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{
    "pk": 47412,
    "title": "Equal Protections in the Age of AI: Navigating Civil Rights in Housing with Machine Learning",
    "subtitle": null,
    "abstract": "<p>As software becomes increasingly widespread, the data generated through bookkeeping and information transfers can be harnessed for the public good. The principle of data science, using forms of information to uncover insights or predict future outcomes, is dependent on inputs to fuel its function. For instance, large language models such as ChatGPT draw from massive datasets to respond to user questions with precision in the form of a large language model. What happens, however, when the data driving these technologies is inaccurate or biased? If flawed inputs lead to life-altering consequences, who should be held accountable? This paper challenges the efficacy of artificial intelligence algorithms used by housing corporations to identify “ideal” tenants. Through examining two recent court cases and outdated privacy legislation, I will identify inconsistencies in user data protection law and reevaluate them through a legal and technological lens. The paper argues that, to protect civil rights in the age of AI, both the data and the laws governing its use must be critically examined. By focusing on AI’s role in national housing development, broader industries leveraging artificial intelligence may integrate similar safeguards to ensure fairness and accountability for the people they serve.</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": "ChatGPT"
        },
        {
            "word": "AI"
        },
        {
            "word": "housing development"
        },
        {
            "word": "data protection law"
        },
        {
            "word": "technology"
        },
        {
            "word": "data science"
        },
        {
            "word": "housing bias"
        }
    ],
    "section": "Article",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/15b8x25k",
    "frozenauthors": [
        {
            "first_name": "Mehri",
            "middle_name": "",
            "last_name": "Sadri",
            "name_suffix": "",
            "institution": "",
            "department": ""
        }
    ],
    "date_submitted": "2025-05-13T13:48:00.705000-05:00",
    "date_accepted": "2025-05-14T01:00:50.694000-05:00",
    "date_published": "2025-05-20T22:00:00-05:00",
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        "label": "PDF",
        "type": "pdf",
        "path": "https://journalpub.escholarship.org/ucsdulr/article/47412/galley/35817/download/"
    },
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            "label": "PDF",
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}