AI BriefingIn-Depth Analysis of AI Safety
Key point
This piece defines AI safety based on the principles of algorithmic fairness and presents the types of risks companies face along with response strategies.
Details
The safety of generative AI is mired in confusion due to ambiguous definitions and sensationalized reporting. To secure practical safety, rather than vague fears about AGI, attention should be focused on immediate issues such as LLM bias, the spread of misinformation, and legal and data compliance.
The harms that AI can cause fall broadly into three categories.
- User Harm: Cases where individuals are directly harmed, such as exposure to stereotypes or loss of employment opportunities
- Societal Harm: Social inequality arising from systemic errors affecting specific groups
- Harm by Malicious Actors: The spread of spam and misinformation
At the core of the safety discussion lies the principle of Algorithmic Fairness. This is broadly divided into two types of harm.
- Allocational Harm: The unequal distribution of resources (e.g., a resume-summarization model showing lower accuracy for a specific gender)
- Representational Harm: Harm to public image or opinion (e.g., generating stereotypes about a specific group)
Recent research trends are moving beyond dataset classification toward addressing Generative Representational Harms, as generative models become mainstream.
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