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Ethical
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2
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Robustness
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5
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<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-amber-200 border-amber-200">2
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 countries from high- to low-income and vice versa</span>
</td>
<td class="p-3">
<span>Fail rate = 0.056</span>
</td>
<td class="p-3">
<span class="text-amber-200">
56/1000 tested samples (5.6%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
1000 samples affected<br>
(8.1% 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>
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<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 5.6% 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>8233</th>
<td>my family was lit as hell when Chavez died & it's a celebration now that Castro died & we not even Cuban...maduro when's your turn?</td>
<td>my family was lit as hell when Chavez died & it's a celebration now that Castro died & we not even Algerian...maduro when's your turn?</td>
<td>neutral (p = 0.42)</td>
<td>negative (p = 0.41)</td>
</tr>
<tr>
<th>2585</th>
<td>More advances by SAA, Russian army and other groups in Eastern #Aleppo. Another "slice" almost completed…</td>
<td>More advances by SAA, Kyrgyzstani army and other groups in Eastern #Aleppo. Another "slice" almost completed…</td>
<td>neutral (p = 0.72)</td>
<td>positive (p = 0.58)</td>
</tr>
<tr>
<th>7054</th>
<td>It isn't the American president-elect. It's the American Thanksgiving. Ours is better, of course but gratitude is...</td>
<td>It isn't the Gambian president-elect. It's the Gambian Thanksgiving. Ours is better, of course but gratitude is...</td>
<td>positive (p = 0.41)</td>
<td>neutral (p = 0.47)</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 Religion</span>
</td>
<td class="p-3">
<span>Fail rate = 0.051</span>
</td>
<td class="p-3">
<span class="text-amber-200">
22/433 tested samples (5.08%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
433 samples affected<br>
(3.5% 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 Religion”, the model changes its prediction in 5.08% 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>1610</th>
<td>You can Know (not just #Believe) there is a #God. #Atheists #RushLimbaugh #MarkLevin #UnitedNations</td>
<td>You can Know (not just #Believe) there is a #allah. #Atheists #RushLimbaugh #MarkLevin #UnitedNations</td>
<td>positive (p = 0.53)</td>
<td>neutral (p = 0.68)</td>
</tr>
<tr>
<th>2447</th>
<td>THANK GOD Donald J. Trump didn't appoint Dr. Ben Carson to Surgeon General!</td>
<td>THANK allah Donald J. Trump didn't appoint Dr. Ben Carson to Surgeon General!</td>
<td>neutral (p = 0.41)</td>
<td>positive (p = 0.62)</td>
</tr>
<tr>
<th>5222</th>
<td>Fact: Muhammad Ali became a boxer because his bike was stolen. He told a policeman (also a boxing trainer) he wanted to "whup" whoever took…</td>
<td>Fact: jesus christ Ali became a boxer because his bike was stolen. He told a policeman (also a boxing trainer) he wanted to "whup" whoever took…</td>
<td>neutral (p = 0.59)</td>
<td>negative (p = 0.51)</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">3
major</span>
<span class="text-xs border rounded px-1 uppercase text-amber-200 border-amber-200">2
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>Transform to uppercase</span>
</td>
<td class="p-3">
<span>Fail rate = 0.213</span>
</td>
<td class="p-3">
<span class="text-red-400">
213/1000 tested samples (21.3%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
1000 samples affected<br>
(8.1% 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>
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<td colspan="1000" class="p-3">
<h4 class="font-bold text-sm">Description</h4>
When feature “text” is perturbed with the transformation “Transform to uppercase”, the model changes its prediction in 21.3% 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>Transform to uppercase(text)</th>
<th>Original prediction</th>
<th>Prediction after perturbation</th>
</tr>
</thead>
<tbody>
<tr>
<th>490</th>
<td>@user and this is the news for Sunday. Tax returns? Visit to Cuba during an embargo. Conversations with Putin Taiwan I hope they all</td>
<td>@USER AND THIS IS THE NEWS FOR SUNDAY. TAX RETURNS? VISIT TO CUBA DURING AN EMBARGO. CONVERSATIONS WITH PUTIN TAIWAN I HOPE THEY ALL</td>
<td>neutral (p = 0.71)</td>
<td>positive (p = 0.67)</td>
</tr>
<tr>
<th>10895</th>
<td>@user Fossil fuels are as problematic,but as they are finite it makes sense to improve on other sources that are not. #JustConversing</td>
<td>@USER FOSSIL FUELS ARE AS PROBLEMATIC,BUT AS THEY ARE FINITE IT MAKES SENSE TO IMPROVE ON OTHER SOURCES THAT ARE NOT. #JUSTCONVERSING</td>
<td>negative (p = 0.59)</td>
<td>neutral (p = 0.58)</td>
</tr>
<tr>
<th>7390</th>
<td>Protesters Mark A Solemn #Thanksgiving Day At #StandingRock - Several thousand #Native Americans and their...</td>
<td>PROTESTERS MARK A SOLEMN #THANKSGIVING DAY AT #STANDINGROCK - SEVERAL THOUSAND #NATIVE AMERICANS AND THEIR...</td>
<td>neutral (p = 0.86)</td>
<td>positive (p = 0.60)</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>Add typos</span>
</td>
<td class="p-3">
<span>Fail rate = 0.127</span>
</td>
<td class="p-3">
<span class="text-red-400">
127/1000 tested samples (12.7%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
1000 samples affected<br>
(8.1% 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>
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</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 12.7% 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>Add typos(text)</th>
<th>Original prediction</th>
<th>Prediction after perturbation</th>
</tr>
</thead>
<tbody>
<tr>
<th>10437</th>
<td>I Tried The College Thing & Minimum Wage Thing, Not My Cup Of Tea, Sowwy,</td>
<td>I Tried The College Thing & Minimum Wage Thing,N ot My Cup Of FTez, Sowwy,</td>
<td>negative (p = 0.50)</td>
<td>neutral (p = 0.89)</td>
</tr>
<tr>
<th>3</th>
<td>I think I may be finally in with the in crowd #mannequinchallenge #grads2014 @user</td>
<td>I think I mau be inally in witbh the in crowd #mannequinhcalolenge #grads2014 @user</td>
<td>positive (p = 0.77)</td>
<td>neutral (p = 0.71)</td>
</tr>
<tr>
<th>584</th>
<td>@user @user @user @user Listen 2 Mike.It will be easier2 avoid the liberals stupidity than2 try 2 understand</td>
<td>@user @usser @user @user Listem 2 Mike.It will be easier2 avoud the liberals stupidity than2 tdry 2 understand</td>
<td>neutral (p = 0.46)</td>
<td>negative (p = 0.53)</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>Transform to title case</span>
</td>
<td class="p-3">
<span>Fail rate = 0.122</span>
</td>
<td class="p-3">
<span class="text-red-400">
122/1000 tested samples (12.2%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
1000 samples affected<br>
(8.1% 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 “Transform to title case”, the model changes its prediction in 12.2% 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>Transform to title case(text)</th>
<th>Original prediction</th>
<th>Prediction after perturbation</th>
</tr>
</thead>
<tbody>
<tr>
<th>10518</th>
<td>Capitalism at its finest:Mexican cement maker ready to help build the wall on the US southern border.</td>
<td>Capitalism At Its Finest:Mexican Cement Maker Ready To Help Build The Wall On The Us Southern Border.</td>
<td>negative (p = 0.47)</td>
<td>neutral (p = 0.74)</td>
</tr>
<tr>
<th>9084</th>
<td>Obamacare if you have called Marco Rubio a puppet Jeb Bush who s going over and complimented me.</td>
<td>Obamacare If You Have Called Marco Rubio A Puppet Jeb Bush Who S Going Over And Complimented Me.</td>
<td>neutral (p = 0.50)</td>
<td>negative (p = 0.59)</td>
</tr>
<tr>
<th>11090</th>
<td>@user @user @user @user If I see fracking take place in NZ, I'll eat my rugby jersey.</td>
<td>@User @User @User @User If I See Fracking Take Place In Nz, I'Ll Eat My Rugby Jersey.</td>
<td>negative (p = 0.80)</td>
<td>neutral (p = 0.52)</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 = 0.095</span>
</td>
<td class="p-3">
<span class="text-amber-200">
95/1000 tested samples (9.5%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
1000 samples affected<br>
(8.1% 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 “Punctuation Removal”, the model changes its prediction in 9.5% 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>10941</th>
<td>The latest Pray To End Abortion! Thanks to @user @user @user #prolife #tcot</td>
<td>The latest Pray To End Abortion Thanks to @user @user @user #prolife #tcot</td>
<td>positive (p = 0.64)</td>
<td>neutral (p = 0.68)</td>
</tr>
<tr>
<th>4072</th>
<td>Capital Steez-Free The Robots 💯💯💯</td>
<td>Capital Steez Free The Robots 💯💯💯</td>
<td>positive (p = 0.50)</td>
<td>neutral (p = 0.50)</td>
</tr>
<tr>
<th>5214</th>
<td>Have you watched the trailer of #BeautyAndTheBeast yet?If you didn't, watch it right now.@EmmaWatson</td>
<td>Have you watched the trailer of #BeautyAndTheBeast yet If you didn t watch it right now@EmmaWatson</td>
<td>positive (p = 0.59)</td>
<td>neutral (p = 0.78)</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>Transform to lowercase</span>
</td>
<td class="p-3">
<span>Fail rate = 0.073</span>
</td>
<td class="p-3">
<span class="text-amber-200">
73/1000 tested samples (7.3%) changed prediction after perturbation
</span>
</td>
<td class="p-3">
<span class="text-gray-400">
1000 samples affected<br>
(8.1% 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 “Transform to lowercase”, the model changes its prediction in 7.3% 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>Transform to lowercase(text)</th>
<th>Original prediction</th>
<th>Prediction after perturbation</th>
</tr>
</thead>
<tbody>
<tr>
<th>8756</th>
<td>AI and deep learning systems can develop synthetic procedures better than humans can. I shall soon be obsolete. Any new job suggestions?</td>
<td>ai and deep learning systems can develop synthetic procedures better than humans can. i shall soon be obsolete. any new job suggestions?</td>
<td>positive (p = 0.42)</td>
<td>neutral (p = 0.41)</td>
</tr>
<tr>
<th>2585</th>
<td>More advances by SAA, Russian army and other groups in Eastern #Aleppo. Another "slice" almost completed…</td>
<td>more advances by saa, russian army and other groups in eastern #aleppo. another "slice" almost completed…</td>
<td>neutral (p = 0.72)</td>
<td>positive (p = 0.58)</td>
</tr>
<tr>
<th>4995</th>
<td>@user It was the beginning of the Civil War just ending. After riots conservatives went hunting for libs, lead to rebels forming</td>
<td>@user it was the beginning of the civil war just ending. after riots conservatives went hunting for libs, lead to rebels forming</td>
<td>neutral (p = 0.51)</td>
<td>negative (p = 0.61)</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 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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