Create a Heatmap for Visualization

Explanation:

Develop a heatmap visualization that displays the assessed risks and their severity levels across different dimensions $($e$.$g$.,$ AI lifecycle phases, impact categories$).$

Use a color scale to represent the severity of each risk, with darker colors indicating higher risk.

This visual representation will help stakeholders understand the overall risk landscape and prioritize mitigation efforts.

expand$\_$more

$\backslash$Epsilon $\backslash$mu $\backslash$phi $$

u $\backslash$iota $\backslash$sigma $\backslash$eta $\backslash$pi $\backslash$rho $\backslash$chi $\backslash$epsi $\backslash$iota $\backslash$rho $\backslash$omega

u

expand$\_$more

Absolutely! Let's create a heatmap to visualize the AI risks across the lifecycle phases and their broader impacts. Given that we have the data already categorized, we can use Python's Seaborn library to create the heatmap.

Python

import seaborn as sns

import matplotlib.pyplot as plt

import pandas as pd

# Create the DataFrame from the table data

data $=\{$

'Risk Description': $[$

'Unwanted Outputs, Literal Memory Bias',

'Lack of Quality, Factuality, Hallucinating',

'Lack of Timeliness',

'Lack of Reproducibility and Explainability',

'Lack of Security of Generated Code',

'Incorrect Response to Specific Inputs',

'Automation Bias',

'Susceptibility to Interpreting Text as Instruction',

'Lack of Confidentiality of Input Data',

'Self$-$reinforcing Impacts and Model Collapse',

'Dependence on the Model Developer$/$Operator$\text{'},$

'Misinformation $($Fake News$)$:$\text{'},$

'Social Engineering:$\text{'},$

$\text{'}$Re$-$identification of Individuals from Anonymous Data:$\text{'},$

'Knowledge Gathering and Processing in the Context of Surveillance:$\text{'},$

'Malware Creation and Improvement:$\text{'},$

'Malware Placement:$\text{'},$

'RCE $($Remote Code Execution$)$ attacks:$\text{'},$

'Attackers can Reconstruct a Model$\backslash \text{'}$s Training Data:$\text{'},$

'Model Subversion Attacks:$\text{'},$

'Member Reasoning Attacks:$\text{'},$

'Homograph Attacks',

'Prompt Injection Attacks:$\text{'},$

'Data Poisoning Attacks:$\text{'},$

'Model Poisoning Attacks:$\text{'},$

'Learning Transfer Attacks:$\text{'},$

'Environmental Impact:$\text{'},$

'Economic Disruption:$\text{'},$

'Overreliance and Dependency:$\text{'},$

'Dual Use Concerns:$\text{'},$

'Unforeseen Consequences:$\text{'},$

$],$

'Planning': $[1,0,1,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0],$

'Data': $[1,1,1,0,0,1,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0],$

'Development': $[1,1,0,1,1,1,0,1,1,1,1,1,1,0,1,1,0,0,1,1,1,0,1,0,1,0,0,0,0,0],$

'Operation': $[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,1,0,0,0,0,0,1,0],$

$\}$

df $=$ pd$.$DataFrame$($data$)$

# Create the heatmap using Seaborn

plt$.$figure$($figsize$=(12,8))$

sns$.$set$($font$\_$scale$=0\mathrm{.}8)$ # Adjust font size as needed

sns$.$heatmap$($df$.$set$\_$index$(\text{'}$Risk Description'$).$transpose$(),$ cmap$=$"YlOrRd", annot$=$True, fmt$=\text{'}$d$\text{'},$ cbar$\_$kws$=\{\text{'}$label$\text{'}$: 'Risk Presence'$\})$

plt$.$title$(\text{'}$AI System Lifecycle Risk Assessment Heatmap'$)$

plt$.$xlabel$(\text{'}$Lifecycle Phase'$)$

plt$.$ylabel$(\text{'}$Impact Category'$)$

plt$.$xticks$($rotation$=45)$

plt$.$show$()$

CREATE THE HEATMAP LIKE AS PHOTO