# Fairness Machine Learning

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## Objective Functions with Fairness Constraints

A binary classifier does not suffer from disparate impact if the probability that a classifier assigns a user to the positive class $\hat y = 1$ is the same for both values of the sensitive features $z$

$$P(\hat y = 1 | z = 0) = P(\hat y = 1 | z = 1)$$

We measure the decision boundary due to disparate impact by means of the covaraince between the users sensitive attributes $z$ and the signed distance from the users’ feasture vectors to the decision boundary $d_\theta(x)$

$$Cov_{DI}(z, d_\theta(x)) = E\bigg[ (z-\bar z)d_\theta(x) \bigg] - E\bigg[ (z-\bar z) \bigg]\bar d_\theta(x) \approx \frac{1}{N}\sum_{x,z \in \mathcal D}(z-\bar z) d_\theta(x)$$

If the decision boundary satisfies the definition of not suffering from disparate impact, we will have

P(d_\theta(x) > 0 | z = 0) = P(d_\theta(x) > 0 | z = 1)

meaning that the $\text{Cov}_{DI}$ should be approximately zero for a sufficiently large training dataset.

More specifically, we can format our objective function as follow

\begin{aligned} &\text{minimize } &&L(\theta) \\ &\text{subject to } &&\frac{1}{N}\sum_{x,z \in \mathcal D}(z-\bar z) d_\theta(x) \le c \\ & &&\frac{1}{N}\sum_{x,z \in \mathcal D}(z-\bar z) d_\theta(x) \ge - c \end{aligned}

• where $c\in \mathbb R^+$ is a given threshold which trades off accuracy and unfairness due to the disparate impact.
• it’s a convex problem and ensures that the trade-off between the classifier loss function and decision boundary covariance is Pareto Optimal

## Distance Function

a distance function $d(\vec x)$ is defined as $$d(\vec{x} - \vec{x_I}) = \min(|\vec{x} - \vec{x_I}|) \quad \text{for all } \vec{x_I} \in \partial \Omega$$

implying that $d(\vec{x}) = 0$ on the boundary where $\vec{x_I} \in \partial \Omega$

## Signed Distance Funtion}

$$|\phi(\vec x)| = \begin{cases} d(\vec x) & \vec x \in \Omega^+ \\ -d(\vec x) & \vec x \in \Omega^- \\ 0 & \vec x \in \partial \Omega \end{cases}$$

## Convex Problem in Standard Form

\begin{aligned} &\text{minimize } &&f_0(x) \\ &\text{subject to } &&f_i(x) \le 0, && i = 1,\ldots, m \\ & && a_i^Tx = b_i, && i = 1,\ldots, p \end{aligned}

• the objective function $f_0(x)$ must be convex
• the inequality constraint functions must be convex
• the equality constraint function $h_i(x) = a_i^T - b_i = 0$ must be affine

Let’s prove that when $\Omega \in R^n$ be a nonempty convex set, function given by $f(x) = d(x,\Omega)$ is a convex function.

Let first redefine the distance function

$$f(x) = d(x;\Omega) = \inf\bigg\{ ||x-y||: y \in \Omega \bigg\}$$

We will show that

$$f(\lambda x_1 + (1- \lambda)x_2) \le \lambda f(x_1) + (1-\lambda)f(x_2)$$

Equivalently,

$$d(\lambda x_1 + (1+\lambda)x_2;\Omega) \le \lambda d(x_1;\Omega) + (1-\lambda)d(x_2;\Omega)$$

By definition, for any $\varepsilon > 0$ there exist $y_1\in \Omega$ and $y_2\in \Omega$ such that

\begin{aligned} ||x_1 - y_1|| < d(x_1;\Omega) + \varepsilon \\ ||x_2 - y_2|| < d(x_2;\Omega) + \varepsilon \end{aligned}

Then

##### Hanchao Zhang
###### Ph.D. Candidate of Biostatistics President of the Chinese Students and Scholars Association (CSSA)

My research interests include Machine Learning, Functional Data Analysis, and Imaging Data.