Introduction

• Define the scope of AI and its societal implications, particularly in perpetuating or amplifying racism.
• Introduce the case study you’ll analyze (e.g., facial recognition systems, predictive policing, or algorithmic bias in hiring tools).
• State the thesis: How the chosen case study illustrates the nexus between AI and racism, integrating one or more of the course’s key concepts.

Section 1: AI and Racism - Setting the Stage

• Provide a concise overview of AI`s role in modern systems, focusing on how it reflects and perpetuates societal biases.
• Discuss how historical patterns of racism are embedded in data and algorithms, leading to discriminatory outcomes.
• Highlight connections to psycho-codification, explaining how the codification of biases into AI systems influences human cognition and societal structures.

Section 2: Case Study Analysis

• Present the selected case study (e.g., the biases in facial recognition technology, which has higher error rates for individuals with darker skin tones).
• Explain the technological mechanisms at play and their real-world consequences, such as misidentification or exclusion.
• Explore how microbial surveillance might metaphorically apply, such as the invasive scrutiny and classification systems disproportionately affecting marginalized groups.
• Critically analyze the case study`s implications, showing how AI systems can serve as tools of systemic racism.

Section 3: Deeper Theoretical Connections

• Introduce and elaborate on post-human warfare, if relevant, by drawing parallels between AI-driven systems and mechanisms of control in a post-human context.
• Argue how AI’s role in surveillance and decision-making might be viewed as a form of “warfare” against marginalized populations, perpetuating structural inequalities.

Section 4: Solutions and Counterarguments

• Discuss potential strategies to mitigate AI-related biases, such as improved data practices, algorithmic transparency, and inclusive design processes.
• Address counterarguments: Can AI ever be truly unbiased? What ethical frameworks must be implemented to achieve equity?

Conclusion

• Summarize the key points of your analysis.
• Reiterate the central thesis, emphasizing the interconnectedness of AI, racism, and the course’s theoretical concepts.
• Offer a final reflection on the broader implications for society, technology, and ethics.

Instructor Feedback Integration

• Demonstrate "audacity" by presenting a strong, critical argument, showing how your case study illuminates deeper societal issues.
• Incorporate concepts like psycho-codification, microbial surveillance, or post-human warfare explicitly to frame and enhance your analysis.

References

• Include the two provided sources and additional relevant literature from your syllabus or external research.