"""
Tool to visualize AI safety risks and mitigation strategies.
"""
import pandas as pd
import plotly.express as px
from plotly.graph_objs import Figure
from typing import Tuple
ai_safety_risks = [
{
"Risk": "Disinformation Spread",
"Category": "Information Integrity",
"Percentage": 20,
"Severity": 8,
"Likelihood": 7,
"Impact": "High",
"Description": "AI-generated content can spread false information rapidly.",
"Mitigation": "Develop AI tools for fact-checking and verification."
},
{
"Risk": "Algorithmic Bias",
"Category": "Fairness and Bias",
"Percentage": 18,
"Severity": 7,
"Likelihood": 8,
"Impact": "High",
"Description": "AI systems may perpetuate or amplify societal biases.",
"Mitigation": "Implement fairness-aware algorithms and diverse datasets."
},
{
"Risk": "Privacy Invasion",
"Category": "Data Privacy",
"Percentage": 15,
"Severity": 6,
"Likelihood": 6,
"Impact": "Medium",
"Description": "AI can infer personal information without consent.",
"Mitigation": "Adopt privacy-preserving techniques like differential privacy."
},
{
"Risk": "Lack of Transparency",
"Category": "Explainability",
"Percentage": 12,
"Severity": 5,
"Likelihood": 5,
"Impact": "Medium",
"Description": "Complex models can be opaque, making decisions hard to understand.",
"Mitigation": "Use explainable AI methods to increase transparency."
},
{
"Risk": "Security Vulnerabilities",
"Category": "Robustness",
"Percentage": 10,
"Severity": 6,
"Likelihood": 5,
"Impact": "Medium",
"Description": "AI systems may be susceptible to adversarial attacks.",
"Mitigation": "Employ robust training methods and continuous monitoring."
},
{
"Risk": "Job Displacement",
"Category": "Economic Impact",
"Percentage": 8,
"Severity": 7,
"Likelihood": 6,
"Impact": "High",
"Description": "Automation may lead to loss of jobs in certain sectors.",
"Mitigation": "Promote reskilling and education programs."
},
{
"Risk": "Ethical Dilemmas",
"Category": "Ethics",
"Percentage": 7,
"Severity": 5,
"Likelihood": 4,
"Impact": "Medium",
"Description": "AI may make decisions conflicting with human values.",
"Mitigation": "Incorporate ethical guidelines into AI development."
},
{
"Risk": "Autonomous Weapons",
"Category": "Physical Safety",
"Percentage": 5,
"Severity": 9,
"Likelihood": 3,
"Impact": "Critical",
"Description": "AI could be used in weapons without human oversight.",
"Mitigation": "Establish international regulations and oversight."
},
{
"Risk": "Environmental Impact",
"Category": "Sustainability",
"Percentage": 3,
"Severity": 4,
"Likelihood": 5,
"Impact": "Low",
"Description": "High energy consumption in AI training affects the environment.",
"Mitigation": "Optimize models and use renewable energy sources."
},
{
"Risk": "Misuse for Surveillance",
"Category": "Human Rights",
"Percentage": 2,
"Severity": 8,
"Likelihood": 2,
"Impact": "High",
"Description": "AI can be used for mass surveillance violating privacy rights.",
"Mitigation": "Enforce laws protecting individual privacy."
},
]
[docs]
def display_ai_safety_dashboard() -> Tuple[Figure, Figure, Figure, pd.DataFrame]:
"""
Creates visualizations for AI safety risks.
Returns
-------
Tuple[Figure, Figure, Figure, pd.DataFrame]
A tuple containing the bar chart, pie chart, scatter plot, and the DataFrame of AI safety risks.
Example
-------
>>> fig_bar, fig_pie, fig_scatter, df = display_ai_safety_dashboard()
"""
df = pd.DataFrame(ai_safety_risks)
# Bar Chart
fig_bar = px.bar(
df,
x='Risk',
y='Percentage',
color='Category',
text='Percentage',
title='Percentage Distribution of AI Safety Risks',
labels={'Percentage': 'Percentage (%)'},
hover_data=['Description', 'Mitigation']
)
fig_bar.update_layout(
xaxis_tickangle=-45,
template='plotly_white',
height=500,
legend_title_text='Risk Category'
)
fig_bar.update_traces(texttemplate='%{text}%', textposition='outside')
# Pie Chart
category_counts = df['Category'].value_counts().reset_index()
category_counts.columns = ['Category', 'Count']
fig_pie = px.pie(
category_counts,
names='Category',
values='Count',
title='Proportion of Risk Categories',
hole=0.4,
color_discrete_sequence=px.colors.qualitative.Pastel
)
# Scatter Plot
fig_scatter = px.scatter(
df,
x='Likelihood',
y='Severity',
size='Percentage',
color='Impact',
hover_name='Risk',
title='Severity vs. Likelihood of AI Risks',
labels={'Severity': 'Severity (1-10)', 'Likelihood': 'Likelihood (1-10)'},
size_max=20
)
fig_scatter.update_layout(template='plotly_white', height=500)
return fig_bar, fig_pie, fig_scatter, df