Group 4:
Title: "Navigating the Ethical Implications of AI in Business Communication"
Slide 1: Title Slide
Good morning/afternoon, everyone. Thank you for joining today's presentation on navigating the
ethical implications of AI in business communication. Today, we'll explore the ethical
considerations related to the use of AI, particularly in the context of data privacy, bias,
transparency, accountability, and human-AI interaction. Let's delve into the fascinating and
important world of ethical AI in business communication.
Slide 2: Ethical Considerations in AI-driven Communication

When it comes to AI-driven communication, one of the key ethical considerations is data
privacy. Protecting user data is paramount in maintaining trust and ensuring compliance with
regulations such as GDPR and CCPA. A prime example of the consequences of data privacy
breaches can be seen in the Cambridge Analytica scandal.

Another ethical concern is bias in AI. Despite their intended neutrality, AI algorithms can
unintentionally perpetuate biases present in the data they are trained on. This can have
significant implications for decision-making and fairness in business communication. For
instance, the gender bias exhibited by Amazon's AI recruiting tool.
Slide 3: Transparency and Explainability of AI

Lack of transparency in AI systems poses a challenge. It is often difficult to understand how
AI arrives at its decisions. This lack of explainability can erode trust and hinder
accountability. A well-known example of the need for algorithmic transparency is the case of
Google's "Black Box" algorithm, where understanding the reasons behind its search results
became a crucial issue.

Accountability is another crucial aspect. In AI-driven business communication, it is essential
to determine responsibility for AI-driven decisions and actions. Establishing mechanisms for
redress and recourse is vital when AI systems make errors or exhibit biases. The case of
Uber's self-driving car accident brings into question who should be held accountable for such
incidents.
Slide 4: Ethical Frameworks and Case Studies

The IEEE Ethically Aligned Design (EAD) and the ACM Code of Ethics serve as valuable
frameworks to incorporate ethical considerations into AI design and implementation.

The EAD encourages practitioners to consider various ethical dimensions, such as privacy,
fairness, explainability, and social impact, during AI design and deployment.

The ACM Code of Ethics provides a framework for computing professionals to navigate
ethical dilemmas and make responsible decisions throughout the AI design and
implementation process.
1. Case Study: YouTube's Recommendation Algorithm and Content Polarization
YouTube's recommendation algorithm, while designed to provide personalized content
suggestions, has faced criticism for contributing to content polarization and the spread of
misinformation. The algorithm's tendency to prioritize engagement over accuracy has led
to the promotion of sensational or extremist content. This case study highlights the ethical
challenges associated with algorithmic content curation and the need to balance
personalization with responsible information dissemination.
2. Case Study: Facial Recognition Technology in Retail Surveillance
Retailers have been adopting facial recognition technology for various purposes, such as
security and personalized marketing. However, concerns regarding privacy, consent, and
potential misuse of this technology have arisen. The case study examines the ethical
implications of facial recognition in retail settings, including the potential for
unauthorized surveillance, discrimination, and the need for transparent and accountable
use.
3. Case Study: Chatbot Customer Service and the Impact on Human Interaction:
Many companies have integrated chatbots into their customer service operations to
provide quick responses and support. However, there are concerns about the potential
loss of human interaction and empathy in customer interactions. This case study explores
the ethical considerations surrounding chatbot-based customer service, including the need
for human oversight, maintaining customer satisfaction, and striking the right balance
between automation and personalized support.
4. Case Study: AI Voice Assistants and Privacy Concerns
AI voice assistants like Amazon's Alexa, Apple's Siri, and Google Assistant have gained
widespread popularity. However, there are privacy concerns surrounding these devices,
as they continuously listen for voice commands, potentially capturing and storing
sensitive information. This case study examines the privacy implications of AI voice
assistants, the handling of user data, and the importance of transparent data practices and
user consent.
Slide 6: Conclusion
References