Article Instance

HTTP 200 OK
Allow: GET
Content-Type: application/json
Vary: Accept

{
    "pk": 47403,
    "title": "Facial Recognition in Policing: How Algorithmic Bias Targets People of Color",
    "subtitle": null,
    "abstract": "<p>The presence of racially biased facial recognition technology (FRT) in law enforcement presents significant legal and ethical challenges, especially in its disproportionate impact on communities of color. The biases embedded in FRT systems, due to non-diverse training datasets, lead to misidentifications that result in wrongful arrests, detentions, and broader violations of constitutional rights. This exacerbates systemic racial inequalities and further entrenches discriminatory practices within the criminal justice system. The growing reliance on FRT for policing necessitates comprehensive reforms in both technology and law to address these concerns. Therefore, solutions aimed at reducing racial bias must include diversifying training datasets, improving data quality, and incorporating advanced techniques to enhance the accuracy of these systems. However, technological improvements alone are insufficient to resolve deeper racial issues. A holistic approach is required, involving robust legal frameworks for accountability, extensive training for law enforcement, and independent audits of FRT use to ensure its fair and transparent application. While further research into the practical, large-scale implementation of these reforms is necessary, addressing the intertwined technological and legal challenges is essential to address the widespread concerns presented by racially biased FRT in policing and to protect the rights of all individuals.</p>",
    "language": "eng",
    "license": {
        "name": "Creative Commons Attribution 4.0",
        "short_name": "CC BY 4.0",
        "text": "Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\r\n\r\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.",
        "url": "https://creativecommons.org/licenses/by/4.0"
    },
    "keywords": [
        {
            "word": "AI"
        },
        {
            "word": "Criminal Justice"
        },
        {
            "word": "Civil Rights"
        },
        {
            "word": "facial recognition technology"
        },
        {
            "word": "racial bias"
        }
    ],
    "section": "Article",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/87c9h9kx",
    "frozenauthors": [
        {
            "first_name": "Velmar",
            "middle_name": "",
            "last_name": "Amador-Lankster",
            "name_suffix": "",
            "institution": "",
            "department": ""
        }
    ],
    "date_submitted": "2025-05-13T06:04:55.525000Z",
    "date_accepted": "2025-05-14T05:34:57.979000Z",
    "date_published": "2025-05-21T03:00:00Z",
    "render_galley": {
        "label": "PDF",
        "type": "pdf",
        "path": "https://journalpub.escholarship.org/ucsdulr/article/47403/galley/35814/download/"
    },
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/ucsdulr/article/47403/galley/35814/download/"
        }
    ]
}