Page 197 - Data Science class 10
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• Bank account number
• Passport number
5.2.2. The Private Information That Is Shared Should Always Be Handled with
Confidentiality
Third party companies share sensitive data, either financial, location related or medical. They should always impose
limitations on when and how that information may be shared.
When managing data confidentiality, follow these guidelines:
• Encrypt sensitive files
• Manage data access
• Physically secure devices and paper documents
• Securely dispose of data, devices, and paper records
• Manage data acquisition
• Manage data utilisation
• Manage devices
5.2.3. Customers/Consumers/Members
Customers/Consumer/Member should always have a clear view of how their data is getting used or traded and
should have the authority to manage the flow of their confidential information across enormous, third party
systems. Customers should fully understand how their data is used before providing it, in an open and transparent
manner, if it is being sold for other purposes they initially intended, and they should be able to have control in the
flow of their private information.
The end goal of all customer data collection should be to provide an improved client experience that makes
customers want to share their data.
5.2.4. Data Should Never Interfere with Human Will
Participants are free to withdraw from any active data collection or intervention program at any point without
pressure or fear of retaliation. Everyone also agrees that information should never interfere with human will. This
data should never be used to determine who a person is before another person is able to make up their own
mind in regard to said person. Data analytics can average out and at times, even discover who we are even before
we make up our mind. Organisations should begin thinking about the different categories of predictions and
conclusions—those that are acceptable and those that are not.
5.2.5. Data Should be Unbiased
The possibility that an individual's data might lead to unfair bias is perhaps one of the greatest, most difficult, and
most divisive topics in data ethics. Try to remain neutral and unbiased. Don’t let your personal preconceptions
or opinions interfere with the data collection process. Sexism and racisms are the biggest concerns. This bias
can come from actual humans, but it can also come from machine learning and algorithms. They can learn to
form unintentional prejudices depending on particular factors. Data should never institutionalise unfair biases
like sexism or racism. Analytical systems can absorb unconscious biases in a crowd and encourage them using
training samples.
5.3. DISCARDING THE DATA
Once you are done with the user data, especially confidential data, it is important that you discard this data in
appropriate way to make sure that it is not accessed by any unauthorised person and it is not misused in anyway.
Ethics in Data Science 195

