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Addressing the moral challenges posed by artificial intelligence, algorithms, and mass surveillance.
Imagine a judge sentencing a defendant based not on the law, but on a 'risk score' generated by a secret algorithm that no human—not even the judge—fully understands. Is this efficiency, or the end of justice?
In modern ethics, Machine Learning Opacity refers to the 'black box' nature of complex algorithms where the logic behind an output is hidden from users. This becomes a moral crisis when Algorithmic Bias occurs. Algorithms are not neutral; they learn from historical data that often contains human prejudices. If a hiring AI is trained on data from a company that historically only hired men, the AI may mathematically conclude that 'being male' is a requirement for success. Because these systems are often proprietary or too complex to audit, we face a lack of accountability. When an algorithm makes a life-altering decision, who is responsible: the programmer, the data, or the machine itself?
1. A tech company uses an AI to filter 10,000 resumes. 2. The AI identifies a correlation: candidates who played 'lacrosse' are more likely to be promoted. 3. It begins automatically rejecting candidates who didn't play lacrosse. 4. Because lacrosse is an expensive sport, the AI has unintentionally created a socio-economic bias, even though 'wealth' was never a programmed variable.
Quick Check
Why is 'opacity' in machine learning considered a threat to justice?
Answer
Because if we cannot see or understand the logic behind a decision, we cannot challenge its fairness or hold anyone accountable for errors.
Digital privacy is often framed as a trade-off against National Security. Proponents of mass surveillance argue that 'if you have nothing to hide, you have nothing to fear.' However, philosophers point to the Panopticon effect: when people believe they are being watched, they self-censor and lose the freedom to explore radical or creative ideas. This creates a power imbalance where the state or corporations possess 'Information Asymmetry' over the individual. The ethical question is whether the collective safety gained by monitoring encrypted data outweighs the individual's right to a private digital life, which is increasingly considered a fundamental human right in the 21st century.
Consider a government program that collects 'metadata' (who you call, when, and for how long) but not the audio of the calls. 1. The state argues this is a minor privacy intrusion for anti-terrorism. 2. Ethicists argue that . 3. By mapping your social graph, the state can predict your political leanings, religion, and health status without ever hearing a word you say.
Quick Check
What is the 'Nothing to Hide' fallacy in the context of digital privacy?
Answer
It assumes privacy is only about hiding wrongdoing, ignoring that privacy is essential for personal autonomy and protection against power imbalances.
The Trolley Problem is no longer a thought experiment; it is a programming requirement for Autonomous Vehicles (AVs). If a car's brakes fail, should it be programmed to hit a group of five pedestrians or swerve and hit one bystander? This forces a choice between Utilitarianism (minimizing total harm: ) and Deontology (the rule that killing is inherently wrong, regardless of the numbers). Furthermore, should the car prioritize the safety of its own passengers over pedestrians? These 'moral algorithms' must be decided before the car ever hits the road, shifting ethics from a reactive human choice to a proactive line of code.
Imagine an AV must choose between: 1. Swerving into a concrete wall (killing the 1 passenger). 2. Continuing straight and hitting a school bus (potentially injuring 20 children). 3. If the car is programmed with a 'Passenger Priority' logic, it chooses option 2. If it uses 'Utilitarian' logic, it chooses option 1. The challenge: Would you ever buy a car that is programmed to kill you to save others?
Which term describes a situation where an AI's decision-making process is hidden from the user?
A utilitarian approach to programming a self-driving car would prioritize:
The 'Panopticon effect' suggests that people act more freely when they know they are being monitored.
Review Tomorrow
In 24 hours, try to explain the difference between Utilitarian and Deontological approaches to the 'Trolley Problem' in self-driving cars.
Practice Activity
Research a real-world case of algorithmic bias (e.g., in facial recognition or credit scoring) and identify what 'biased data' might have caused the issue.