EbayLauren WilcoxAnnouncement
eBay's Responsible AI Principles
Summary
eBay sets out principles for developing and using AI-powered customer tools and services while addressing safety and community needs. Its framework covers reliability, safety, security, inclusivity, equity, fairness, accountability, lawfulness, privacy by design, and transparency. The company says it rigorously tests AI technologies, protects confidential data used to train or operate systems, includes measures to avoid excluding groups, and applies quality and safety checks to datasets. It also calls for human oversight throughout development, launch, and monitoring, risk-based decisions, regulatory compliance, and privacy-focused design using state-of-the-art methods. For users, eBay says transparency may involve disclosures within an experience or broader communication, depending on lifecycle stage and the audience.
Context
eBay describes responsible AI principles for AI-powered customer tools and services, stating that these technologies must be implemented safely while meeting the needs of its community.
Approach / What changed
The principles address reliable, safe, secure, inclusive, equitable, fair, accountable, lawful, privacy-focused, and transparent AI. The policy includes rigorous testing, confidential-data protections, dataset checks, human oversight, risk-based decision-making, regulatory compliance, Privacy by Design, and user disclosures about AI use.
Takeaways
AI systems are expected to be reliable, safe, secure, resilient, and designed to perform as intended under varied conditions.
Human oversight is intended to span AI development, launch, and monitoring, alongside risk-based decision-making and compliance with regulatory frameworks.
Transparency may be provided within an AI-enabled experience or through broader disclosures, depending on the technology’s lifecycle stage and the role of the people interacting with it.
Source and freshness
- Source
- innovation.ebayinc.com
- Published
- Added to Yomu
Summaries are generated from the original by a language model. Check the original before relying on it.
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