Page 43 - Cyber Defense eMagazine July 2024

discussed, these algorithms can further exacerbate inequalities amongst already marginalized groups
and technology illiterate. As the pace of innovation and rate of change increases, many will be left behind.

In this article I’ll attempt to tease out some of the more granular issues that are being overlooked or
under-examined. As a point of reference, I will use the current White House Office of Science and
Technology Policy (OSTP) Blueprint for an AI Bill of Rights and will discuss other potential measures for
regulation and best practices to improve trust, transparency, safety, and accountability, while minimizing
harm of AI, particularly as it relates to marginalized communities.

The AI Dilemma

To put it simply, AI relies on massive amounts of data to create statistical correlations in order to
accelerate decision-making. In the context of generative AI, models can create new text, image, sound,
and more based on the training data sets. These operations have risks around privacy and security, and
are already grappling with generating output that may be seen as bias or discriminatory.

Privacy and Security

AI algorithms are dependent on vast amounts of personal data being collected, stored, and analyzed.
Like with any technology, the potential for data breaches and unauthorized access poses severe risks.
Data leaks, tampering, and downtime of essential services can have significant effects on individuals and
businesses depending on the AI systems. Effective cybersecurity controls must be implemented to
minimize the likelihood of exposure, misuse, and other compromise. By its nature, the complexity of AI
systems often makes it challenging for users to understand how their data is being used, raising concerns
regarding transparency and true informed consent. Do you know what data is being collected and how
it's being used and shared? Even if you know, can you do anything about it? Clear communication and
implementing robust data privacy and security practices is critical to the effective protection of users.

Bias and Discrimination

AI algorithms depend heavily on the quality of the training data they receive and unfortunately, numerous
headline-making stories have demonstrated the inherent risk of these platforms inadvertently amplifying
existing biases which can lead to the unfair treatment of different groups, often those already
marginalized. Gender biases in training sets can lead to unequal treatment, as shown in the well-
documented case of Amazon’s recruiting tool that was trained on previous resumes, which were
predominantly men’s, thus leading to the algorithm inadvertently favoring male applicants.

Leveraging biased data sets may also perpetuate systemic racism, leading to discretionary decision-
making affecting equal employment opportunities, financial lending, or law enforcement. One example is
demonstrated as an AI-based tool used to score the likelihood of criminal re-offense incorrectly labelled
Black defendants as twice as likelihood to reoffend as white defendants. Having human intervention and
fallback mechanisms are crucial in these situations before the biases are known. But that said – and

Cyber Defense eMagazine – July 2024 Edition 43
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