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World Congress 2022 • Jun 15, 2022

Staying Safe in the AI Future

Cassie Kozyrkov

Are your AI models making objective decisions, or seamlessly scaling human negligence? Learn why surviving the Software 2.0 shift requires robust MLOps safety nets and strict pipeline audits.

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#1 about 7 min

Recognizing artificial intelligence as software and coding by example

Framing machine learning as a shift from explicit instructions to defining behavior through data examples.

#2 about 5 min

Defining subjective objectives for evaluating machine learning models

How the fundamental purpose of an automation system determines the correct answers and highlights underlying subjectivity within classifications.

#3 about 2 min

Anticipating perfectly reliable implementations of flawed system objectives

Designing models with the assumption that machines will reliably execute explicit instructions without deducing unstated human intent.

#4 about 4 min

Scaling intelligent decision making across software automation workflows

Applying rigorous human thought to optimization objectives to prevent the rapid automated amplification of negligent parameters.

#5 about 2 min

Adopting site reliability engineering practices for machine learning

Building protective safety nets to mitigate system failures when algorithmic execution inevitably deviates from human expectations.

#6 about 2 min

Selecting valid engineering use cases for algorithmic automation

Limiting artificial intelligence implementations to complex programmatic instructions where traditional code structures become unmaintainable or overly difficult.

#7 about 3 min

Preventing machine learning overfitting through isolated testing data

Validating models against dataset memorization by assessing algorithmic performance using entirely unseen and pristine information repositories.

#8 about 2 min

Identifying hidden variables and spurious training dataset correlations

Evaluating overall model behavior against actual intent rather than assuming positive validation verifies success on spurious environmental variables.

#9 about 3 min

Addressing implicit human biases embedded within algorithmic datasets

Taking responsibility for algorithmic fairness by recognizing that datasets inherently reflect the unspoken values of their human authors.

#10 about 2 min

Ensuring training dataset quality through required team diversity

Catching fundamental dataset discrepancies by mandating comprehensive analytics and introducing multiple diverse perspectives during human review stages.

Matching moments

5:18 min

Addressing psychological safety and ethical risks of AI adoption

Vera Slavnić Vera Slavnić · Europe 2026 Virtual

6:00 min

Building trust and cultural adoption for ai frameworks

Kai Grunwitz Kai Grunwitz +2 · WWC 2025

2:03 min

Addressing institutional inertia and AI pilot failures

Alexandre Guenoun Alexandre Guenoun +3 · WWC Europe 2026

3:08 min

Implementing responsible artificial intelligence frameworks to mitigate model bias

Alexander Wallner Alexander Wallner +3 · WWC 2024

1:39 min

Balancing value and risk for responsible artificial intelligence

Seppe Housen Seppe Housen · Europe 2026 Virtual

1:34 min

Introduction to responsible artificial intelligence and societal impact

Toju Duke · WWC 2022

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