Many organizations use data mining and web scraping techniques to gather data for research or advertising purposes. Sometimes, they need to understand how their brand and/or products are performing in the market. If they are on the declining trend or not receiving the expected response in the market, businesses rethink their marketing or development strategies based on the analysis that is conducted on mined or scraped data.
Businesses are legally required to ask the user's permission before using their data for any type of research. And as site owners and bloggers, this is something we see on a daily basis as we log in and navigate through different sites and services online.
However, there are certain organizations that illegally scrape user data from websites without their permission. Therefore, users are advised to use a Facebook proxy even when surfing Facebook, this masks their identity by directing their traffic through secure servers with the help of Smartproxy.
Businesses are often faced with the dilemma of whether to employ ethical practices or not. Clearly, they have to remain ethical for their own benefit as it saves them all the hassle of going through the legal proceedings in case they are caught.
Data ethics concern numerous analysts and IT professionals and worry about how their organization collects, stores, and lets them use the data. Even when they might not be responsible for deploying the web scraper that extracts and builds a database, they are responsible for asking their organization to follow ethical procedures to gain access to data.
Every individual has ownership of their personal information. It is unlawful and unethical to obtain someone's personal data without consent. Therefore, organizations should insert written agreements and digital privacy policies. These present the users with the organization's terms and conditions which they are required to sign. And through the use of business intelligence tools, such tasks can either be improved or fully automated in nearly all niche markets and industries.
Users have a right to know how the organization is planning to collect, store and utilize their data. Therefore, these organizations should make their process transparent. For example, if your organization made a decision to deploy an algorithm that personalizes the website experience by analyzing and working on the user's behavior on the site.
For this, a clear comprehensible policy should be written that explains that cookies will be used to track users' behavior. All collected data will be added to a secure database and will only be used to train an algorithm that will provide users with a personalized website experience.
If users want to provide false cookies to websites and social media platforms, they should consider using Facebook proxies.
Ensuring the user's privacy is another ethical responsibility that comes while handling data. Regardless of their consent of letting the business collect, store, and analyze any Personally Identifiable Information (PII), it is still the organization's responsibility to keep it safe and not make it publicly available.
Some examples of PII are:
For privacy protection, organizations need to ensure that they are storing the collected data in a secure database to save it from falling into the wrong hands. Employing the use of specific data security methods also help protect privacy as they include features such as dual-authentication and file encryption.
The intention of the organization matters whenever a discussion on any branch of ethics happens. Before organizations start collecting data, they should know exactly why they need it, what they will be able to gain from it, and what changes will occur after analysis.
Even if the organization's intentions are pure and they will generate a positive outcome from the analysis, then they should use the data.
Even with the right intentions, certain data analysis outcomes exert unintentional harm to the users who have contributed to the database. This effect is known as a disparate impact, which is ruled as unlawful. This harm can happen due to bugs in the algorithm or due to inputting wrong variables into the algorithm itself. Either way, there is a possibility of spreading false information generated through the algorithm itself. Therefore, special care should be taken to ensure that there is no possibility that disparate impact could occur.
Since algorithms are written by humans, there is a possibility that a bias can be left in the code intentionally or unintentionally. To use the algorithm ethically, it should follow the data science principles, which are:
Data is used to train machine-learning algorithms, therefore an unrepresented dataset can cause the algorithm to prefer certain variables over others.
There is a possibility that the back-end code of the algorithm itself is written with an unintentional bias.
There is a chance the algorithm learns from biased feedback provided by the users. If an attribute is added a number of times, the algorithm will automatically start preferring the specified attribute over others.
Businesses should consider ethical practices while doing data mining or web scraping as sometimes, they are able to unearth sensitive data that can potentially harm the users who contribute to their research and analysis. However, to protect themselves, users should mask their digital footprint by using proxies.