Data ethics are guidelines for how an organization handles data. Due to recent privacy scandals (think Facebook and Cambridge Analytica), users want to know how their data is collected, processed, stored and distributed.
But that’s not all.
According to research, 97% Percentage of people concerned that companies will misuse their data. In other words, if a company is serious about building trust with its customers, it must have a data ethics framework. Data ethics matter.
What is data ethics?
Data ethics can be better understood by breaking down the two concepts that make up the term data ethics. Data and Ethics. Data refers to quantities, symbols, characters, statistics, or facts on which operations such as storage, processing, or transmission are performed.
on the other hand, ethics It is a field of good and evil. It involves codifying, advocating, and advocating right and wrong in different areas of life.
Data ethics, therefore, is about codifying, advocating, or advocating what is right and wrong regarding the collection, processing, storage, and use of data.
In business, data ethics helps build trust with customers, comply with regulatory requirements, and ensure fair play.
Four principles of data ethics
The Data Ethics Principles are the foundation of the Data Ethics Framework. These principles are non-reducible minimums in developing a framework for data ethics.
Users own their data. Just as taking someone’s property without their consent is unethical, so is taking someone’s data. Before you can collect someone’s data, you must first obtain their consent. Not asking for consent is a violation of privacy.
As a business, you should develop an ethical framework that outlines the ethics of your practices regarding user data. A framework should cover all aspects.
Some of these frameworks require a signed written consent form or a website pop-up asking the user to agree to the website’s privacy terms.
A comprehensive ethical framework should lay down rules for how data is collected.
These frameworks can be guided by regulatory frameworks such as the General Data Protection Regulation. GDPRor comply with regulatory laws such as the CCPA (California Consumer Privacy Act).
Transparency creates trust and should be a top priority when developing an organization’s data ethics.
You must clearly communicate why you are collecting user data and how you intend to store, process, and distribute that data.
for example, artificial intelligence And machine learning is the latest frontier in digital technology. Training these systems requires enormous amounts of data. If you use customer data to train these systems, you should communicate that fact so that users can make informed decisions. Trying to trick them into opting in is unethical and illegal.
Let’s say you are using WordPress chat plugin This allows you to communicate with your website visitors. If a conversation is saved for later personalization, it must be specifically stated in your policy. Don’t store conversations that may contain sensitive customer data without user consent. Consent must be obtained even if it does not contain sensitive customer data.
Here’s another example: ChatGPT is an AI bot trained to interact with people like humans do. The current version is in beta and current user input is being used to further train the AI.
3. Ethical use of data algorithms
Using algorithms raises ethical issues. Algorithms are trained using data, so there may be bias in either the algorithm or the data. We recommend controlling as much bias as possible to ensure objective use of user data.
There are three potential biases in artificial intelligence and machine learning systems. these are:
Data bias: Algorithmic systems learn from input data. A good ethical decision is to make sure the data used is not biased. Involve diverse data-centric teams. This ensures that the data represents the natural world in which the system should operate.
Code bias: Don’t assume your code has no bias. Double check that you are controlling for expected bias.
Feedback data bias: These systems also learn from feedback data, so care must be taken to ensure that the feedback does not bias the algorithm toward a particular outcome.
Businesses should try as much as possible to remove bias from their systems. Any form of bias can undermine trust in your business. Moreover, bias is bad for business. Using such information to make business decisions will result in wrong or uninformed decisions.
Data should not be collected for the sake of collecting data. Data should be collected with a specific intent. Intentional data collection prevents unwanted collection of sensitive data.
Data certified as sensitive is data that identifies a user. Also this:
- passport number
- bank account number
- date of birth
- telephone number
- Credit card information
- social security card
- full name
The above data should only be collected when necessary to avoid unintended situations. The more personal data you collect from your users, the more data hackers can use illegally. This is data that can be used for things like identity theft.When identity stolen As a result of a data breach, companies can be held liable.
Also, after data is collected, it should be anonymized for use by analysts.
Intent must guide data collection, data storage, data processing, and data use. As mentioned earlier, companies should communicate their intent to their users as well.
Data is essential to business in today’s technology age. Business decisions are made based on data. It is through data that businesses gain a competitive edge.
But while data is a great business asset, it must be approached ethically. It must be collected and used responsibly so as not to invade an individual’s privacy. This requires companies to develop data ethics frameworks and policies that protect users from data misuse.
To establish a viable data ethics framework, there are certain principles that must be enforced. These include:
Owned by: You acknowledge that the data belongs to you and cannot be used without your consent.
Transparency: Clearly communicate to users why we collect data and how we process, store and use it.
Ethical use of data algorithms: Make sure the algorithms that process user data are unbiased.
intention: Be clear about why you are collecting data so that we only collect and use the data you need.
Ethically responsible companies do more than just comply with data privacy regulations. You should also be able to build trust with your users.
Disclaimer: The author takes full responsibility for the content of this article. The opinions expressed are their own and do not represent those of IEEE, the Computer Society or its leadership.
https://www.computer.org/publications/tech-news/trends/why-data-ethics-matters/ Data Ethics and Guidelines | IEEE Computer Society