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World Congress 2025 • Aug 20, 2025 • Session details

AI & Ethics

PJ Hagerty

Generative AI hype often drains massive compute on easily solvable logic problems. Learn to implement ethics-first guardrails and build responsible models that end toil instead of human jobs.

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#1 about 3 min

Differentiating simple algorithms from true artificial intelligence

Differentiating simple algorithms from true artificial intelligence prevents overpromising basic binary systems as autonomous human behavioral mimics.

#2 about 2 min

Defining machine learning capabilities and historical origins

Tracking the historical evolution of explicit programmatic instructions clarifies how modern statistical models adapt autonomously without hardcoded constraints.

#3 about 2 min

The ethical quandaries of deep learning and neural networks

Simulating complex human brain power in computers without fully understanding biological brain mechanics introduces severe ethical risks into neural networks.

#4 about 2 min

How large language models relate to cultural ethical standards

Training enormous foundation models on billions of diverse data points forces technologists to navigate fluid cultural ethical standards.

#5 about 3 min

Inherited training bias and the illusion of intelligent reasoning

Unfiltered historical training data transfers human prejudices to language models that ultimately lack the capacity for independent reasoning.

#6 about 2 min

The severe environmental costs of misapplying large language models

Replacing efficient databases with resource-intensive language models for simple forecasting tasks wastes computational power and harms the climate.

#7 about 4 min

Distinguishing genuine artificial intelligence from business marketing hype

Overstated business usage statistics confuse standard natural language processing applications with the nonexistent capabilities of fully autonomous reasoning systems.

#8 about 2 min

Navigating outdated training sets and malicious generative image manipulation

Delays in training large language models produce obsolete safety constraints that allow bad actors to easily exploit image generation.

#9 about 3 min

Recognizing the current toddler-like limitations of modern intelligence models

Applying human attributes to software obscures the reality that current models merely parrot text rather than demonstrating independent logical reasoning.

#10 about 3 min

Applying historical philosophical frameworks to preserve end-user dignity

Adopting historical philosophical frameworks ensures technologies are built to eliminate mundane workplace toil rather than replace human creativity.

#11 about 3 min

Examining the devastating impact of unregulated and homogenous technical testing

Deploying homogenous testing data creates racially biased systems that highlight the urgent necessity for open source community auditability.

#12 about 2 min

Addressing lack of consumer consent and lagging government regulations

Moving fast without consumer consent mechanisms leaves automated systems vulnerable to failures while slow government regulations lag behind technical reality.

#13 about 3 min

Implementing active guardrails and data anonymization to ensure compliant platforms

Deploying proactive prompt filtering and masking personally identifiable information prevents toxic outputs and ensures compliance with strict privacy laws.

#14 about 2 min

Unlearning bad contextual data and avoiding technology vendor marketing hype

Stripping unethical data from models without destroying related contextual connections provides a pathway past misleading marketing towards safe engineering practices.

Matching moments

1:34 min

Mitigating the inherent challenges of generative AI tools

Mary Grygleski Mary Grygleski · LIVE

3:17 min

Balancing AI regulation with technological innovation in human resources

Rudi Bauer Rudi Bauer +1 · Cappuccino with HR

5:18 min

Addressing psychological safety and ethical risks of AI adoption

Vera Slavnić Vera Slavnić · Europe 2026 Virtual

1:34 min

Introduction to responsible artificial intelligence and societal impact

Toju Duke · WWC 2022

9:39 min

Predicting the future intersection of artificial intelligence and accessibility

Léonie Watson Léonie Watson +4 · A11y + AI

1:28 min

Core challenges facing the generative AI developer ecosystem today

Prashanth Chandrasekar Prashanth Chandrasekar · WWC 2024

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