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    Area of Science:

    • Law and Technology
    • Computer Science
    • Social Sciences

    Background:

    • Algorithmic systems are increasingly prevalent in decision-making processes.
    • Discriminatory bias in these systems is a well-documented issue.
    • Current legal responses often focus on indirect discrimination.

    Purpose of the Study:

    • To challenge the prevailing legal approach to algorithmic bias.
    • To argue that a narrow focus on indirect discrimination is inadequate.
    • To explore the potential for algorithmic bias to constitute direct discrimination.

    Main Methods:

    • Analysis of existing anti-discrimination law.
    • Examination of machine learning algorithms and their biases.
    • Legal and normative critique of indirect discrimination frameworks.

    Main Results:

    • Certain forms of algorithmic bias may constitute direct discrimination.
    • Over-reliance on indirect discrimination is legally flawed and normatively undesirable.
    • Automated decision-making systems challenge traditional legal concepts.

    Conclusions:

    • Anti-discrimination law needs to adapt to the complexities of algorithmic bias.
    • A broader legal lens, including direct discrimination, is necessary.
    • Further examination of the conceptual challenges posed by AI is required.