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<!doctype html>
<html>
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no">
<title>Transparency in Algorithmic Fairness</title>
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<div class="reveal"><div class="slides">
<section data-background-color="#000">
<h1><span class="highlight">Transparency in Algorithmic Fairness</span></h1>
<p><font size="10">Novi Quadrianto</font></p>
<p>Reader in Machine Learning</p>
<p>Department of Informatics, University of Sussex</p>
<img src="images/sussex_logo.png" style="width: 12%; border: none;"/>
</section>
<section data-background-color="#000" data-markdown><textarea data-template>
## Talk outline
- Importance and challenges of algorithmic fairness
- Algorithmic fairness <span class="highlight">with transparency at its heart</span>
- in-processing approach
- pre-processing approach
- Future directions
</textarea></section>
<section data-background-color="#000">
<h2>Machine learning systems</h2>
<p align="left">Machine learning systems are being implemented in all walks of life</p>
<table>
<tbody>
<tr>
<td style="border: none;" width="30%"> <img src="images/scs.jpg" width=65% title="Social Credit System" style="margin:-20px 0px"/> <p style="font-size:10px">Picture credit: Kevin Hong</p></td>
<td style="border: none;" width="30%"> <img src="images/algowatch.jpg" width=65% title="Automating Society" style="margin:-20px 0px"/><p style="font-size:10px">Picture credit: AlgorithmWatch</p></td>
<td style="border: none;" width="30%"> <img src="images/CDEI.png" width=65% title="Automating Society" style="margin:-20px 0px"/><p style="font-size:10px">Picture credit: Centre for Data Ethics and Innovation, UK</p></td>
</tr>
<tr>
<td style="border: none;" width="30%">Social credit system, China</td>
<td style="border: none;" width="30%">Personal budget calculation, UK</td>
<td style="border: none;" width="30%">Financial services, Crime and justice, </td>
</tr>
<tr>
<td style="border: none;" width="30%"></td>
<td style="border: none;" width="30%">Loan decision, Finland</td>
<td style="border: none;" width="30%">Recruitment,</td>
</tr>
<tr>
<td style="border: none;" width="30%"></td>
<td style="border: none;" width="30%">etc.</td>
<td style="border: none;" width="30%">Local government</td>
</tr>
</tbody>
</table>
</section>
<section data-markdown data-background-color="#000"><textarea data-template>
## Fairness in machine learning
- A fair machine learning system takes biased datasets and outputs non-discriminatory decisions to people with differing <span class="highlight">protected attributes</span> such as race, gender, and age
- For example, ensuring classifier to be equally accurate on male and female populations
<img width=90% src="images/fairML.png" title="Fair ML"/>
</textarea></section>
<section>
<p> </p>
<p> </p>
<h1>Algorithmic fairness definitions</h1>
</section>
<section data-markdown data-background-color="#000"><textarea data-template>
## Fairness definitions
- Discrimination in the law:
- <span class="highlight">Direct discrimination</span> with respect to intent
- <span class="highlight">Indirect discrimination</span> with respect to consequences
<span class="citeme">Article 21, EU Charter of Fundamental Rights</span>
- From the legal context to algorithmic fairness, e.g.:
- Removing direct discrimination by <span class="highlight">not using group information at test time</span>
- Removing indirect discrimination by enforcing <span class="highlight">equality on the outcomes</span> between groups
</textarea></section>
<section data-markdown data-background-color="#000"><textarea data-template>
## Equality on the outcomes
When protected attribute $s$, target label $y$, predicted label $\widehat{y}$ is <span class="highlight">binary</span>:
<div class="container">
<div class="col">
<p align="left">• Equality of acceptance rate (<span class="highlight">AR</span>): $\text{Pr}(\widehat{y}=1|s=1)=\text{Pr}(\widehat{y}=1|s=0)$</p>
</div>
<div class="col">
<center>
<img src="images/AR.png" width=70% title="AR" />
</center></div>
</div>
<p align="left">• Equality of true positive rate (<span class="highlight">TPR</span>):
$
\text{Pr}(\widehat{y}=1|s=1,y=1)=\text{Pr}(\widehat{y}=1|s=0,y=1)
$</p>
<p align="left">• Equality of positive predicted value (<span class="highlight">PPV</span>):
$
\text{Pr}(y=1|s=1,\widehat{y}=1)=\text{Pr}(y=1|s=0,\widehat{y}=1)
$</p>
<p align="left">• If we are fair with regards to <span class="highlight">all notions of fair</span>, then we're fair... right?</p>
</textarea></section>
<section data-markdown data-background-color="#000"><textarea data-template>
## Mutual exclusivity by Bayes' rule
<p align="left">• Suppose we have <span class="highlight">equality of TPR/FPR</span> (FPR<sub>s=1</sub> = FPR<sub>s=0</sub> and TPR<sub>s=1</sub> = TPR<sub>s=0</sub>), can we have <span class="highlight">equality of PPV</span> (PPV<sub>s=1</sub> = PPV<sub>s=0</sub>)? <b><span class="highlight">NO!</b></p>
<p align="left">• For each protected attribute $s=0$ and $s=1$, we compute:</p>
$
\underbrace{\text{Pr}(y=1|\widehat{y}=1)}_{\text{Positive Predicted Value (PPV)}} = \frac{\overbrace{\text{Pr}(\widehat{y}=1|y=1)}^{\text{True Positive Rate (TPR)}}\times\overbrace{\text{Pr}(y=1)}^{\text{Base Rate (BR)}}}{\text{Pr}(\widehat{y}=1|y=1)\text{Pr}(y=1) + \underbrace{\text{Pr}(\widehat{y}=1|y=-1)}_{\text{False Positive Rate (FPR)}}(1-\text{Pr}(y=1))}
$
<p align="left">• <span class="highlight">EXCEPTION</span>: if we have a <span class="highlight">balanced dataset</span> (i.e. BR<sub>s=1</sub> = BR<sub>s=0</sub>) or a <span class="highlight">perfect predictor</span> (i.e. FPR=0 and TPR=1 for s=1 and s=0)</p>
<span class="citeme">Kehrenberg, Chen, NQ: Tuning fairness by balancing target labels, Frontiers in Artificial Intelligence, Minor Revision.</span>
</textarea></section>
<section data-markdown data-background-color="#000"><textarea data-template>
## Which fairness notion to use?
- There's no right answer, all the above are "fair"
- Fairness is fundamentally <span class="highlight">not a challenge of algorithms alone</span>, but very much a sociological challenge
- It's important to consult <span class="highlight">domain experts</span> to find which is the best fit for each problem
- Importantly, if we want to encourage <span class="highlight">public conversations</span>, <span class="highlight">transparency</span> in how fairness is met is an integral ingredient
</textarea></section>
<section>
<p> </p>
<p> </p>
<h1>Algorithmic fairness methods with <span class="highlight">transparency</span></h1>
</section>
<section data-markdown data-background-color="#000"><textarea data-template>
## How to enforce fairness?
<span class="highlight">Three</span> different ways to enforce fairness:
<img src="images/fairmethods.png" width=75% title="Fair Methods"/>
</textarea></section>
<section data-markdown data-background-color="#000"><textarea data-template>
## In-processing
- Specify fairness metrics as constraints on learning
- Optimise for accuracy under those constraints
- No free lunch: additional constraints lower accuracy
<img src="images/pareto.png" width=33% style="margin:0px 0px"/>
<span class="citeme">NQ and Sharmanska, Recycling privileged learning and distribution matching for fairness, NIPS 2017</span>
</textarea></section>
<section data-markdown data-background-color="#000"><textarea data-template>
## Challenges?
- Fairness-accuracy trade off is controlled by some intermediate, unintuitive control parameters such as $\tau=5$ and $\mu=1.2$.
<center>
<img src="images/pareto_control_.png" width=33% style="margin:0px 0px"/>
</center>
</textarea></section>
<section data-markdown data-background-color="#000"><textarea data-template>
## Intuitive control parameters
- Control fairness-accuracy trade off with <span class="highlight">the rate of positive predictions</span> (e.g. $PR=0.6$ for $s=0$ and $s=1$)
- Introduce new <span class="highlight">target labels</span> $\widehat{y}$ such that the <span class="highlight">dataset is balanced</span>
- Need to connect target labels $\widehat{y}$ and dataset label $y$. Let $\eta_s(x)=P(y=1|x, s)$ denote the dataset conditional probability, and accordingly for $\widehat{\eta}_s(x)$, we have the following <span class="highlight">linear relationship</span>:<br>
$\widehat{\eta}_s(x)=m_s\cdot \eta_s(x) + b_s$ with $m_s := P(\widehat{y}=1|y=1,s) + P(\widehat{y}=0|y=0,s) - 1$, and $
b_s := 1 - P(\widehat{y}=0|y=0,s) $
<span class="citeme">Kehrenberg, Chen, NQ, Tuning fairness by balancing target labels, Frontiers in Artificial Intelligence, Minor revision.</span>
</textarea></section>
<section data-markdown data-background-color="#000"><textarea data-template>
## Accuracy–Fairness trade-off
- Ensure balanced dataset condition by <span class="highlight">controlling the positive predictions</span> $P(\widehat{y}=1|s)$ (e.g. $PR=0.6$ for $s=0$ and $s=1$) in the transition probabilities $P(\widehat{y}|y,s)$
- <span class="highlight">Theorem</span>: The probability that $y$ and $\widehat{y}$ disagree ($y\neq\widehat{y}$) for any input $x$ in the dataset is given by:
$P(y\neq\widehat{y}|s)=P\left(\left|\eta_s(x) - \tfrac{1}{2}\right| < t_s\right) \leq Ct_s^\lambda$
with $t_s = \left|\frac{m_s+2b_s-1}{2m_s}\right|$
- The term $t_s$ determines the accuracy penalty that we have to accept in order to gain fairness
<span class="citeme">Kehrenberg, Chen, NQ, Tuning fairness by balancing target labels, Frontiers in Artificial Intelligence, Minor revision.</span>
</textarea></section>
<section data-markdown data-background-color="#000"><textarea data-template>
## Transparency in fairness
- <span class="highlight">Left</span>: Accuracy–Fairness trade-off; <span class="highlight">Right</span>: the achieved TPR
- By setting different target PR for our method, we can affect TPR as well, where <span class="highlight">higher $PR_t$ leads to higher $TPR$</span>
<img src="images/eo_together_acc_.png" width=30% />
<img src="images/eo_together_control_.png" width=30% />
<img src="images/eo_together_legend_.png" width=30% />
</textarea></section>
<section data-markdown data-background-color="#000"><textarea data-template>
## Challenges?
- Consider that we have 2 roles, a <span class="highlight">data vendor</span>, who is charge of collecting the data and preparing it
- Our other role is a <span class="highlight">data user</span>, someone who will be making predictions based on our data
- The data vendor is concerned that the data user may be using their data to make unfair decisions.
- So the data vendor decides to rely on <span class="highlight">pre-processing methods</span>
</textarea></section>
<section data-markdown data-background-color="#000"><textarea data-template>
## Pre-processing: Contrastive examples
- The <span class="highlight"> balanced dataset</span> contains an imaginary data point for each person, i.e. the one inside the black box, whereby we <span class="highlight">intervene</span> and set the gender attribute to the opposite that is in real life
<img src="images/balanced_Page_1.png" width=50% title="Balanced1"/>
<img src="images/balanced_Page_2.png" width=30% title="Balanced2"/>
<span class="citeme">NQ, Sharmanska, Thomas, Discovering fair representations in the data domain, CVPR 2019</span>
<span class="citeme">Sharmanska, Hendricks, Darrell, NQ, Contrastive examples for addressing the tyranny of the majority, under review</span>
</textarea></section>
<section data-markdown data-background-color="#000"><textarea data-template>
## Transparency in fairness
- All previous work with adversarial learning try to <span class="highlight">remove</span> protected attributes from data
- Instead, we use adversarial learning to <span class="highlight">generate</span> data points with pre-specified protected attributes (contrastive examples)
- Contrastive examples "can be easily interpreted" <!--because they do have the semantic meaning of the input-->
<center>
<table style="border-collapse: collapse; border: none;">
<tbody ><tr style="border: none;"><td width="40%" colspan="4" style="border: none;"></td><td width="1%" style="border: none;"></td><td width="40%" style="border: none;"></td></tr>
<tr style="border: none;">
<td width="10%" style="border: none;"></td>
<td width="30%" style="border: none;">Real</td>
<td width="30%" style="border: none;">GAN contrastive</td>
<td width="30%" style="border: none;">NN contrastive</td>
</tr>
<tr style="border: none;">
<td width="10%" style="border: none;">Male</td>
<td width="30%" style="border: none;"><img src="images/008526_m_real.jpg" width=60% title="RIm1"/></td>
<td width="30%" style="border: none;"><img src="images/008526_m_fake.jpg" width=60% title="RIm1"/></td>
<td width="30%" style="border: none;"><img src="images/008526_m_match.jpg" width=60% title="RIm1"/></td>
</tr></tbody>
</table></center>
</textarea></section>
<section data-background-color="#000">
<h2>Results on the Diversity in Faces dataset</h2>
<p align="left">• We use <span class="highlight">skin colour (light/dark)</span> as the protected attribute.</p>
<p align="left">• The classification task is to predict <span class="highlight">seven age groups</span>: [0-3],[4-12],
[13-19],[20-30],[31-45],[46-60],[61+]. </p>
<small>
<table>
<thead><tr>
<td>method</td>
<td>Acc.</td>
<td><span class="highlight">TPR Diff.</span></td>
<td><span class="highlight">FPR Diff.</span></td>
</tr>
</thead>
<tbody>
<tr>
<td>logistic regression (original)</td>
<td width="10%">69.79</td>
<td width="10%">1.55</td>
<td width="10%">0.76</td>
</tr>
<tr>
<td>logistic regression (original and GAN contrastive)</td>
<td width="10%">69.81</td>
<td width="10%">1.3</td>
<td width="10%">0.69</td>
</tr>
<tr>
<td>logistic regression (original and NN contrastive)</td>
<td width="10%">69.72</td>
<td width="10%">1.43</td>
<td width="10%">0.63</td>
</tr>
<tr>
<td>$\ddagger$logistic regression (original and GAN contrastive with output consistency)</td>
<td width="10%"><span class="highlight">72.14</span></td>
<td width="10%"><span class="highlight">0.89</span></td>
<td width="10%"><span class="highlight">0.54</span></td>
</tr>
</tbody>
</table>
</small><br>
<p align="left">$\ddagger$: <span class="highlight">Rejection learning</span> -- classifier only makes a prediction if there is an agreement between original and contrastive examples (occurs in $364,766$ out of $412,556$ test examples, i.e. $88.42\%$).</p>
</section>
<section data-markdown data-background-color="#fff"><textarea data-template>
## Future directions
- <b>BayesianGDPR</b>: Bayesian models and algorithms for fairness and transparency (<span class="highlight">ERC Starting Grant</span> -- 1 April 2020 - 31 March 2025)
<p align="center">
<img src="images/bayesiangdpr.png" width=80% title="BayesianGDPR" />
</p>
</textarea></section>
<section data-markdown data-background-color="#fff"><textarea data-template>
## Future directions
- <b>Inclusive Green Infrastructures for Urban Well-Being</b> (<span class="highlight">British Academy</span> -- 18 November 2019 - 17 November 2021)
<center>
<img src="images/greeninfrastructure.png" width=100% title="Green Infrastructures" style="margin:0px 0px"/>
</center>
</textarea></section>
<section data-markdown data-background-color="#fff"><textarea data-template>
## Future directions
- <b>INTERVENE</b>: Causal machine learning for interventional modelling (<span class="highlight">Surgo Foundation</span>)
<p align="center">
<center>
<img src="images/causal.jpg" width=100% title="INTERVENE" style="margin:0px 0px" />
</center>
</textarea></section>
<section data-background-color="#000" data-markdown><textarea data-template>
## <span class="highlight">Thank you for your attention</span>
<p align="left">Also thanks to</p>
<div class="container">
<div class="col">
<p align="left">• My team at the Univ. of Sussex</p>
</div>
<div class="col">
<center>
<img src="images/Team.png" width=70% title="ThankstoPAL" style="margin:0px 0px" />
</center></div>
</div>
<div class="container">
<div class="col">
<p align="left">• The funders</p>
</div>
<div class="col">
<center>
<img src="images/Funders.png" width=70% title="ThankstoFunders" style="margin:0px 0px" />
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</textarea></section>
<section data-background-color="#000" data-markdown><textarea data-template>
## Extra: Constrained optimization
$
\underset{w\in\Lambda}{\text{minimise}}\quad \frac{1}{N}\sum_{n=1}^{N}\Delta(y^n,f(x^n;w))+\tau||w||^2 + \mu(\text{Unfairness}(w))
$
- For example, the term $\text{Unfairness}(w)$ can be a distribution matching criterion that ensures the distributions $\text{Pr}(\text{sign}(f(x;w))==y|s=0,y=1)$ and $\text{Pr}(\text{sign}(f(x;w))==y|s=1,y=1)$ to be close, this is to satisfy the <span class="highlight">equality of true positive rate criterion</span>
- Instead of setting exogenously parameters $\tau,\mu$ to balance the multiple chunks of the objective, we can use <span class="highlight">multi-objective optimisation approaches</span> to return a set of models
</textarea></section>
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