World Congress 2025 Aug 20, 2025 Session details

Kill Switch or Moral Compass: Who Programs AI’s Conscience?

Torsten Stiller

Amazon's biased CV screener proved the danger of unchecked algorithms. Learn to proactively engineer a moral compass into your AI workflows so the kill switch gathers dust.

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#1 about 3 min

The hidden dangers of automated algorithms

How an e-commerce recommendation engine inadvertently flattened product diversity by lacking a moral compass.

#2 about 3 min

The limits of technical rules in robotics

Why hardcoded directives alone fail to resolve complex moral conflicts in artificial systems.

#3 about 2 min

The developer role in programming artificial conscience

How daily design choices and data selection embed specific values and biases into machine learning models.

#4 about 7 min

Real-world tech failures from missing ethical guardrails

Case studies of major tech failures involving flawed algorithms, amplified bias, and autonomous accidents.

#5 about 7 min

Core principles for building responsible systems

Strategies to ensure fairness, transparency, accountability, and proactive safety measures in technical applications.

#6 about 4 min

Actionable playbook for ethical software development workflows

Practical methods for integrating ethical considerations into standard workflows using adversary testing and empathy exercises.

#7 about 3 min

Regulatory pressures and the EU AI Act

How upcoming legislation is transforming ethical design from a theoretical ideal into a strict compliance mandate.

#8 about 3 min

Balancing technical fail-safes with proactive moral compasses

Why developers must architect systems that inherently respect human agency rather than relying solely on emergency disengagements.

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Introduction to responsible artificial intelligence and societal impact

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Aligning artificial intelligence safeguards with corporate realities

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Implementing responsible artificial intelligence frameworks to mitigate model bias

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1:58 min

Prioritizing human impact and professional responsibility in AI engineering

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