Ten key problems when deploying AI

Ten key issues with AI.

No.	Issue	Definition	Impact	Cause
1	Hallucinations and Factual Inaccuracy	AI models can produce incorrect or fabricated outputs.	Critical in sensitive domains like healthcare, law, journalism, and finance.	Models prioritize fluency over factual correctness.
2	Bias and Discrimination	AI models can amplify societal biases.	Leads to unfair practices and perpetuates stereotypes.	Biased training data.
3	Intellectual Property and Copyright Infringement	AI-generated content may infringe on existing copyrights.	Legal and ethical concerns, lawsuits.	Unsettled legal frameworks.
4	Misinformation and Disinformation	AI can create realistic fake content.	Threatens trust in media, used for manipulation and fraud.	Increasingly difficult to detect fakes.
5	Environmental Impact and Resource Intensity	AI requires significant computational power and energy.	Contributes to climate change and resource strain.	High energy consumption and cooling needs.
6	Lack of Transparency and Explainability	AI models are often “black boxes.”	Difficult to identify errors, build trust, and ensure fairness.	Complex internal workings.
7	Data Privacy and Security Risks	AI may reproduce sensitive information from training data.	Data breaches and privacy violations.	Large amounts of data used.
8	Job Displacement and Workforce Transformation	AI can automate tasks traditionally done by humans.	Job displacement and need for reskilling.	Automation of various tasks.
9	Over-reliance and Loss of Critical Thinking	Users may over-rely on AI without fact-checking.	Decline in critical thinking skills and spread of unverified information.	Increased use of AI tools.
10	Ethical Governance and Regulation Lag	Legal and ethical frameworks lag behind AI development.	Regulatory vacuum and difficulty in establishing guidelines.	Rapid pace of AI development.

Ten key issues with AI.

No.	Issue	Definition	Impact	Cause
1	Hallucinations and Factual Inaccuracy	AI models can produce incorrect or fabricated outputs.	Critical in sensitive domains like healthcare, law, journalism, and finance.	Models prioritize fluency over factual correctness.
2	Bias and Discrimination	AI models can amplify societal biases.	Leads to unfair practices and perpetuates stereotypes.	Biased training data.
3	Intellectual Property and Copyright Infringement	AI-generated content may infringe on existing copyrights.	Legal and ethical concerns, lawsuits.	Unsettled legal frameworks.
4	Misinformation and Disinformation	AI can create realistic fake content.	Threatens trust in media, used for manipulation and fraud.	Increasingly difficult to detect fakes.
5	Environmental Impact and Resource Intensity	AI requires significant computational power and energy.	Contributes to climate change and resource strain.	High energy consumption and cooling needs.
6	Lack of Transparency and Explainability	AI models are often “black boxes.”	Difficult to identify errors, build trust, and ensure fairness.	Complex internal workings.
7	Data Privacy and Security Risks	AI may reproduce sensitive information from training data.	Data breaches and privacy violations.	Large amounts of data used.
8	Job Displacement and Workforce Transformation	AI can automate tasks traditionally done by humans.	Job displacement and need for reskilling.	Automation of various tasks.
9	Over-reliance and Loss of Critical Thinking	Users may over-rely on AI without fact-checking.	Decline in critical thinking skills and spread of unverified information.	Increased use of AI tools.
10	Ethical Governance and Regulation Lag	Legal and ethical frameworks lag behind AI development.	Regulatory vacuum and difficulty in establishing guidelines.	Rapid pace of AI development.