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<!DOCTYPE html>
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  <title>On Measuring Fairness in Generative Models (NeurIPS23)</title>

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          <h1 class="title is-1 publication-title">NeurIPS23: On Measuring Fariness in Generative Models</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="-">Christopher T. H. Teo</a><sup>1</sup>,</span>
            <span class="author-block">
              <a href="-">Milad Abdollahzadeh</a><sup>1</sup>,</span>
            <span class="author-block">
              <a href="-">Ngai-Man Cheung</a><sup>1</sup>,
            </span>
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          <div class="is-size-5 publication-authors">
            <span class="author-block"><sup>1</sup>Singapore Univeristy of Technology and Design (SUTD),</span>
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            Recently, there has been increased interest in fair generative models. In this work,
            we conduct, for the first time, an in-depth study on fairness measurement, a
            critical component in gauging progress on fair generative models. We make three
            main contributions. First, we conduct a study that reveals that contrary to prior
            work’s assumption the existing fairness measurement framework has considerable
            measurement errors, even when highly accurate sensitive attribute (SA) classifiers
            are used. For example, a ResNet-18 for Gender with accuracy ≈ 97% could still
            result in an measurement error of 4.98%. This oversight raises concerns about the
            accuracy reported in previous works, where relative fairness improvement falls
            within these error margins. Second, to address this issue, we propose CLassifier
            Error-Aware Measurement (CLEAM), a new framework which uses a statisti-
            cal model to account for inaccuracies in SA classifiers. Our proposed CLEAM
            reduces measurement errors significantly, e.g., 4.98%→0.62% for StyleGAN2
            w.r.t. Gender. CLEAM achieves this with minimal additional overhead. Third,
            we utilize CLEAM to measure fairness in important text-to-image generator and
            GANs, revealing considerable biases in these models that raise concerns about
            their applications. Code and reproducibility instructions are included in Supp.
          </p>
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        <h2 class="title is-3">Overview</h2>

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			<center>
				<table align="center" width="880px">
					<tbody><tr>
						<td width="260px">
							<center>
								<img class="round" style="width:880px" src="./resources/Fig1_new_v6.png">
							</center>
						</td>
					</tr>
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				<table align="center" width="880px">
					<tbody><tr>
						<td>
							<p style="text-align:justify; text-justify:inter-ideograph;">
                </p><h4 class="title is-5">Contributions</h4>
							<b>1: </b>
              We conduct a study to reveal that even highly-accurate SA classifiers could still incur significant
              fairness measurement errors when using existing framework.
              <br> <br>
							<b>2: </b>
              To enable evaluation of fairness measurement frameworks, we propose new datasets based on
              generated samples from StyleGAN, StyleSwin and SDM, with manual labeling w.r.t. SA
              <br> <br>
              <b>3: </b>
              We propose a new and accurate fairness measurement framework, CLEAM, that accounts for SA
              classifier inaccuracies and provides point and interval estimates
              <br> <br>
              <b>4: </b>
              Using CLEAM, we reveal considerable biases in several important generative models, prompting
              careful consideration when applying them for different applications.
              <br>
          	</td>
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					<tbody><tr>
						<td width="260px">
							<!-- <center>
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    <h2 class="title is-3">GenData Dataset</h2>
    <p>
    In our work, we present a new dataset based on generated samples from State-of-the-Art Generative models: StyleGAN2,StyleSwin and Diffusion models.
    In this dataset we provide labels for each samples w.r.t. Gender and BlackHair collecting utilizing Amazon MTurk.
    More specifically, our dataset contains ≈9k randomly generated samples based on the original saved weights and codes of the respective GANs, and ≈2k
    samples for four different prompts inputted in the SDM. These samples are then hand labeled w.r.t. the
    sensitive attributes. Then with these labeled datasets, we can approximate the ground-truth sensitive attribute
    distribution, p∗, of the respective GANs.
   </p>

    <div class="content has-text-justified">
  <center>
    <!--Gender-->
    <table align="center" width="660px">
      <tbody><tr>
        <td width="260px">
          <center>
            <img class="round" style="width:330px" src="./resources/Gender0_Samples_StyleGAN2.png">
            <img class="round" style="width:330px" src="./resources/Gender1_Samples_StyleGAN2.png">
          </center>
          <h3 class="subtitle has-text-centered" style="font-size:100%";>
              Figure 1: Examples of generated samples in GenDataw.r.t Gender i.e., LHS: Female samples and RHS: Male samples.
          </h3>
        </td>
      </tr>
    </tbody></table>
    <!--Blackhair-->
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      <tbody><tr>
        <td width="260px">
          <center>
            <img class="round" style="width:330px" src="./resources/Blackhair0_Samples_StyleGAN2.png">
            <img class="round" style="width:330px" src="./resources/Blackhair1_Samples_StyleGAN2.png">
          </center>
          <h3 class="subtitle has-text-centered" style="font-size:100%";>
              Figure 2: Examples of generated samples in GenData w.r.t BlackHair i.e., LHS: No-BlackHair samples and RHS: BlackHair samples.
          </h3>
        </td>
      </tr>
    </tbody></table>
    <!--Rejected-->
    <table align="center" width="660px">
      <tbody><tr>
        <td width="260px">
          <center>
            <img class="round" style="width:330px" src="./resources/rejected_Samples_StyleGAN2.png">
            <img class="round" style="width:330px" src="./resources/rejected_Samples_StyleSwin.png">
          </center>
          <h3 class="subtitle has-text-centered" style="font-size:100%";>
              Figure 3: Examples of rejected generated samples in GenData i.e., LHS: StyleGAN2 samples and RHS: StyleSwin samples.
          </h3>
        </td>
      </tr>
    </tbody></table>

  </center>
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  </div>
</div>


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      <div class="container is-max-desktop content">
        <h2 class="title">BibTeX</h2>
        <pre><code>
@article{teo2024measuring,
  title={On measuring fairness in generative models},
  author={Teo, Christopher and Abdollahzadeh, Milad and Cheung, Ngai-Man Man},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}
}
</code></pre>
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