> For the complete documentation index, see [llms.txt](https://library.xrguild.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://library.xrguild.org/ai-ethics/trustworthy-ai-and-ethics-with-ibm-consultings-phaedra-boinodiris.md).

# Trustworthy AI and Ethics with IBM Consulting's Phaedra Boinodiris

## Overview

**Authors:** AI Interrogator Podcast

**Publication Date:** 8 April 2024

**Link:** <https://aiinterrogator.podbean.com/e/phaedra-boinodiris/>

**Keywords:** trustworthy AI, ethical AI, Inclusive AI, IBM Consulting, governance, diverse teams

**Type:** Audio/Podcast

## Summary

In this episode of Infosys's Being AI-First podcast, AI Interrogator, host Kate Bevan is joined by Phaedra Boinodiris, IBM Consulting's global lead for Trustworthy AI. They delve into the challenges of creating ethical and inclusive AI systems, highlighting the critical role of diverse teams and the next generation in shaping AI's future. Phaedra shares insights on building trustworthy AI, emphasizing the importance of accountability and governance while addressing misconceptions about AI-driven apocalyptic fears.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://library.xrguild.org/ai-ethics/trustworthy-ai-and-ethics-with-ibm-consultings-phaedra-boinodiris.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.