Name: Can AI Voices Be Trusted? Tackling Hallucination, Transparency & Accountability
Start: 2025-02-27T17:00:00-08:00
End: 2025-02-27T19:00:00-08:00
Location: DTLA Arts District

**[RSVP AT https://lu.ma/00c77tjb](https://lu.ma/00c77tjb)**

Post MIT IAP, this seminar series aims to bridge the cutting-edge developments in speech synthesis technology with critical discussions on Al safety and ethics.

Join the Responsible Al Reading Group and Gridspace for an engaging fireside chat and live demonstration exploring the evolving landscape of Al voice agents and voice synthesis technology. As Al-powered voice systems become increasingly embedded in customer service, healthcare, and finance, concerns around transparency, accountability, bias, and misinformation are more pressing than ever.

The session will begin with a discussion on key ethical and technical challenges in Al-generated voices. We will explore how Al voice agents can remain interpretable and predictable, ensuring that businesses deploying voice Al maintain strong accountability mechanisms.

Following this, experts from Gridspace will showcase real-world solutions designed to mitigate hallucination and bias in Al-driven voice interactions. Through a live demonstration, attendees will gain insights into how innovative safeguards are being implemented to enhance the trustworthiness of Al voice technology. Join us for an insightful panel discussion featuring industry leaders, AI experts, and manufacturing innovators as we explore how AI is changing the way companies manufacture their hardware products. We’ll dive into real-world applications and challenges in an AI-driven manufacturing landscape.

Key topics include:
AI technologies driving efficiency, cost reduction, and improved sustainability

The different roles of machine learning and generative AI in manufacturing operations

Challenges to adoption and privacy considerations

Todd

AI LA

Technology

Artificial Intelligence

Robotics

New Technology

Interaction Design

Big Data

User Experience

Natural Language Processing

Machine Learning

Software Development

Automation

Data Science

Technology Startups

Personal Robots

Humanoid Robots

Bots

Amethyst Zhang

Christopher Mayfield

Tania Damha

Noah Petherbridge

Ed Chen

Keith Matthews

Brian Allman

Can AI Voices Be Trusted? Tackling Hallucination, Transparency & Accountability

Audio Engineering

DTLA Arts District

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Can AI Voices Be Trusted? Tackling Hallucination, Transparency & Accountability

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