İŞLETME ETİĞİ

Brey (2012) offers a three-level approach (technology, artifact and applications) to look at technology that can be useful when analyzing ethical issues

At the artifact level, products, systems, and procedures are developed.

Data analysis techniques such as data mining can be used on large pools of customer data to find patterns that can guide decision making in firms and predict future behavior. One of the data mining technologies, called non-obvious relationship awareness (NORA), has the potential to be unethically used.

Genetic engineering involving the applications to human beings, such as stem cell research and cloning, have raised enormous ethical challenges for the biotech industry.

Brain interventions, brain imaging, memory dampening and stem cell therapy are examples of neuroethics topics.

Table 6.3 ethical issues in AI

Ethical use of biometric technology must also be addressed by organizations especially those with more sensitive personal data such as those involving health and medical data.

Building trust is essential to e-commerce firms and is achieved through having a good ethical reputation and being transparent in their operations.

Improper practices such as collection of unnecessary data, data transfer over insecure channels and failing to remove personal data upon request or after relationship termination.

A deepfake uses AI and deep learning to manipulate or generate visual and audio content in which a person’s existing image or video is replaced with someone else’s likeness

Immersive technology

Internet of Things

Machine learning

Digital divide

Accountability holds all parties, who profit from decisions to use/share data, accountable for their acts and consequences

Cookies

Affordability of data such that all the interested parties may pay for it

Trade secret

System security

Deepfake