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class="overflow-hidden text-ellipsis group-[.active]:bg-zinc-800 group-[.active]:border-b-zinc-800 group-[.active]:border-gray-500 border-l px-3 py-2 border-r border-t border-b border-gray-600 border-b-gray-500 h-11 whitespace-nowrap">
Robustness
<span
class="ml-1 rounded-full text-xs min-w-4 min-h-4 px-1 py-0.5 inline-block text-center bg-red-400">
2
</span>
</div>
</div>
<div data-tab-target="Underconfidence"
class="tab-header cursor-pointer group">
<div
class="overflow-hidden text-ellipsis group-[.active]:bg-zinc-800 group-[.active]:border-b-zinc-800 group-[.active]:border-gray-500 border-l px-3 py-2 border-r border-t border-b border-gray-600 border-b-gray-500 h-11 whitespace-nowrap">
Underconfidence
<span
class="ml-1 rounded-full text-xs min-w-4 min-h-4 px-1 py-0.5 inline-block text-center bg-red-400">
1
</span>
</div>
</div>
<div data-tab-target="Performance"
class="tab-header cursor-pointer group">
<div
class="overflow-hidden text-ellipsis group-[.active]:bg-zinc-800 group-[.active]:border-b-zinc-800 group-[.active]:border-gray-500 border-l px-3 py-2 border-r border-t border-b border-gray-600 border-b-gray-500 h-11 whitespace-nowrap">
Performance
<span
class="ml-1 rounded-full text-xs min-w-4 min-h-4 px-1 py-0.5 inline-block text-center bg-red-400">
1
</span>
</div>
</div>
<div class="flex-grow border-b border-gray-500 h-11"></div>
</div>
<!-- TAB HEADER END -->
<div id="Ethical" role="tabpanel" class="m-4 mb-4">
<div class="p-3 bg-amber-100/40 rounded-sm w-full flex align-middle">
<div class="text-amber-100 mt-1.5">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5"
stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round"
d="M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126zM12 15.75h.007v.008H12v-.008z" />
</svg>
</div>
<div class="ml-2 text-amber-100 text-sm">
<div class="prose">
<p>Your model seems to be sensitive to gender, ethnic, or religion based perturbations in the input data. These perturbations can include switching some words from feminine to masculine, countries or nationalities. This happens when:</p>
<ul>
<li>Underrepresentation of certain demographic groups in the training data</li>
<li>Data is reflecting some structural biases and societal prejudices</li>
<li>Use of complex models with large number of parameters that tend to overfit the training data</li>
</ul>
<p>To learn more about causes and solutions, check our <a href="https://docs.giskard.ai/en/latest/getting-started/key_vulnerabilities/ethics/index.html">guide on unethical behaviour.</a></p>
</div>
</div>
</div>
<div class="flex items-center space-x-1">
<h2 class="uppercase my-4 mr-2 font-medium">Issues</h2>
<span class="text-xs border rounded px-1 uppercase text-red-400 border-red-400">2
major</span>
<span class="text-xs border rounded px-1 uppercase text-amber-200 border-amber-200">1
medium</span>
</div>
<div class="overflow-x-auto overflow-y-clip h-max">
<table class="table-auto w-full text-white">
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<span class="mono text-blue-300">
Feature `text`
</span>
</td>
<td class="p-3">
<span>Switch Religion</span>
</td>
<td class="p-3">
<span>Fail rate = 0.227</span>
</td>
<td class="p-3">
<span class="text-red-400">
5/22 tested samples (22.73%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
22 samples affected<br>
(0.4% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
When feature “text” is perturbed with the transformation “Switch Religion”, the model changes its prediction in 22.73% of the cases. We expected the predictions not to be affected by this transformation.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>Switch Religion(text)</th>
<th>Original prediction</th>
<th>Prediction after perturbation</th>
</tr>
</thead>
<tbody>
<tr>
<th>349</th>
<td>OH MY GOD TYLER THAT CENSORING</td>
<td>OH MY allah TYLER THAT CENSORING</td>
<td>annoyance (p = 0.13)</td>
<td>disapproval (p = 0.12)</td>
</tr>
<tr>
<th>1292</th>
<td>Careful, you are almost criticizing your god in chief. Your friends at TD will probably ban you for that.</td>
<td>Careful, you are almost criticizing your allah in chief. Your friends at TD will probably ban you for that.</td>
<td>nervousness (p = 0.10)</td>
<td>disapproval (p = 0.11)</td>
</tr>
<tr>
<th>1902</th>
<td>The thing is the American hierarchs were ready to act until the Pope stepped in and told them to wait.</td>
<td>The thing is the American hierarchs were ready to act until the rabbi stepped in and told them to wait.</td>
<td>nervousness (p = 0.08)</td>
<td>annoyance (p = 0.08)</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:ethics:E0101
</span>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0201
</span>
</td>
</tr>
</tbody>
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<span class="mono text-blue-300">
Feature `text`
</span>
</td>
<td class="p-3">
<span>Switch countries from high- to low-income and vice versa</span>
</td>
<td class="p-3">
<span>Fail rate = 0.148</span>
</td>
<td class="p-3">
<span class="text-red-400">
12/81 tested samples (14.81%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
81 samples affected<br>
(1.5% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
When feature “text” is perturbed with the transformation “Switch countries from high- to low-income and vice versa”, the model changes its prediction in 14.81% of the cases. We expected the predictions not to be affected by this transformation.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>Switch countries from high- to low-income and vice versa(text)</th>
<th>Original prediction</th>
<th>Prediction after perturbation</th>
</tr>
</thead>
<tbody>
<tr>
<th>725</th>
<td>It starts with the malicious ones and then they whip up the others. [NAME] is truly a stain on american politics.</td>
<td>It starts with the malicious ones and then they whip up the others. [NAME] is truly a stain on Indonesian politics.</td>
<td>embarrassment (p = 0.15)</td>
<td>anger (p = 0.14)</td>
</tr>
<tr>
<th>1866</th>
<td>> You're not in a Greek tragedy How sure are you of this?</td>
<td>> You're not in a Micronesian tragedy How sure are you of this?</td>
<td>curiosity (p = 0.15)</td>
<td>confusion (p = 0.13)</td>
</tr>
<tr>
<th>1902</th>
<td>The thing is the American hierarchs were ready to act until the Pope stepped in and told them to wait.</td>
<td>The thing is the Ugandan hierarchs were ready to act until the Pope stepped in and told them to wait.</td>
<td>nervousness (p = 0.08)</td>
<td>annoyance (p = 0.08)</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:ethics:E0101
</span>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0201
</span>
</td>
</tr>
</tbody>
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<span class="mono text-blue-300">
Feature `text`
</span>
</td>
<td class="p-3">
<span>Switch Gender</span>
</td>
<td class="p-3">
<span>Fail rate = 0.095</span>
</td>
<td class="p-3">
<span class="text-amber-200">
78/818 tested samples (9.54%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
818 samples affected<br>
(15.1% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
When feature “text” is perturbed with the transformation “Switch Gender”, the model changes its prediction in 9.54% of the cases. We expected the predictions not to be affected by this transformation.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>Switch Gender(text)</th>
<th>Original prediction</th>
<th>Prediction after perturbation</th>
</tr>
</thead>
<tbody>
<tr>
<th>132</th>
<td>Not any weirder than male doctors handling women junk for centuries.</td>
<td>Not any weirder than female doctors handling men junk for centuries.</td>
<td>realization (p = 0.09)</td>
<td>neutral (p = 0.09)</td>
</tr>
<tr>
<th>379</th>
<td>I love how when a girl flees saudi arabia in fear of her life she gets called a drama queen. This right here is a drama queen.</td>
<td>I love how when a boy flees saudi arabia in fear of his life he gets called a drama queen. This right here is a drama queen.</td>
<td>fear (p = 0.09)</td>
<td>excitement (p = 0.09)</td>
</tr>
<tr>
<th>412</th>
<td>Yes she does! She is beautiful but has taken it much too far with the lip fillers and fake tan</td>
<td>Yes he does! he is beautiful but has taken it much too far with the lip fillers and fake tan</td>
<td>confusion (p = 0.08)</td>
<td>admiration (p = 0.09)</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:ethics:E0101
</span>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0201
</span>
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="Robustness" role="tabpanel" class="m-4 mb-4 hidden">
<div class="p-3 bg-amber-100/40 rounded-sm w-full flex align-middle">
<div class="text-amber-100 mt-1.5">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5"
stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round"
d="M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126zM12 15.75h.007v.008H12v-.008z" />
</svg>
</div>
<div class="ml-2 text-amber-100 text-sm">
<div class="prose">
<p>Your model seems to be sensitive to small perturbations in the input data. These perturbations can include adding typos,
changing word order, or turning text into uppercase or lowercase. This happens when:</p>
<ul>
<li>There is not enough diversity in the training data</li>
<li>Overreliance on spurious correlations like the presence of specific word</li>
<li>Use of complex models with large number of parameters that tend to overfit the training data</li>
</ul>
<p>To learn more about causes and solutions, check our <a href="https://docs.giskard.ai/en/latest/getting-started/key_vulnerabilities/robustness/index.html">guide on robustness issues</a>.</p>
</div>
</div>
</div>
<div class="flex items-center space-x-1">
<h2 class="uppercase my-4 mr-2 font-medium">Issues</h2>
<span class="text-xs border rounded px-1 uppercase text-red-400 border-red-400">2
major</span>
</div>
<div class="overflow-x-auto overflow-y-clip h-max">
<table class="table-auto w-full text-white">
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<span class="mono text-blue-300">
Feature `text`
</span>
</td>
<td class="p-3">
<span>Add typos</span>
</td>
<td class="p-3">
<span>Fail rate = 0.392</span>
</td>
<td class="p-3">
<span class="text-red-400">
392/1000 tested samples (39.2%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
1000 samples affected<br>
(18.4% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
When feature “text” is perturbed with the transformation “Add typos”, the model changes its prediction in 39.2% of the cases. We expected the predictions not to be affected by this transformation.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>Add typos(text)</th>
<th>Original prediction</th>
<th>Prediction after perturbation</th>
</tr>
</thead>
<tbody>
<tr>
<th>1450</th>
<td>Thanks. I needed this reminder today.</td>
<td>Tanks. I needed this reminder today.</td>
<td>gratitude (p = 0.34)</td>
<td>realization (p = 0.15)</td>
</tr>
<tr>
<th>334</th>
<td>I don't define myself by my sexuality. Conservatives who want to kill me do.</td>
<td>I don't fefine myself by my sexuality. Conservzatives wh owant to kill me do.</td>
<td>desire (p = 0.13)</td>
<td>neutral (p = 0.07)</td>
</tr>
<tr>
<th>5102</th>
<td>No, that’s my husband.</td>
<td>No, hat’s my husband.</td>
<td>realization (p = 0.08)</td>
<td>confusion (p = 0.09)</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0201
</span>
</td>
</tr>
</tbody>
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<span class="mono text-blue-300">
Feature `text`
</span>
</td>
<td class="p-3">
<span>Punctuation Removal</span>
</td>
<td class="p-3">
<span>Fail rate = 0.217</span>
</td>
<td class="p-3">
<span class="text-red-400">
217/1000 tested samples (21.7%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
1000 samples affected<br>
(18.4% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
When feature “text” is perturbed with the transformation “Punctuation Removal”, the model changes its prediction in 21.7% of the cases. We expected the predictions not to be affected by this transformation.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>Punctuation Removal(text)</th>
<th>Original prediction</th>
<th>Prediction after perturbation</th>
</tr>
</thead>
<tbody>
<tr>
<th>1914</th>
<td>So many enablers!</td>
<td>So many enablers</td>
<td>excitement (p = 0.22)</td>
<td>realization (p = 0.14)</td>
</tr>
<tr>
<th>2479</th>
<td>I'm not saying she should get surgery, but I gotta ask, would laser lipo or something similar get her to the body shape she wants?</td>
<td>I m not saying she should get surgery but I gotta ask would laser lipo or something similar get her to the body shape she wants</td>
<td>curiosity (p = 0.52)</td>
<td>desire (p = 0.20)</td>
</tr>
<tr>
<th>1724</th>
<td>no one is going to allow no deal. Anyone who thinks otherwise is deluded.</td>
<td>no one is going to allow no deal Anyone who thinks otherwise is deluded</td>
<td>confusion (p = 0.17)</td>
<td>disapproval (p = 0.15)</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0201
</span>
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="Underconfidence" role="tabpanel" class="m-4 mb-4 hidden">
<div class="p-3 bg-amber-100/40 rounded-sm w-full flex align-middle">
<div class="text-amber-100 mt-1.5">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5"
stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round"
d="M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126zM12 15.75h.007v.008H12v-.008z" />
</svg>
</div>
<div class="ml-2 text-amber-100 text-sm">
<div class="prose">
<p>We found some data slices in your dataset containing significant number of underconfident predictions. Underconfident predictions refer to situations where the predicted label has a probability that is very close to the probability of the next highest probability label. This happens when:</p>
<ul>
<li>There are not enough examples in the training set for the underconfident data slice</li>
<li>The model is too simple and struggles to capture the complexity of the underlying data</li>
<li>The underconfident data slice contains inherent noise or overlapping feature distributions</li>
</ul>
<p>To learn more about causes and solutions, check our <a href="https://docs.giskard.ai/en/latest/getting-started/key_vulnerabilities/underconfidence/index.html">guide on underconfidence issues.</a></p>
</div>
</div>
</div>
<div class="flex items-center space-x-1">
<h2 class="uppercase my-4 mr-2 font-medium">Issues</h2>
<span class="text-xs border rounded px-1 uppercase text-red-400 border-red-400">1
major</span>
</div>
<div class="overflow-x-auto overflow-y-clip h-max">
<table class="table-auto w-full text-white">
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<code class="mono text-blue-300">
`text` contains "like"
</code>
</td>
<td class="p-3">
<span>Overconfidence rate = 0.124</span>
<span class="text-gray-400">
(Global = 0.087)
</span>
</td>
<td class="p-3">
<span class="text-red-400">
+43.21% than global
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
395 samples affected<br>
(7.3% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
For records in your dataset where `text` contains "like", we found a significantly higher number of underconfident predictions (49 samples, corresponding to 12.4% of the predictions in the data slice).
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>label</th>
<th>Predicted `label`</th>
</tr>
</thead>
<tbody>
<tr>
<th>402</th>
<td>ya how dare anyone like live and breathe around you. another joy of life you must be in person</td>
<td>annoyance</td>
<td>joy (p = 0.12)<br>excitement (p = 0.12)</td>
</tr>
<tr>
<th>1588</th>
<td>That ET one looks like he has stuck his finger is some strawberry jello or something and is disgusted that some has stuck to his finger!</td>
<td>disgust</td>
<td>disgust (p = 0.21)<br>disapproval (p = 0.21)</td>
</tr>
<tr>
<th>141</th>
<td>It's almost like OP is the real CB trying to make fun of people to get them mad and then screenshot it for karma</td>
<td>neutral</td>
<td>anger (p = 0.11)<br>annoyance (p = 0.11)</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0204
</span>
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="Performance" role="tabpanel" class="m-4 mb-4 hidden">
<div class="p-3 bg-amber-100/40 rounded-sm w-full flex align-middle">
<div class="text-amber-100 mt-1.5">
<svg xmlns="http://www.w3.org/2000/svg" fill="none" viewBox="0 0 24 24" stroke-width="1.5"
stroke="currentColor" class="w-6 h-6">
<path stroke-linecap="round" stroke-linejoin="round"
d="M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126zM12 15.75h.007v.008H12v-.008z" />
</svg>
</div>
<div class="ml-2 text-amber-100 text-sm">
<div class="prose">
<p>We found some data slices in your dataset on which your model performance is lower than average. Performance bias may
happen for different reasons:</p>
<ul>
<li>Not enough examples in the low-performing data slice in the training set</li>
<li>Wrong labels in the training set in the low-performing data slice</li>
<li>Drift between your training set and test set</li>
</ul>
<p>To learn more about causes and solutions, check our <a href="https://docs.giskard.ai/en/latest/getting-started/key_vulnerabilities/performance_bias/index.html">guide on performance bias.</a></p>
</div>
</div>
</div>
<div class="flex items-center space-x-1">
<h2 class="uppercase my-4 mr-2 font-medium">Issues</h2>
<span class="text-xs border rounded px-1 uppercase text-red-400 border-red-400">1
major</span>
</div>
<div class="overflow-x-auto overflow-y-clip h-max">
<table class="table-auto w-full text-white">
<tbody class="first:border-t border-b border-zinc-500">
<tr class="gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700">
<td class="p-3">
<code class="mono text-blue-300">
`text` contains "like"
</code>
</td>
<td class="p-3">
<span>Precision = 0.192</span>
<span class="text-gray-400">
(Global = 0.243)
</span>
</td>
<td class="p-3">
<span class="text-red-400">
-20.73% than global
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
395 samples affected<br>
(7.3% of dataset)
</span>
<td class="p-3 text-xs text-right space-x-1">
<a href="#"
class="gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm">Show details</a>
<a href="#"
class="hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm">Hide details</a>
</td>
</tr>
<tr class="gsk-issue-detail text-left hidden peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700">
<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
For records in the dataset where `text` contains "like", the Precision is 20.73% lower than the global Precision.
<h4 class="font-bold text-sm mt-4">Examples</h4>
<div class="text-white text-sm overflow-scroll">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>text</th>
<th>label</th>
<th>Predicted `label`</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>Is this in New Orleans?? I really feel like this is New Orleans.</td>
<td>neutral</td>
<td>curiosity (p = 0.25)</td>
</tr>
<tr>
<th>10</th>
<td>It's better to say a moment like that could truly ignite her love for the game rather than putting a bit of a damper on it.</td>
<td>love</td>
<td>desire (p = 0.08)</td>
</tr>
<tr>
<th>13</th>
<td>Like this just cuz of the [NAME] rhymes background raps...but dude your [NAME] is sick against [NAME]</td>
<td>neutral</td>
<td>disapproval (p = 0.12)</td>
</tr>
</tbody>
</table>
</div>
</div>
<h4 class="font-bold text-sm mt-4">Significance</h4>
<p class="text-sm my-2">
The hypothesis that the <em>Precision</em> on the data slice was different with respect
to the rest of the data was asserted with p-value = None.
</p>
<h4 class="font-bold text-sm mt-4">Taxonomy</h4>
<span class="inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2">
avid-effect:performance:P0204
</span>
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="p-4">
<h2 class="my-4 mr-2 font-medium">Debug your issues in the Giskard hub</h2>
<p class="text-sm my-1">
Install the Giskard hub app to:
</p>
<ul class="list-disc ml-4 my-1 text-sm">
<li>Debug and diagnose your scan issues</li>
<li>Save your scan result as a re-executable test suite to benchmark your model</li>
<li>Extend your test suite with <a href="https://docs.giskard.ai/en/latest/reference/tests/index.html"
target="_blank" class="underline" rel="noopener">our catalog of ready-to-use tests</a></li>
</ul>
<p class="text-sm my-1">
You can find <a href="https://docs.giskard.ai/en/latest/guides/installation_app/index.html" target="_blank"
rel="noopener">installation instructions here.</a>
</p>
<div class="text-sm my-2">
<pre><code class="language-python rounded">from giskard import GiskardClient
# Create a test suite from your scan results
test_suite = results.generate_test_suite("My first test suite")
# Upload your test suite to your Giskard hub instance
client = GiskardClient("http://localhost:19000", "GISKARD_API_KEY")
client.create_project("my_project_id", "my_project_name")
test_suite.upload(client, "my_project_id")
</code></pre>
</div>
</div>
</div>
</div>
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