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AI and ML - Coggle Diagram
AI and ML
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Cybersecurity Risks
Machine Learning Attacks
Description: Actors exploit a defense system by feeding subtle, imperceptible data that tricks the AI into ignoring malicious instructions.
Options:
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Make a "defense-in-depth" approach combining AI defense tools with human oversight and basic rule-based firewalls.
Relying on an easily manipulated AI model allows for unacceptable vulnerabilities. Care ethics emphasizes interconnectedness and vulnerability. Organizations have the responsibility to protect their users and employees from system collapse caused by technological blind spots.
Automated Cyberattacks
Description: A hostile nation used an AI model to develop super-personalized, convincing phishing emails and malware at an unpredictable scale, targeting energy infrastructure.
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Focus on hardening grid defenses, improving employee security training, and deploying more advanced filtering.
Try to avoid escalating into a cyber arms race over infrastructure. Just War Theory suggests that defensive measures are necessary, non-escalatory, and aimed at avoiding reckless harm, with adherence to ethical restraint.
Ethical Challenges
Bias in Criminal Justice Risk System
Description: A court uses an ML risk-assessment tool to help determine bail lengths, but it has historically penalized minority defendants because of historical crime data.
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AI in Warfare
Description: A military designs fully autonomous AI-powered drones that can target and engage targets without human authorization.
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Ban fully autonomous lethal systems, allowing humans to make their own life-or-death decisions on the battlefield.
(assignment speaking) Allowing machines to do our bidding takes away human moral accountability and violates humanitarian principles. Through the lens of Moral Agency and Principlism, machines lack moral agency, conscience, and empathy; therefore, life-or-death decisions require human agents with moral responsibility and accountability.