What Is Data Ethics?

Updated: Oct 14


We're living in a world where digitalization continues to grow stronger by the minute. The dynamic use of technology is changing the way we think and perform our daily tasks. Therefore, it may not be surprising for many to realize that technology will dominate the future of businesses and households. As is becoming evident, artificial intelligence (AI) will too.


Digitalization is meant to improve interactions and general client experience. It generates a lot of new data as it fulfills its mission. Much of this new data tracks micro-behaviors along the customer journey. This information is precious to organizations. It also represents new concerns for them about how they manipulate, store, and use the data and for which purposes.


Businesses cannot afford a privacy breach or invasion, regardless of how well established the company may be. After all, one wrong move with their data could put the business in a vulnerable position. One incident can have significant ramifications. Cybercrime and privacy breaches are no stranger to anyone in this world. Hackers are on the rise, and businesses are at constant risk of sabotage or exploitation. Therefore, it is imperative to develop a more precise understanding of data ethics and how it may intercede with our work.


Read through the rest of this article to understand how Data Governance covering data ethics will help maintain your company's reputation intact as you try to leverage your data assets and create business value.


Why Care About Data Ethics


In simple terms, data ethics relates to the process of assessing all potential moral problems that can arise from having a particular piece of data. It has become a widely discussed topic and a new hot area of ethics.

Data goes through various lifecycle stages, sometimes multiple times: generation, sharing, processing, curation, use of data, and storage. There are ethical questions to ponder at every step. If you are in the USA, and a Chinese firm is the lowest bidder on a platform that offers segmentation services for your B2C client list, do you consider it? This may be an easy one, but what if the firm is USA-based? Sharing data with a third party requires more scrutiny than where that party operates; the criteria may or may not include legal ones, but will include ethical ones.


Artificial intelligence uses this data through algorithms, machine learning, robots, bots, and innovative technology. It transforms the data into something new, for instance, a prediction. Algorithms follow the data. An example speaks volumes here. A man goes online, makes a credit application, and gets instantly approved. His wife does the same a few minutes later; the same IP address, same assets, same revenue (they have a business together), and she gets declined. This is not a good look. There are ways to catch this beforehand called bias management. It's an easy one too now, but five years ago, when all this AI was just starting, it happened.


The problem arose when companies began collecting a lot more data from ordinary individuals because of digitization and, of course, wanted to use this data to serve them better while managing their risks.


Organizations need a plan.


What Are Ethical Principles For Data


There is a general framework when it comes to data ethics. It helps organizations with what they are allowed and not allowed to do with the data they receive from their customers, patients, shoppers, or consumers. Data ethics is about doing what is right, but guidelines can still be vague. Experts have come up with a few factors that are generally agreed upon, which should be considered and put into place when it comes to handling data.


Keeping data private


When a customer hands in their data, it is essential that the organization adheres to the terms agreed by keeping the person's identity hidden unless stated and agreed upon otherwise. As long as the data is collected with consent, it is acceptable. However, companies must never forward the data to other companies or expose it in a way where an individual's identity is attached to it. Therefore, data must also be kept hidden and confidential. Any privacy breaches that occur or intentional sharing of data without permission may lead to dire consequences against data ethics. GDPR and other data protection regulations are helping in deciding what should be viewed as acceptable or not. That is when the organization still has a choice.


Humans in the Loop


A company that handles data and takes it into account must always prioritize and seek the benefit of human interest over commercial and institutional interests. With personal data, we are dealing with people's property, not bits, and bytes. People come from all walks of life and differ significantly in their sense of understanding, sixth sense, empathy, sovereignty, and creativity; this makes them prevail in terms of status compared with machines. Humans should always be the primary concern.


Transparency


While it is binding on a company to ensure the data remains hidden, it is also necessary for the customer base to understand why their data needs to be collected and how it will be used or monetized. A business must clearly state and divulge these FAQs before collecting any personal data from people.

Customers must always have the upper hand in controlling how their private information is being used or given out. They must also be notified about any risks that could intercede with ethical, social, or societal values.


Interceding With Human Will


The data that a company intends to collect should never collide with the will of an individual. Based on the data received, it must not be used to influence who a person is before somebody else deciding and making up their minds in relevance to the concerned person. Therefore, a person giving their data and their self-determination must not only be prioritized in the process, but they must also be part of how the data is used.


Biasness


It is possibly one of the most controversial problems when it comes to data handling. It turns out to be quite a complicated one too. Several issues stem from data ethics and how a person's data may establish biases or unfairness. Racism and gender inequality are top concerns. Algorithms, machine learning, or even humans may be the cause of bias actions, as it may be a matter of a subconscious mishandling or oversight based on several factors.


Data Handling And Equality


The process of democratic data handling is a part of power relations in society. As an organization uses data, there must be proper consideration paid to people who are in a vulnerable position. For example, they may be subject to profiling, which could impact them negatively and expose them to stigmas or discrimination. For instance, a person undergoing a rough patch due to their health, financial, or social conditions might co-exist. Self-learning algorithms must be carefully evaluated and actively adjusted in a manner where a person's identity stays protected and confidential.


Accountability


This is a factor that often relates to a company's way of handling and processing data. It is the reflective and legitimate use of data and the business's measures to avoid privacy breaches and protect data information. Accountability remains a significant factor in the process of handling and using data. Organizations are consistently and actively working on reducing or mitigating any ethical risks or societal/social consequences.

Sustainable and proper handling of data involves using a forwarding date (if needed) through an appropriate and safe channel. It is often mandatory within the data collecting organization as it helps ensure a sense of responsibility in the short and long term of working. The accountability taken upon by the company must also apply similar rules to partners, clients, and subcontractors who are processing data.

These few principles are being emphasized over extensively at the moment, especially in privacy regulations. However, there is still a whole lot more to develop, learn, and discuss the foundation of data ethics before it is out in black and white.


Third parties and data handling


Third parties and mishandling of private data is a debatable topic. Let's say that a business deals with vulnerable parties, minorities, or children. In that case, it is not (data) ethical to permit the interference of third-party cookies on the same website where data is being collected or handled. Moreover, the website mustn't share sensitive data with the third parties involved.


With any health-related data, political subjectivity, or religious orientation-based data of as much, it is not considered ethical to let third-party cookies over the website. Similarly, the public sector must not use the citizens' private data or behavior by sharing it publicly or through third-party cookies.


Data sharing gets very tricky. We may see it as sneaky even if we have openly agreed to it and any of its terms through a pop-up. Though it is allowed to an extent, it is still perceived as uncomforting and disturbing for many as it may exceed limitations, which are again unethical.


Organizations must take precautions when involving third-party cookies on their sites or as their partners in handling data. They must be especially careful with handling data and information on such sensitive groups.


Conclusions


Any organization that handles personal data will require a data risk assessment. Data ethics will play an essential part in avoiding any consequences that could be troublesome for the individual who has faced a privacy breach via the company that collected his data. Privacy, ensuring complete transparency, fairness, and proper regulation of data is essential for companies who need to keep up with data ethics.


Several national and international governments have taken steps to articulate, publish, and enforce data ethics rules. Some examples include the Montreal convention held in November of 2017. There, over 400 participants from the areas of legal liability, moral psychology, and cybersecurity attended. The forum hosted several international influencers to conclude and involve general agreement while drafting more data ethics laws. Similarly, Singapore continues to make efforts even today as it first discussed artificial intelligence and the ethical use of data in 2018. The European Union, too, made some notable arguments in the General Data Protection Regulation (GDPR).


Hopefully, the number of privacy breaches will decrease in the coming years, and consumers can safely share data without worrying about any manipulation or coming across any consequences. Such kinds of conventions and establishments may be beneficial in restoring justice with data ethics.



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