The AI Ethics Brief #188: The Names We Give Things

2026-01-20 · Source: The AI Ethics Brief · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Emerging Technologies & Innovation · Depth: Intermediate, long

Summary

The AI Ethics Brief highlights critical discrepancies between stated purposes and actual uses of AI technologies, focusing on data collection, AI emotional attribution, and policy definitions. Niantic Spatial, for example, launched a commercial visual positioning system trained on 30 billion images crowdsourced from Pokémon GO players, raising questions about user consent for long-term data repurposing. LinkedIn's "BrowserGate" scandal revealed silent device fingerprinting of its one billion users, scanning for over 6,000 browser extensions and collecting 48 hardware/software characteristics without disclosure in its privacy policy. Furthermore, Anthropic's research into Claude's "emotional vectors" underscores the need for careful language to avoid anthropomorphizing AI, a misinterpretation that has led to tragic consequences for teenagers. The brief also notes the divergence in "AI for Good" definitions between China and the EU, and Wikipedia's ban on AI-generated content due to factual inaccuracies.

Key takeaway

For CTOs and VPs of Engineering/Data evaluating AI deployments, you must critically assess the full lifecycle of data use and the implications of AI's public perception. Ensure your organization's data collection practices are transparent and align with user expectations, not just technical legality, to avoid future "BrowserGate"-like scandals. Additionally, mandate precise, non-anthropomorphic language when discussing AI capabilities to prevent dangerous overestimation by users and stakeholders, especially concerning emotional or cognitive functions.

Key insights

The gap between AI's stated purpose and its actual implementation creates ethical and privacy challenges.

Principles

• Data repurposing requires explicit, informed consent.
• Anthropomorphic language for AI can lead to harmful overestimation.
• AI policy terms like "AI for Good" lack universal definition.

Method

The article analyzes case studies of data collection (Niantic, LinkedIn) and AI model interpretation (Anthropic's "emotional vectors") to expose the divergence between AI's naming and its reality, highlighting the need for scrutiny.

In practice

• Scrutinize data collection practices beyond initial opt-ins.
• Avoid anthropomorphic terms when describing AI capabilities.
• Examine underlying definitions in AI policy discussions.

Topics

• AI Ethics
• Data Privacy
• Device Fingerprinting
• World Models
• Anthropomorphism in AI

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Ethicist, Policy Maker, Legal Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI Ethics Brief.