Ethics in Data Science, Privacy and Usage of Data in Data Science

Data Science Crucials: Ethics, Privacy, and Data Usage

By Manas Kochar Category Data Science Reading time 7.5 mins Published on Jul 10, 2023

Nowadays, data is used to make most of the business decisions in a company. This data primarily holds personal information that needs to be safeguarded. Hence, businesses utilizing user data must be careful and follow some ethical standards to maintain privacy concerns. Still, ethics in data science is a concern for many.

Everyone working with data has to stick to ethical standards. You can enroll in a data science online course to learn more about these standards and the proper way to use data.

Ethics in data science

Data-driven enterprises confront numerous ethical dilemmas. The gathered, evaluated, and used data may be prone to hacking. Unknown parties can utilize the data to target individuals, which is a breach of privacy. To counteract these issues in data, scientists found a new field involving the study of ethics in data science.

Data ethics relates to the moral duties of collecting, protecting, and adopting private data. It also cares about how the data impacts society. Data ethics demands, 'What is the correct approach?' and 'How to improve?'

The primary impact of ethics is on the fields of data analytics, IT, and data science. Everyone involved in data science online training Should know and understand the basics first.

The key usage of data ethics is creating and extending the limits of information and computer ethics. It is shifting the industry towards a data-driven approach. Data privacy became a concern when businesses started using personal user data for non-ethical purposes.

Why are ethics important while learning data science?

As data impacts a business in many ways, it is significant to consider its risks. There are many examples where data used without ethical concerns resulted in a backlash for the company. For example, OpenAI ChatGPT ethically faced problems when its chatbot started showing bias and prejudice in its answers, which many noticed.

Algorithms can modify various tasks, making them easier to implement. Businesses already use them to automate small tasks previously done by an individual. Through algorithms, businesses can save on costs and boost speed, accuracy, and scalability. Algorithms have the power to build a consistent system that is less likely to include bias.

Despite all this, many data concerns regarding using personal information among users exist. People demand companies stay transparent about user data, as this data can be used to target them personally. It also leads to data security concerns due to such behavioral aspects.

Because of the recent issues with data usage, there is a major push towards getting data science online training. There is a need to set proper rules that regulate how businesses can use data for advertising without any negative outcomes. Many companies also agree to this fair use of data.

Ethical Practices in data science

Companies must develop various ethical standards to follow if they want to use data properly. With advanced technology, it becomes easier to access data. They should address ethical issues in both formal and informal areas. People are now more inclined to leave the company that shows unethical usage of their data.

  • Ownership

It is unethical for a business to gain a user's data without their consent. It states that the user has full ownership over their data. If a user feels their data is being taken without permission, they have the full right to pull away whenever they want.

A data science online course can help you understand these legalities properly. Some ways are through terms and conditions agreement, written approval with the user's signature, or asking for permission to use cookies that track the user's online behavior. It is a safe practice for a company to always ask for consent before collecting user data.

  • Transparency

Businesses should also maintain transparency while collecting data. Users should have knowledge of how, when,and where their personal information is being utilized. Companies should tell the users how they intend to gather, keep and use their data. Users must also have control over data movement across different platforms for analysis.

For example, a company uses an algorithm that tailors the user experience on its website. This algorithm uses personal data like users' online behavior and purchasing habits. They must inform the users of this change using a policy that details how their data is collected, stored, and used to personalize their website experience.

They should not lie or withhold information about the company's plans or methods. This deception will result in immoral and unsuitable utilization of standardized user data.

  • Privacy

This is the most vital data ethics concern that many users worry about. Data privacy states that a company should ensure they handle user data with their privacy maintained. A customer gives their consent to gather, store and use their data to a company. But they don't want their data to be accessible to the public.

The user data is also known as Personally Identifiable Information (PII). PII refers to any data that may be utilized for tracking the person. PII contains some of the following data:

  • Full name
  • Birthdate
  • Bank account information
  • Address
  • Credit card data
  • Mobile number
  • Social security
  • Passport number

There are many online courses for data science where you can get a detailed view of the PIIs and how to use them.

To maintain privacy, businesses should store data in a secure database. They must use methods to keep it out of the wrong hands. Companies may use methods like data encryption and securing passwords through dual authentication.

However, companies may still make mistakes in protecting data. They may have some slip-ups as they handle and analyze personal data daily. They can prevent these by the disidentifying information set.

Disidentifying can be done by removing each PII unit. This results in only anonymous data remaining. The analysts can find links among variables they want without tying certain data points to particular individuals.

  • Intention

Intentions are a must for any ethical practice. A company should not intend to hurt any individual, profit from them, or have any wrong intention. Before gathering the data, companies should consider if the data points are applicable to the problem they wish to solve.

For example, asking about someone's gender is a sensitive topic these days. So while trying to collect this data, companies add gender as an optional entity to not hurt anyone's sentiments.

A data science online training can help you understand these intricacies in data gathering and storing.

Even if your intentions are fair in a case. For example, gathering data to understand the needs of underprivileged children. You still need to evaluate the intention behind each data collection to figure out the possible data ethical concerns.

  • Outcomes

Sometimes, the outcome of certain good intentions can also cause harm to others. This is known as disparate impact, which is considered an unlawful act. The outcome of an act may display incorrect or false information, which may negatively impact a group or individual.

If the algorithm is biased or based on false information, it can show the wrong data to the public. It may be unintentional, but the outcome is wrong and may cause harm. Therefore, this may result in creating real damage for any individual.

However, unless a data analysis is done, you cannot predict the impact it will have. But you may avoid possible negative effects by asking and working on the potential outcome.

The ethical use of Algorithms for Fairness

Those who write, train or manage machine learning algorithms should take care of the algorithms that violate any ethical practices in data science. You may learn data science online to understand the many benefits of ML algorithms.

The algorithms are human-generated and may contain biased data sets. It isn’t a skillful practice for an algorithm to show biased data as it may lead to harmful outcomes.

There are some ways your algorithm can create bias. You should consider these factors to prevent bias in your algorithms.

  • Training

The ML algorithms train on a given data set. If the data set has biased information, then training the algorithm on that data set can cause it to become biased. It may lead to some adverse outcomes for the users.

  • Code

The algorithm will follow whatever the code provides. Even if the training data is biased or not, the algorithm will display biased outcomes if the code is written with wrong intentions. Make sure the codes written in the programming languages follow ethical practices properly.

  • Feedback

Many algorithms learn from users' feedback on a site or program to improve their output. The algorithms may show biased results based on users' biased feedback.

For example, an algorithm is used in a job search platform to suggest roles to users. If the program sees a hike in 'male' users getting more calls from hiring managers, the algorithm will try to learn and improve the job listings. This may result in 'male' users getting more offers than 'female' users. The algorithm learns that the 'male' element gets more engagement and will change itself accordingly.


Following ethics is the main issue in data science these days. Companies using data for their benefit must stick to these ethical standards to prevent unfair or biased outcomes.

Even for aspirants in data science online training, it is significant to understand the ethical usage of data. Proper ethics will guide the future of data science and its applications in many industries. Therefore, learners should start practicing ethics in data science.

Students can enroll in any data science online course for educational activity and ethical methodologies. Online courses for data science provide live training, career counseling, and interview aid.

The Advanced Data Science and AI Program provides expert mentors in their fields for students to learn data science online. Students also get to practice under their guidance, so mentors can help them properly implement learned concepts. They earn IBM and Microsoft data science certifications after the course completion, allowing them to be placed globally around top MNCs.

Frequently asked questions

1. Which is the top online learning platform for data science?

Ans. Many platforms are available for learning data science, but the ones providing placement help and interview aid are the most helpful. For example, the advanced data science and AI course is an amazing data science online course that offers the best online help to students.

2. How much does a data science online course cost?

Ans. The data science course fees can vary between Rs.50,000 and Rs.1,50,000 on the basis of the type of course material. Some courses also provide EMI options for students who need them.

3. Is data science a six months course?

Ans. A data science course duration can vary for freshers and professionals. Normally, six months is enough for beginners, but opting for proper data science online training that provides a complete syllabus is a preferable option.