A short insight into AI

1. ARTIFICIAL INTELLIGENCE (AI):
CONCEPTS, APPLICATIONS, AND
ETHICAL CONSIDERATIONS
COMPILED BY MR B MAIMELA

2. INTRODUCTION TO AI
AI refers to machines or
applications performing tasks
requiring human-like intelligence
(Dumont et.al, 2016; Artificial
Intelligence Class IX, n.d.).

3. Key Domains:
• Data Science: Extracts insights from structured/unstructured data (Artificial
Intelligence Class IX, n.d.).
• Computer Vision: Trains machines to identify objects like humans (Artificial
Intelligence Class IX, n.d.).
• Natural Language Processing (NLP): Enables human-computer language
interactions (Artificial Intelligence Class IX, n.d.).

4. ADVANTAGES AND DISADVANTAGES OF AI
Advantages
• Reduced errors (Artificial Intelligence Class IX, n.d.).
• Improved decision-making (Artificial Intelligence Class IX, n.d.).
• 24/7 operation (Artificial Intelligence Class IX, n.d.).

5. DISADVANTAGES
• High implementation costs (Artificial Intelligence Class IX, n.d.).
• Dependence on hardware/software (Artificial Intelligence Class IX,
n.d.).
• Limited to predefined tasks (Artificial Intelligence Class IX, n.d.).

6. AI PROJECT CYCLE
1. Problem
Scoping –
Define the
problem
4.
Modelling –
Develop AI
algorithms
3. Data
Exploration –
Identify patterns
5. Evaluation –
Test model
accuracy
6. Deployment –
Implement in
real-world
scenarios
2. Data
Acquisition –
Collect reliable
data

7. AI IN ACTION – REAL-WORLD APPLICATIONS
• Healthcare: AI aids in diagnosis
and treatment (Smith, 2017).
• Finance: Fraud detection and
risk management (Wesi, n.d.).
• Retail: Chatbots for customer
service (Biswas, n.d.).
• Autonomous Vehicles: Self-
driving cars (Smith, 2017).

8. ETHICAL CONSIDERATIONS IN AI
• Accountability: Who is responsible for AI decisions? (Artificial
Intelligence Class IX, n.d.).
• Bias: AI can inherit biases from training data & therefore could make
biased decisions (Artificial Intelligence Class IX, n.d.).
• Privacy: Data security risks (Biswas, n.d.).
• Transparency: Need for explainable AI (Biswas, n.d.).

9. THE FUTURE OF AI
• Agentic AI: Agentic AI represents the next evolution of artificial intelligence, where systems move
beyond simple reactive responses to become proactive, autonomous entities capable of:
• Goal-directed behavior: Setting and pursuing objectives without constant human oversight e.g., an AI
concierge like Hilton's "Connie" that learns guest preferences over time (Smith, 2017, slide 2)
• Dynamic task decomposition: Breaking complex goals into sub-tasks e.g., planning a trip by
autonomously booking flights, hotels, and activities (Biswas, n.d., slide 17)
• Self-improvement: Continuously refining strategies through reinforcement learning (Wesi, n.d., slide 8)
• Key Distinction from Traditional AI:
While conventional AI follows predefined rules (like IBM's chess-playing Deep Blue), agentic AI exhibits
emergent behaviors - as demonstrated by Microsoft's AI "objects" that independently perform tasks
(Biswas, n.d., slide 4).
1. Agentic AI: Autonomous, Goal-Driven Systems

10. 2. HUMAN-AI COLLABORATION: ENHANCING
PRODUCTIVITY
• Operational Models:
• Augmented Intelligence: AI assists human decision-making e.g., IBM's automated
radiologist highlights potential issues for doctor review (Smith, 2017, slide 31)
• Symbiotic Workflows:
• Humans provide oversight and ethical judgment
• AI handles repetitive tasks (Smith, 2017, slide 32)
• Sector-Specific Examples:
• Healthcare: AI analyzes treatment options while physicians make final decisions (Smith,
2017, slide 22)
• Manufacturing: Predictive maintenance systems alert human technicians only when needed
(Wesi, n.d., slide 5)
• Productivity Impact:
Contrary to replacement fears, only 5% of European AI startups focus on job displacement -
most enhance human capabilities (Dumont et.al, 2016, slide 16).

11. 3. REGULATIONS: ENSURING ETHICAL AI
DEVELOPMENT
Current Frameworks:
• EU GDPR (2018):
• Right to contest automated decisions (Smith, 2017, slide 45)
• Requires transparency in AI decision-making processes
• IBM's Ethical AI Principles (Smith, 2017, slides 40-42):
• Purpose: AI should aid, not replace humans (symbolic relationship)
• Transparency: Disclose training data and control mechanisms
• Skills: Train human workers to use AI tools effectively

12. EMERGING CHALLENGES:
ALGORITHMIC BIAS: "WE OFTEN HAVE NO WAY
OF KNOWING WHEN AND WHY PEOPLE ARE
BIASED" (WACHTER IN SMITH, 2017, SLIDE 44)
PRIVACY RISKS: LLMS MAY LEAK TRAINING DATA
PROPERTIES (BISWAS, N.D., SLIDE 48)
ACCOUNTABILITY: NEED FOR EXPLAINABILITY IN
MULTI-AGENT SYSTEMS (BISWAS, N.D., SLIDE 49)
IMPLEMENTATION CASE:
SALESFORCE'S "AGENTFORCE" INCORPORATES
ETHICAL GUARDRAILS FOR AUTONOMOUS AI
AGENTS IN CUSTOMER SERVICE (BISWAS, N.D.,
SLIDE 4).

13. CONCLUSION
AI is transforming industries but requires ethical oversight. Improper use
of AI can have severe consequences. Continuous learning and
adaptation are key. In a rapidly evolving world, the question we have to
ask ourselves is: How can we ensure AI benefits society responsibly?

14. REFERENCES:
•Biswas, D. (n.d.). A comprehensive guide to agentic AI systems [PDF slides].
SlideShare. Retrieved August 18, 2025, from
https://www.slideshare.net/slideshow/a-comprehensive-guide-to-agentic-ai-systems-c7
42/274678426
•Dumont, J.-B.; Verdillion, L. & Gossart, E. (2016). The AI rush [PDF slides].
SlideShare. https://www.slideshare.net/slideshow/the-ai-rush/81139551
•Smith, C. (2017, October 13). AI and machine learning demystified by Carol Smith at
Midwest UX 2017 [PowerPoint slides]. SlideShare.
https://www.slideshare.net/slideshow/ai-and-machine-learning-demystified-by-carol-smi
th-at-midwest-ux-2017/80840514
•SweetandSourCandy, S. (n.d.). Artificial intelligence PPT-Class IX [PDF slides].
SlideShare. Retrieved August 18, 2025, from
https://www.slideshare.net/slideshow/artificial-intelligence-ppt-class-ix-pdf/271627720
•Wesi, M. (n.d.). Agentic AI: The next wave of intelligence [PowerPoint slides].
SlideShare. Retrieved August 18, 2025, from
https://www.slideshare.net/slideshow/agentic-ai-the-next-wave-of-intelligence-pptx/275
308782

15. IMAGES:
• Emory University. (n.d.). Ethics-themed visual from Emory Responsible AI promotes the ethical use of Artificial
Intelligence [AI-Ethics1/JPEG]. Emory University, Emory Continuing Education: Responsible AI programme
article. Retrieved August 18, 2025, from
https://ece.emory.edu/articles-news/responsible-ai-program.php
• Goddard, W. (2020, November 8). Where is AI used today? [where-is-ai-used-1024x683 (1)/JPEG]. IT
Chronicles. Retrieved August 18, 2025, from
https://itchronicles.com/artificial-intelligence/where-is-ai-used-today/
• Shoolini University. (n.d.). [Applications-of-AI/PNG]. In AI applications take the world by storm (blog post).
Shoolini University. Retrieved August 18, 2025, from
https://shooliniuniversity.com/blog/ai-applications-take-the-world-by-storm/