The Ethics of Big Data

Introduction

It is virtually impossible to open the business section of any newspaper without encountering a reference to a ‘big data’ problem or speaking to an organizational leader or innovator who doesn’t see big data as a source of opportunity in the digital age. Big data includes referenced information such as articles, books, news reports, purchase orders, invoices, and videos, as well as unreferenced data such as connected digital devices like sensors, video cameras, and televisions. It is estimated that about 2.5 quintillion bytes of data are generated every day, and the data volume is doubling every two years. In this context, innovation (in both mainstream business and social sciences) is seen as a conscious effort to leverage insights from big data to create marketable products. And innovation, of course, depends on establishing trust through interacting successfully with people. Clearly, big data presents unparalleled opportunities for insights and innovation. However, taking advantage of these opportunities also raises ethical challenges.
Big data refers to collecting, processing, and analyzing large datasets to identify patterns, trends, and correlations that would otherwise be inaccessible. It can be defined as the three v’s – volume, velocity, variety, and veracity – of data and the creation of new analytical techniques and technologies to extract meaning out of these v’s.
It’s obvious that ethics is important for big data because big data’s use can affect an individual, society as a whole, and even democracy in the way decisions are made and in the way people are advertised to and experience things online. Big data, if not used properly, ethically, and in a transparent way, can easily be used to invade privacy, discriminate, manipulate opinions or trust, and so on.
Yet it’s undeniable that privacy threats cut to the core of the booming era of big data, where massive amounts of personal information are collected and used to make sense of the world and its challenges. While big data promises to revolutionize healthcare, transportation, finance, urban planning, and countless other areas of life, innovation cannot come at the expense of protecting privacy. The key is to figure out how to nurture innovation while still shielding individual rights to privacy, and that requires establishing an ethical framework, a regulatory regime, and some technological rules of the road to ensure respect for both.

In today's data-driven decision-making, big data could be revolutionary in some ways, providing many benefits in many domains. In this essay, I will discuss three ways big data comes up with positive results.
Here are three ways big data can lead to positive results.

Improving decision-making processes

One major advantage of big data is that it can give organizations unprecedented insights into customer behavior, market trends, and operational efficiencies. By analyzing vast amounts of data in real-time, businesses can make better decisions, identify emerging trends, and respond speedily to shifting market dynamics. From optimizing supply chain logistics and predicting customer preferences to reducing risks and improving decision-making processes, big data equips organizations with the tools that can help them gain a competitive advantage in today’s marketplace.

Enhancing business operations

Routine processes are streamlined, costs are reduced, and operational efficiency is optimized. Business operations are now notably impaired without big data analysis. By analyzing operational data, a business can find inefficiencies and reduce waste through the automation of routine steps, such as eliminating unnecessary paper-based reporting. If a retail operation is losing sales, the analysis of electronic point-of-sale data can reveal ways to optimize sales on the shop floor. Predictive maintenance in heavy industry settings can divert manpower away from defective system inspections and towards more efficient work related to the system’s primary function. In e-commerce, big data allows recommendation systems and targeted marketing practices to provide value to customers more effectively while maximizing operational performance.

Advancing scientific research and innovation

Big data is driving discovery, innovation, and collaboration in scientific research. Empowered by big data, researchers can aggregate and analyze enormous quantities of data from multiple sources and across multiple dimensions, thus acquiring deeper insights into complex phenomena, accelerating the pace of discovery, and developing solutions to grand societal challenges. For example, analyzing big data such as genomic data provides research evidence to accelerate the development of personalized medicine, while monitoring environmental data helps researchers identify environmental trends to cope with climate change.

Privacy concerns in Big Data

With the advancement of big data and its steadily growing influence on our personal and professional spheres, privacy concerns have shifted toward collecting, holding, and utilizing personal information. Here are the main privacy concerns of big data:

Data collection practices

Surveillance capitalism - In a time dominated by a data-driven society, surveillance capitalism has become a powerful and prevalent business model driven by the collection and commodification of user data. It’s driven by the ceaseless collection of user data and it is used to construct comprehensive profiles that are then circulated and made available to different corporations, media companies, consumer goods firms, government agencies, news outlets, and third-party advertisers who are willing to pay for those publics, mainly for the purpose of targeted advertising and recommendation of personalized services. But it also creates privacy concerns.
Data brokers - Aggregation and co-ordination of data in other hands as well. Data brokers, the shadowy organisms in the universe of digital money, remove data from its original source and sell access to it. Unbeknown to the person themselves, data brokers amalgamate access to data about individuals from every corner of the globe. This data comes not just from websites where we carry out transactions or post comments but also from social media and other public records. These companies purchase and sell access to stored information on huge numbers of people for the purposes of marketing, advertising, employment practices, and consumer credit. People do not even know how much data they hold on them or which data brokers own it.

Data breaches and security risks

More recently, data breaches and security incidents, which expose personal information to third-party access, theft, exploitation, and malicious use, have snowballed. From the data breach of a bank to the leak of social media information, from the theft of medical records to the breach of databases maintained by various entities, data breaches are now frequent and expose individuals and organizations to serious harm. Financial losses, reputational damage, and the erosion of trust in the digital infrastructure – the mentioned reasons explain why it’s so crucial to strengthen cybersecurity and protect personal information from nefarious actors.

Lack of transparency and consent

A basic problem with big data is that users often do not know what data about them is being collected by or shared with anyone else. Their privacy policies are often maddeningly difficult to understand, and the consent that should go along with data collection practices is often buried in a whirlwind of small print in a user’s agreement to terms of service, which few have the time or wherewithal to read closely.

Ethical issues in Big Data

While big data is hailed as a potential engine of innovation and growth, it likewise has important and novel ethics issues that demand attention in the days ahead. In particular, three big-data-related ethical issues stand out here for our consideration.

Discrimination and bias

Organizational ideologies, programming ‘bugs,’ and human biases can also infiltrate big data algorithms. In turn, this can exacerbate human-created systemic biases or introduce new ones, potentially perpetuating or widening gaps in social inequality and discrimination. Big data analytics could be used, for example, in discriminatory hiring by regulating certain traits, underpricing in financial services by relying on historical data that illustrates past prejudice, or profiling of categories such as race, gender, and socioeconomic status.

Manipulation and control

The data-mining outputs provided by big-data analytics could be used to manipulate individuals’ conduct, thoughts, and sentiments in various ways, giving rise to a multitude of ethical dilemmas concerning self-determination and personal freedom of choice. Advertisements tailored to capitalize on specific psychological susceptibilities, algorithmic curation of social media feeds that sow partisan divisions in deliberative politics, or personalized press platform suggestions that consolidate opinion echo chambers, using big-data analytics for manipulation and control contribute to undermining the exercise of individual agency and, in turn, democratic principles.

Privacy violations

Often at the root of big-data ethics, privacy violations cut to the core of many thorny moral questions by revealing the extent to which the unrelenting accumulation and analysis of data on individuals seriously undermines rights to privacy, autonomy, and self-determination. Issues of privacy become extra charge in the context of big data when one factor in the surveillance by government agencies, the collation of behavioral data by commercially run online platforms, and the subtle forms of profiling that make it possible to track people and map their activities and thereby subject them to external analysis and judgment.

Balancing privacy and innovation

Alongside the positive social benefits of improving big data’s trustworthiness, the growing ethical concerns mean that it’s more important than ever to strike a balance between protecting the privacy and advancing innovation – so this is how you can do it.

Regulatory frameworks and compliance

Faced with the growing problems of big data, governments around the world are developing regulatory regimes to protect individual rights of privacy through legislation and regulation. For example, the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States set new standards for collecting, processing, and storing personal data, including requirements of transparency concerning its uses, requirements of the consent of the affected individuals, and standards for notification concerning data breach.

Privacy by design principles

Privacy by design is a ‘proactive and preventive’ approach for system engineering that addresses privacy and data security and integrates data protection considerations into the design and development of business processes and IT systems. By doing so, organizations can be more proactive at mitigating privacy risks and gaining user trust from the inception of their products or services. Principles of privacy by design include: data minimization – only collect and store the personal information that is required, relevant, and proportionate to the identified purpose of your processing activity for the product, service or system; purpose limitation – use personal information only for the specific and clearly defined purpose for which you collected it; user-centric design – consider how your product, service or system will be used to engage with people and create tailored interactions that meet specific needs for particular individuals or groups; and end-to-end encryption.

Ethical data governance

What we mean by ethical data governance here is placing in place policies, procedures, and practices that guide data stewardship across the data lifecycle, including defining roles and responsibilities associated with the flow of data, establishing well-structured data governance organization and processes with regular audits to ensure continued financial and ethical compliance, and creating transparency and accountability mechanisms to allow stakeholders a view into the workings of collecting, processing and using data and to hold organizations accountable for their data practices.

Best practices for businesses and organizations

In navigating the complex landscape of big data ethics, businesses and organizations play a pivotal role in upholding privacy rights and fostering responsible data practices. Here are three key best practices for achieving this:

Transparent data practices

Trust can be fostered through greater transparency around what happens to big-data. Businesses and organisations should adopt more transparent data practices so that users understand better how their data are collected, processed and used. For example, they should provide clear, concise, and readily understandable privacy notices and explain what data are collected, for what purposes they are processed, and to whom they are shared.

User control and consent mechanisms

User autonomy and consent should be respected across all data uses. Users should have meaningful agency over their data. That means robust consent procedures for users via an accessible set of settings that allow users to opt out of, or control, these data practices. Not only should consent be explicitly given for the collection or processing of data (with a generally recognised opt-in or opt-out method), but users should also have a range of means for accessing, correcting or deleting their data.

Ethical data use policies

Organizations must embrace the need to set clear and enforceable ethical data use policies to guide real-world applications and data stewardship. For example, to support eventual autonomous ethical decision-making, these policies should document the organization’s normative ethical principles and values that govern data use (e.g., fair use, openness, responsibility, and respect for individual rights). They should also include specific procedures for making ethical decisions (e.g., simple behavioral checklists), and meaningful processes for data governance and risk control.

Summing up

Big data is both the greatest ethical challenge in history and the most important advance in the drive for innovation. As we move forward, business, non-profit, and government entities will be challenged to lead in ethical extensive data development. We can and will achieve this once it becomes a standard feature of big data production and once transparency and accountability, as well as the privacy rights of citizens, become intrinsic requirements of big data use. By following best practices, we will find a path forward with big-data analytics that can both protect the values we hold dear and promote the innovation that can transform society for the better. The future of big-data privacy and innovation is in our hands. Managing big data in ethical ways while advancing innovation will determine whether big data guarantees more significant benefits to society and strengthens democracy.

The ethics of Big Data: Balancing privacy and innovation