AI, on Algorithmic Justice: A New Proposal Toward the Identification and Reduction of Discriminatory Bias in Artificial Intelligence Systems [Abstract]

6 Pages Posted: 31 Jul 2020

See all articles by Emile Loza de Siles

Emile Loza de Siles

Duquesne University School of Law; Technology & Cybersecurity Law Group; University of Maryland Global Campus

Date Written: 2020


As with all digitally transformational technologies, AI systems’ technical complexity and rapid
adoption in society fast and far outpace the current knowledge and experience of most
lawmakers and regulators and operate beyond the envisaged scope of many existing legal
doctrines and frameworks. Even if existing law is potentially adequate to address discriminatory
algorithmic bias in some domains, the contextual “translation” of those laws to this brave new AI
world is indeterminate and uncertain. Further, due to the difficulty of enforcement against illegal
algorithmic discrimination, jurisprudential guidance is sparse and slow in coming.

These gaps between the currently limited status of algorithmic law and the realities of the Wild
AI West create a void in which unscrupulous or merely unfettered and ambitious algorithm
purveyors may pursue enriching, but societally corrosive opportunities. Uninformed persons
making decisions about and using AI systems may adopt and deploy such systems without
appropriate insight, preparation, or restraint. Further, they may use algorithmic systems in ways
contrary to purveyors’ guidance, such with recidivism risk systems used in sentencing.

All these factors coalesce to create the potential for discriminatorily-biased algorithms to do
exponentially amplified, persistent, and irreparable harm to individuals, communities, and
society. The need is urgent for a workable system of algorithmic justice by which to illuminate
and eradicate discriminatory computational biases, or at least to more quickly identify them and
reduce their incidence and duration. Fostering greater access to justice, public trust in AI
technology, and other important policy goals, an algorithmic justice system also would provide
empirical mechanisms by which to establish baselines and measure and communicate the status
of progress toward eradicating discriminatory algorithm bias in State of the Algorithmic Nation
reports, for example. In addition, this algorithmic justice system would provide a framework for
crafting meaningful policy, legislative, and regulatory systems for AI and a more accessible and
definitive means of enforcing and litigating against illegal algorithmic discrimination.

This work offers a new model toward an algorithmic justice system. As a beginning to address
the likely immense and certainly multiply complex problem of discriminatory algorithmic bias,
this new model commences with two foundational processes.

Keywords: law, artificial intelligence, discrimination, machine learning,

Suggested Citation

Loza de Siles, Emile, AI, on Algorithmic Justice: A New Proposal Toward the Identification and Reduction of Discriminatory Bias in Artificial Intelligence Systems [Abstract] (2020). Duquesne University School of Law Research Paper Forthcoming, Available at SSRN: or

Emile Loza de Siles (Contact Author)

Duquesne University School of Law ( email )

600 Forbes Avenue
Pittsburgh, PA 15282
United States

Technology & Cybersecurity Law Group ( email )

Washington, DC
United States

University of Maryland Global Campus ( email )

3501 University Boulevard East
Adelphi, MD 20783
United States

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