Data is the new currency of the digital age, fueling innovation, driving decision-making, and shaping the very fabric of our interconnected world. As organizations harness the power of data science, a critical question looms: how do you ensure ethical data handling?
As a data scientist, there are strict ethical codes of conduct you are required to follow and adhere to. Below, we examine the five ethical codes of conduct you should consider as a data scientist.
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Upholding and respecting privacy starts with understanding the difference between data protection vs data security. As a data scientist, your key concern should be keeping your clients’ data private and safe from third-party access.
When companies trust you with their critical data, like their contact information, protecting them from identity theft and fraud risks is essential.
Protecting and securing their data through tried methods like data classification, access control, and data encryption could go a long way in upholding their privacy.
Maintain End-to-End Accuracy and Accountability
As a data scientist, you’re expected to uphold accuracy and accountability when acquiring and relaying data on both ends of the data chain. This means employing effective data encryption practices like Twofish, Triple DES (3DES), and Advanced Encryption Standard (AES).
Adhering to an end-to-end accuracy and accountability principle helps you maintain accurate findings and a seamless decision-making process. You’ll also effortlessly achieve your data management goals, gaining helpful insight from the data you collect.
Design and Use Algorithms Responsibly
If you’re designing your data research algorithms, you must ensure they are not trained on existing prejudices, which may lead to unpredictable outcomes. This is important because algorithms aren’t just a set of commands you input into your computer for data analysis. They are a powerful influence on people’s lives and behavioral patterns.
As an ethical data scientist, you must design, test, and deploy algorithms that are void of harm to a certain group of people. If you identify potential bias sources, it’s important to implement a mechanism that will swiftly correct the errors where necessary.
Eliminate Discrimination and Bias
The data science industry is never short of instances of biases and preferences, some of which arise from popular myths in data collection trends. One such myth that leads to accuracy bias is that larger sets of data (big data) are more accurate than smaller ones.
While this might be true, big data may sometimes contain bogus or unnecessary statistics. In such cases, instead of focusing on the data size, it’s best to focus on data accuracy and cleanliness.
When collecting data from individuals of different origins, you should prioritize equal and fair treatment. Additionally, ensure you use stratified sampling to clean and filter data before use. This way, you can eliminate instances of bias and present reliable and useful data.
Maintain High Respect for Intellectual Property Rights
Data scientists respect intellectual property rights by ensuring accurate and proper work attribution. You should also avoid plagiarism as this beats the purpose of your research work and diminishes trust.
To succeed in a career in data science, you must always achieve high data ethics standards. Any slight slip could jeopardize your company’s operations and put your licenses at risk. So, whether you’re starting a new data collection company or looking to join an established one, these five codes of ethics should guide you.