Who's Afraid of the Black Box Models?
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**Prayson is on the mission to help companies gain AI competitive advantage by growing revenue, slashing production timelines, multiplying efficiency and making data-driven decisions.** Take a dive into the deep depths and pitfalls of explainable machine learning, going beyond the illusions of interpretability and explainability. Draw from Prayson's experience and explore how ethical data handling and counter-factual fairness model testing help in keeping black-box models and yet satisfy GDPR and ALTAI ([Guidelines for Trustworthy AI](https://digital-strategy.ec.europa.eu/en/library/communication-building-trust-human-centric-artificial-intelligence))
Transcript
Prayson is on the mission to help companies gain AI competitive advantage by growing revenue, slashing production timelines, multiplying efficiency and making data-driven decisions.
Take a dive into the deep depths and pitfalls of explainable machine learning, going beyond the illusions of interpretability and explainability.
Draw from Prayson's experience and explore how ethical data handling and counter-factual fairness model testing help in keeping black-box models and yet satisfy GDPR and ALTAI (Guidelines for Trustworthy AI)
About the speakers
On a mission to help companies gain AI competitive advantage