AI Ethics

Principles of AI Ethics

Enabling AI Ethics

Transparency

AI models must be explainable, trustworthy, and accountable if harm is caused

Policies

Corporate guidelines for transparency, fairness, accountability, and respect for human rights

Privacy

AI should not expose private information, especially when integrating with public LLMs

Multi-stakeholder

Employees, customers, regulators, and community engagement for broad ethical perspectives

Non-maleficence

AI exists to enhance human capabilities and intelligence and must benefit humanity and avoid harm

Integrate

Ethics integrated into AI Development Lifecycle, including algorithm development, choice of data sources, and avenues of redress

AI Goverance

Effective governance requires a hybrid team with stakeholders from multiple business units to oversee implementation and adoption. We recommend that team establish key security and performance metrics while drawing on already well-established, industry-specific frameworks. Every use case is different. We work with subject matter experts within organizations to develop guidelines and metrics to safely and ethically harness the enormous power of AI.

AI Risk and Mitigation

AI risk falls into three categories:

Privacy: leaking private information
Hallucinations: generated output may be false or misleading
Bias: Systematically false output regarding a specific group

We mitigate or eliminate those risks by:

Obfuscation: Private information is never sent off prem
Guardrails: Keep LLMs on-topic and factual
Human-in-the-loop: Built-in quality checks and stakeholder oversight

How do we mitigate data and algorithmic bias?

Our AI models are built on diverse and representative data. We take special care to ensure our end-uses understand the sources of the data they use and that it includes data from underrepresented groups.

Content vs. Decision

Generative AI bias influences content creation. Discriminative AI bias leads to unfair decisions. Built into our platform is robust and regular auditing for bias as well as the ability to test diverse scenarios and generate independent assessments.

Selection bias: what if the data itself is biased?

All stakeholders in our platform are trained to recognize bias in data sets in order to mitigate its influence.

What is obfuscation?

Obfuscation means that, before any information is sent to a public LLM, sensitive data is removed. So your information never becomes part of the training data or vocabulary of a public LLM.

And, if greater privacy is required, we can train a private LLM for you.

Why are guardrails necessary?

Guardrails force the output of an LLM to stay on topic and factual. Our platform integrates with your enterprise APIs so that it only learns facts from the data you give it.