Source URL: https://algorithmwatch.org/en/auditing-algorithms-and-ai-from-end-to-end/
Source: AlgorithmWatch
Title: Why we need to audit algorithms and AI from end to end
Feedly Summary: The full picture of algorithmic risks and harms is a complicated one. So how do we approach the task of auditing algorithmic systems? There are various attempts to simplify the picture into overarching, standardized frameworks; or focus on particular areas, such as understanding and explaining the “black box” of models. While this work and thinking have benefits, we need to look at systems from end to end to fully capture the reality of algorithmic harms.
AI Summary and Description: Yes
Summary: The text discusses the concept of “End-to-End Auditing” in the context of Generative AI (GenAI) systems, emphasizing the need for a comprehensive approach to address the diverse harms associated with these technologies. It highlights examples of risks involved in both the upstream (e.g., model training, labor practices) and downstream (e.g., misuse of generated content) processes and calls for improved legislation and worker protections.
Detailed Description:
– **End-to-End Auditing**:
– The text introduces the idea of auditing AI systems through an end-to-end perspective, where various components of the AI value chain are scrutinized.
– It likens the auditing process to assembling a jigsaw puzzle, representing the complexity and interconnection of AI systems.
– **Generative AI Focus**:
– The analysis specifically targets Generative AI, which can produce various types of content based on user inputs.
– It raises awareness around the critical upstream processes, such as “de-toxifying” models to mitigate the risk of generating harmful content, referencing historical failures (e.g., Microsoft’s Tay Chatbot).
– **Labor and Ethical Implications**:
– It discusses the ethical implications of outsourcing content moderation to low-paid workers in less developed countries, emphasizing how this reflects broader power imbalances in AI development.
– Examples illustrate the exploitation of these workers and their potential violations of basic rights.
– **Sustainability Concerns**:
– The text notes that environmental costs (e.g., energy and water usage) and social implications (e.g., workers’ rights) are essential considerations in assessing the sustainability of AI technologies.
– **Downstream Risks**:
– There is a spotlight on the misuse of Generative AI in producing derogatory content, particularly against women, emphasizing the legal and social challenges this presents.
– **Legislative Solutions**:
– Several legislative frameworks are identified as relevant for addressing these issues, including the EU’s proposed Corporate Sustainability Due Diligence Directive and Digital Services Act.
– The text advocates for inclusive policymaking that incorporates diverse stakeholder perspectives to better address the societal implications of AI.
– **Complex Interrelations**:
– The analysis highlights how interconnected issues of accountability, transparency, and power dynamics need to be addressed collectively.
– It critiques simplistic frameworks for examination of AI systems and calls for more nuanced and multifaceted approaches to governance.
– **Research and Coordination**:
– AlgorithmWatch, the organization mentioned, emphasizes the importance of combining broad analysis with empirical research to investigate the real-life implications of technology on individuals and communities.
– Calls for collaboration between civil society, human rights organizations, and policymakers to advocate for improvements in corporate practices and regulatory measures.
Overall, the text serves as a critical examination of the need for a holistic view of AI auditing, reinforcing the significance of social, environmental, and ethical dimensions in the deployment of Generative AI technologies. This perspective is crucial for security, compliance, and tech experts engaged in the evolving landscape of AI.