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<ArticleSet>
  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Journal of Historical Research, Law and Policy</JournalTitle>
      <Issn>3115-7505</Issn>
      <Volume>2</Volume>
      <Issue>Serial Number 6</Issue>
      <PubDate PubStatus="epublish">
        <Year>2024</Year>
        <Month>04</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Indicators of Human Rights Risks in Automated Decision-Making Systems</ArticleTitle>
    <VernacularTitle>Indicators of Human Rights Risks in Automated Decision-Making Systems</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>8</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2024</Year>
        <Month>02</Month>
        <Day>12</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;This study aims to identify key indicators of human rights risks inherent in automated decision-making systems to inform governance frameworks and safeguard fundamental rights. A qualitative research design was employed, involving semi-structured interviews with 23 participants from Tehran who possess expertise or experience related to automated decision-making and human rights. Sampling continued until theoretical saturation was reached. Data were transcribed verbatim and analyzed thematically using NVivo software to extract main themes, subthemes, and concepts regarding human rights risk indicators. Three primary themes emerged: (1) Legal and Regulatory Indicators, highlighting gaps in compliance, accountability, transparency, ethical guidelines, and oversight mechanisms; (2) Technical and Algorithmic Risks, including algorithmic bias and discrimination, error and inaccuracy, automation limitations, security vulnerabilities, and accountability gaps; and (3) Social and Human Impact Factors, focusing on access and inclusion, psychological effects, threats to fundamental rights, and user awareness. These indicators collectively illustrate the multidimensional nature of human rights risks in automated decision-making systems, emphasizing the need for integrated legal, technical, and social safeguards. The study underscores the complexity of protecting human rights within automated decision-making contexts and the necessity of comprehensive indicators to assess and mitigate risks. Developing robust governance mechanisms that address transparency, fairness, privacy, accountability, and inclusivity is critical. The findings provide a foundational framework for policymakers, technologists, and civil society to guide ethical AI deployment and enhance human rights protections in digital environments.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Automated decision-making systems</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">human rights risks</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">algorithmic bias</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">transparency</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">accountability</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">qualitative research</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">AI governance</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://jhrlp.com/index.php/jhrlp/article/download/31/31</ArchiveCopySource>
  </Article>
</ArticleSet>
