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AI and ML - Coggle Diagram
AI and ML
Privacy and Security Challenges
Data breaches and misuse or personal information
Scenario: A company storing data without proper encryption
lack of transparency in data handling
Scenario: An AI algorithm that collects personal data without user consent.
Ethical: Implementing strong encryption and transparent data practices
Unethical: Collecting data without informing the user or securing it properly
My choice: Utilizing the ethical choice because it builds Transparency and trust. Deontology supports this choice because it focuses on the duty to protect individuals' rights to privacy and transparency, ensuring that companies follow strict rules when handling personal data.
Cybersecurity Advantages
AI detecting anomalies in real time
Scenario: AI identifying a phishing attack before it reaches users
Ethical: Using AI to protect users’ data and privacy.
Unethical: Ignoring potential biases in AI that could lead to misidentifying legitimate users.
My choice: Ethical, as using AI to enhance security aligns with protecting user interests. Utilitarianism is relevant because using AI for cybersecurity benefits the majority by reducing the risk of cyber attacks.
automatic threat detection and responses
Scenario: ML system preventing unauthorized access to sensitive information
Cybersecurity Risks
Adversarial attacks on AI systems
Scenario: hackers manipulating AI to bypass security
Ethical: Ensuring that AI systems are protected from adversarial manipulation.
Unethical: Using AI to surveil users without their consent.
My choice: Ethical, as AI should be used for protection, not for violating privacy. Deontology supports this decision, as it focuses on respecting individuals' rights to privacy, and unauthorized surveillance would violate those rights.
AI driven surveillance in systems
Scenario: AI systems used for mass surveillance violating privacy
Ethical Challenges
Bias in AI algorithms
Scenario: an ai system showing bias in hiring decisions
Accountability in AI decisions
Scenario: difficulty in assigning responsibility when AI systems fail
Ethical: Actively working to reduce biases in AI training data.
Unethical: Ignoring biases or failing to audit AI decisions.
My Choice: Ethical, because fairness in AI is essential to avoid discrimination. Virtue ethics would argue that fairness and justice are key virtues, and addressing biases in AI reflects those values, ensuring fairness for all candidates.