<h1 id="challenges-and-risks"><a aria-hidden="true" class="anchor-heading" href="#challenges-and-risks"><svg aria-hidden="true" viewBox="0 0 16 16"><use xlink:href="#svg-link"></use></svg></a>Challenges and Risks</h1>
<h2 id="challenges-and-risks-with-ai"><a aria-hidden="true" class="anchor-heading" href="#challenges-and-risks-with-ai"><svg aria-hidden="true" viewBox="0 0 16 16"><use xlink:href="#svg-link"></use></svg></a>Challenges and risks with AI</h2>
<table aria-label="Challenges and risks with AI" class="table">
<thead>
<tr>
<th>Challenge or Risk</th>
<th>Example</th>
</tr>
</thead>
<tbody>
<tr>
<td>Bias can affect results</td>
<td>A loan-approval model discriminates by gender due to bias in the data with which it was trained</td>
</tr>
<tr>
<td>Errors may cause harm</td>
<td>An autonomous vehicle experiences a system failure and causes a collision</td>
</tr>
<tr>
<td>Data could be exposed</td>
<td>A medical diagnostic bot is trained using sensitive patient data, which is stored insecurely</td>
</tr>
<tr>
<td>Solutions may not work for everyone</td>
<td>A home automation assistant provides no audio output for visually impaired users</td>
</tr>
<tr>
<td>Users must trust a complex system</td>
<td>An AI-based financial tool makes investment recommendations -  what are they based on?</td>
</tr>
<tr>
<td>Who's liable for AI-driven decisions?</td>
<td>An innocent person is convicted of a crime based on evidence from facial recognition – who's responsible?</td>
</tr>
</tbody>
</table>
<h2 id="6-principles-of-responsible-ai"><a aria-hidden="true" class="anchor-heading" href="#6-principles-of-responsible-ai"><svg aria-hidden="true" viewBox="0 0 16 16"><use xlink:href="#svg-link"></use></svg></a>6 Principles of Responsible AI</h2>
<ol>
<li>Fairness</li>
<li>Reliability and Safety</li>
<li>Privacy and Security</li>
<li>Inclusiveness</li>
<li>Transparency</li>
<li>Accountability</li>
</ol>

Challenges and Risks


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Challenge or Risk	Example
Bias can affect results	A loan-approval model discriminates by gender due to bias in the data with which it was trained
Errors may cause harm	An autonomous vehicle experiences a system failure and causes a collision
Data could be exposed	A medical diagnostic bot is trained using sensitive patient data, which is stored insecurely
Solutions may not work for everyone	A home automation assistant provides no audio output for visually impaired users
Users must trust a complex system	An AI-based financial tool makes investment recommendations - what are they based on?
Who's liable for AI-driven decisions?	An innocent person is convicted of a crime based on evidence from facial recognition – who's responsible?