5.2 Ethical issues in data analytics

Major ethical issues in big data analytics

Libby Bishop (2017) identifies the principal issues as follows:

1. Privacy which can be protected by limiting the data collected. Also, altering data in a manner that makes it less revealing as well as regulating access to data are proposed to ensure the privacy of data.
2. Informed consent whereby users are made fully aware of the purpose of the intended present as well as future uses of data. This may save embarrassment for organizations as well as customers who for instance, unsuspectingly fill out customer data only to find themselves the helpless targets of some marketing campaigns of which they did not want to be (Read Target example where the discovery of teen girl’s pregnancy was known to the retail group even before her family did).
3. An absence of any de-identification or anonymization terms that will help masking or removal of any elements that might identify a person.

Five principles mentioned in Unified Ethical Frame for Big Data Analytics (Abrams, 2015)

Beneficial – Does our use of data benefit consumers as much as it benefits us?

Progressive – Do we have a culture of continuous improvement and data minimization?

Sustainable – Are the insights we identify with data sustainable over time?

Respectful – Have we been transparent and inclusive?

Fair – Have we thought through the potential impacts of our data use on all interested parties?

Data ethics describes a behavior code, often focused on what is wrong and what is right. This encompasses the following:

Data management – This includes recording, generation, curation, dissemination, processing, use, and sharing.

Algorithms – This includes machine learning al, robots, and artificial agents.

Corresponding practices – It includes programming, responsible innovation, professional codes, and hacking.

Ethical principles for using data

1. Privacy customer identity and data should remain private – The term privacy does not mean confidentiality, as private information might need for auditing based on the requirements in the legal procedure. However, this private data was acquired from an individual with full consent. It is also noted that the data should not be revealed for the utilization of other individuals or companies, allowing them to track their identity.
2. Shared private information should always remain private – In most cases, third-party companies share sensitive data. The typical examples of these are locational, financial, and medical data. Also, they need to have limitations on how the data can be shared for privacy and legal concern.
3. Customers should exercise a transparent view of how the data is being sold or utilized. They also need to have the ability to handle the flow of their private data across third-party and massive analytical systems.
4. There should be no interference between big data and human will. This is one of the ethical principles for using data as big data analytics can determine and even moderate who we are before making up our minds. Organizations need to start to ponder about the types of inferences and predictions that should be and not allowed.
5. Big data should not institutionalize prejudicial biases – The typical examples of these are sexism and racism. Algorithms of machine learning can grip unconscious biases in people and empower them through countless training samples.

Ethical principles for using data provide a high-level and wide context for resolving ethical predicaments, namely:

1. Secure vulnerable humans who could be impaired by the activities in their professions.
2. Enhance and protect the trust and reputation for the profession.
3. Give a basis for public evaluation and expectations of the profession.
4. Make the profession as a diverse moral public worthy of self-sufficiency from external regulation and control.
5. Serve as a guide for adjudicating disputes among organizations, both non-members and members.
6. Make institutions buoyant in the face of external burdens.
7. Answer the past destructions made by the profession.

Importance of practicing ethical data principles and management

1. Consumer Relations – Having excellent data ethics is a brilliant business decision. If you respect others’ personal information and careful with data, they will appreciate you. This will definitely build loyalty. On the contrary, unethical data management can destroy your organization’s public image.
2. Legality – Data management becomes a legal concern in some aspects as well. Thus, you need to comply with the given regulations.
3. Implementing Data Ethics – If you opt to stay in business for a couple of years, it is a must to manage your data ethically. The manner of protecting your people’s data greatly depends on your company’s needs. Whatever you gather, you should always be transparent.