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• Trustworthy Machine Learning by Kush R. Varshney
• Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms
• Obtaining Fairness using Optimal Transport Theory
• On the Compatibility of Privacy and Fairness
• On the Tradeoffs Between Privacy and Fairness
• Fairness Through Awareness
• Automating Fairness Configurations for Machine Learning
• The Distributive Effects of Risk Prediction in Environmental Compliance: Algorithmic Design, Environmental Justice, and Public Policy
• [Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information](https://arxiv.org/abs/2102.0

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import math, sys 
import numpy as np

def Euler(f, y0, T, n):
    """Solve y’= f(y,t), y(0)=y0, with n steps until t=T."""
    t = np.zeros(n+1)
    y = np.zeros(n+1) # y[k] is the solution at time t[k]
    y[0] = y0
    t[0] = 0
    dt = T/float(n)

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#Forward Euler 
import math, sys 
import numpy as np

def ForwardEuler(f, y0, T, n):
    """Solve y’= f(y,t), y(0)=y0, with n steps until t=T."""
    t = np.zeros(n+1)
    y = np.zeros(n+1) # y[k] is the solution at time t[k]
    y[0] = y0
    t[0] = 0

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#Heun Method for solutions approach
import math, sys 
import numpy as np

def Heun(f, y0, T, n):
    """Solve y'=f(t,y), y(0)=y0, with n steps until t=T."""
    t = np.zeros(n+1)
    y = np.zeros(n+1)  # y[k] is the solution at time t[k]
    dt = T/float(n)
    y[0] = y0

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#Midpoint Method for solutions approach
import math, sys 
import numpy as np

def Midpoint(f, y0, T, n):
    """Solve y'=f(t,y), y(0)=y0, with n steps until t=T."""
    dt = T/float(n)
    t = np.zeros(n+1)
    y = np.zeros(n+1)  # y[k] is the solution at time t[k]
    y[0] = y0