AI Safety & Risk for Decision-Makers

Steps

1. Data controls in the OpenAI platform openai beginner

   Your data is your data. An overview of how OpenAI uses your data, including retention and usage policies.

   Start with the question every executive asks first: 'What happens to our data?' OpenAI's data controls page explains the critical distinction between consumer products (where data may be used for training) and the API (where it is not by default). This directly addresses board-level concerns about customer data privacy. Understanding these controls helps you ask the right questions of any AI vendor.

2. Reduce Hallucinations anthropic-platform intermediate

   Hallucination — when AI generates confident-sounding but incorrect information — is the primary quality risk in any AI product. This guide explains mitigation techniques: grounding the AI in source documents, requiring citations, and building explicit 'I don't know' escape hatches. For decision-makers, the key takeaway is that hallucination risk is manageable with proper engineering, but it can never be eliminated entirely. Your risk tolerance should drive your use case selection.

3. Safety best practices openai intermediate

   Comprehensive safety practices for responsible AI deployment — covering moderation, adversarial testing, human oversight, prompt engineering for safety, and production monitoring.

   OpenAI's safety guide introduces the defense-in-depth principle: no single safety measure is sufficient, so you layer multiple protections. Think of it like physical security — you don't rely on just a lock, you add cameras, alarms, and guards. For product leaders, this means budgeting for safety engineering as a percentage of total AI development cost, not as an afterthought.

4. Safety Modes cohere intermediate

   The safety modes documentation describes how to use default and strict modes in order to exercise additional control over model output.

   Cohere's safety modes show that AI safety can be configured, not just coded — you set a safety level (strict, default) rather than building custom filters. This is an important mental model for leaders: safety exists on a spectrum, and different use cases warrant different settings. An internal analytics tool needs less safety filtering than a customer-facing chatbot.

5. The EU's AI Act and How Companies Can Achieve Compliance ai-strategy article beginner

   Practical guide to the EU AI Act — the world's first comprehensive AI law — explaining risk categories, compliance requirements, and what companies need to do to prepare.

   The EU AI Act is the first comprehensive AI law globally and affects any company serving European customers. This HBR article translates the legal framework into business terms: risk tiers, compliance timelines, and what documentation you need. Even if you don't serve EU customers today, this regulatory framework is becoming the global template. Bring this to your next board meeting.

6. Designing a Responsible AI Program? Start with this Checklist ai-strategy article beginner

   Eight critical questions organizations should answer before implementing enterprise-wide responsible AI programs to avoid rushing deployment and wasting resources.

   Close the path with a practical checklist for standing up a responsible AI program. The eight questions here — from defining objectives to designing metrics to creating a strategic roadmap — give you a concrete framework to bring to your leadership team. Use this as the agenda for your first AI governance meeting.