib.uts.edu.au/chapter/10.1007%2F978-3-319-28422-4_7
Autonomy or self-responsibility — addresses the pressing questions concerning the responsibilities and liabilities of people and organizations in charge of data processes, strategies and policies, with the goal to define an ethical framework to shape professional codes about responsible innovation, development and usage. In this case, some crucial challenges which may include moral responsibility and accountability of both designers and data officers with respect to unforeseen and undesired consequences as well as missed opportunities are addressed.
Algorithms -addresses issues posed by the increasing complexity and autonomy of algorithms broadly understood (e.g. including artificial intelligence and artificial agents such as Internet bots), especially in the case of machine learning applications. There is a growing reliance on algorithms (machine learning, artificial intelligence and robotics) in data processing, which pose pressing issues of fairness, responsibility and respect of human rights, among others.
Data Ethics Principles
Having established need for data ethics as a critical component in ensuring secure handling of personal data , I would advocate for the establishment of data ethics code of conduct based on;
- The ethics of collecting data.
- The ethics of processing and analysing data.
- The ethics of data-driven practices where data becomes a resource in everyday life.
The below principles adapted from accenture https://www.accenture.com/_acnmedia/PDF-24/Accenture-Universal-Principles-Data-Ethics.pdf , would form a good starting point in developing data ethics code of conduct for organizations involved in data handling. These are as follows;
- The highest priority is to respect the persons behind the data. When insights derived from data could impact the human condition, the potential harm to individuals and communities should be the paramount consideration. Big data can produce compelling insights about populations, but those same insights can be used to unfairly limit an individual’s possibilities.
- Attend to the downstream uses of datasets. Data professionals should strive to use data in ways that are consistent with the intentions and understanding of the disclosing party. Many regulations govern datasets on the basis of the status of the data, such as “public,” “private” or “proprietary.” However, what is done with datasets is ultimately more consequential to subjects/users than the type of data or the context in which it is collected. Correlative uses of repurposed data in research and industry represents both the greatest promise and the greatest risk posed by data analytics.
- Strive to match privacy and security safeguards with privacy and security expectations. Data subjects hold a range of expectations about the privacy and security of their data and those expectations are often context-dependent. Designers and data professionals should give due consideration to those expectations and align safeguards and expectations as much as possible.
- Provenance of the data and analytical tools shapes the consequences of their use. There is no such thing as raw data — all datasets and accompanying analytic tools carry a history of human decision-making. As much as possible, that history should be auditable, including mechanisms for tracking the context of collection, methods of consent, the chain of responsibility, and assessments of quality and accuracy of the data.
5. Data can be a tool of inclusion and exclusion. While everyone deserves the social and economic benefits of data, not everyone is equally impacted by the processes of data collection, correlation, and prediction. Data professionals should strive to mitigate the disparate impacts of their products and listen to the concerns of affected communities.
6. As much as possible, explain methods for analysis and marketing to data disclosers. Maximizing transparency at the point of data collection can minimize more significant risks as data travels through the data supply chain.
7. Data scientists and practitioners should accurately represent their qualifications, limits to their expertise, adhere to professional standards, and strive for peer accountability. The long-term success of the field depends on public and client trust. Data professionals should develop practices for holding themselves and peers accountable to shared standards.
8. Aspire to design practices that incorporate transparency, configurability, accountability, and auditability. Not all ethical dilemmas have design solutions, but being aware of design practices can break down many of the practical barriers that stand in the way of shared, robust ethical standards. Data ethics is an engineering challenge worthy of the best minds in the field.
9. Products and research practices should be subject to internal, and potentially external ethical review. Organizations should prioritize establishing consistent, efficient, and actionable ethics review practices for new products, services, and research programs. Internal peer-review practices can mitigate risk, and an external review board can contribute significantly to public trust.
10. Governance practices should be robust, known to all team members and reviewed regularly. Data ethics poses organizational challenges that cannot be resolved by familiar compliance regimes alone. Because the regulatory, social, and engineering terrains are so unsettled, organizations engaged in data analytics require collaborative, routine and transparent practices for ethical governance.
In conclusion , multi national organizations and technology based organizations that run social media sites hold publicly/privately shared personal information with limited supervision on their operations , which is troubling. It has not be uncommon to read of personal data being sold to third party entities for commercial or political exploitation .The time has come to develop guidelines and regulations that protect users while still allowing these companies to operate in responsible ways
References
Richterich, A. (2018). The Big Data Agenda : Data Ethics and Critical Data Studies. In The Big Data Agenda : Data Ethics and Critical Data Studies (Vol. 6). University of Westminster Press. https://doi.org/10.16997/book14
Floridi L, Taddeo M. (2016) What is data ethics?: Phil. Trans. R. Soc. A 374: 20160360. http://dx.doi.org/10.1098/rsta.2016.0360
Desjardins, J (2019, April 15th) How much data is generated each day?https://www.visualcapitalist.com/how-much-data-is-generated-each-day/
Zwitter, A. (2014). Big Data ethics. Big Data & Society. https://doi.org/10.1177/2053951714559253
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