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As 2024 comes to a close we are taking a moment to reflect on some of the year’s most impactful conversations in AI. From exploring the ethical use of legendary rapper Tupac’s vocals using AI to advancing global AI policy through inclusive practices, our top six blogs of the year have sparked important discussions across the PAI community. These blogs reflect the wide breadth of topics within AI, demonstrating its impact on culture, ethics, and governance. We recognize the vast scope of work that lies ahead as AI continues to permeate our everyday lives and continue leading in fostering equitable and responsible innovation in this rapidly evolving field.
1. Drake vs Kendrick vs AI: They Not Like Us
Earlier this year, rap legends Kendrick Lamar and Drake found themselves at the center of what some are calling the rap battle of the century. What began as a time honored tradition of battling it out on the airwaves quickly turned into a deeper conversation around the ethical uses of AI in the music industry. Highlighting the many challenges that AI in the music industry poses, this blog post explores important questions the feud raised, such as: the importance of consent, authorship, and disclosure when it comes to the use of AI in music.
2. Prioritizing Equity in Algorithmic Systems through Inclusive Data Guidelines
Beyond generative AI applications, algorithmic systems are pervasive in our everyday lives, from screening job applicants to recommending content tailored to our interests to figuring out the fastest route for our commutes to work. But with their widespread adoption comes the issue of algorithmic bias. Disproportionately impacting marginalized communities, these biases not only lead to discriminatory outcomes for users of these systems but can also perpetuate existing structural inequities. In this blog post we introduce a draft of the Participatory & Inclusive Demographic Data Guidelines which aim to provide AI developers, teams within technology companies, and other data practitioners with guidance on how to collect and use demographic data for fairness assessments to advance the needs of data subjects and communities. The final version of the guidelines will be released next year.
3. Balancing Safety and Accessibility for Open Foundation Models
Foundation models, or general purpose AI, are not just advancing rapidly but also increasingly being released with open access. The coming months and years may see the release of more powerful open models and as these models become more accessible to larger audiences we need to focus on developing tailored risk mitigation strategies. This blog post explores the AI value chain for open foundation model governance to determine effective mitigation strategies for actors to implement specific guidance to address risks. Following this blog, we released a resource to help explore new approaches for releasing future cutting-edge models.
4. How Better AI Documentation Practices Foster Transparency in Organizations
A better understanding of the AI/Machine Learning development, deployment, and decision-making processes can support user trust in AI/ML systems. Users need assurance that these systems will reliably offer accurate and informed outputs, safeguard against failures, and protect and uphold privacy. Transparency involves making a system’s properties, purpose, and origins clear and explicit to users, practitioners, and other impacted stakeholders. This blog dives into PAI’s ABOUT ML initiative which aims to increase standardization and improve the rigor of AI/ML documentation by sharing best practices. We share three reports on pilots conducted with Intuit, UN OCHA, and Biologit as well as the key takeaways gathered from the studies.
5. 10 Things You Should Know About Disclosing AI Content
AI has made it easier to manipulate and generate media, posing challenges to truth and trust online. In response, policymakers and AI practitioners have rightfully called for greater audience transparency about AI-generated content. Our recently released case studies are focused around direct disclosure — methods to convey to audiences when content has been modified or created with AI, like labels or other visual signals. These cases informed the takeaways in this blog, which are reflective of one moment in time for a rapidly evolving field.
6. Meaningful AI Policy Requires Inclusive, Multistakeholder Participation
With more and more AI tools reaching the hands of the public, there is an urgent need for policies to protect people and communities from its harms and to advance responsible innovation. As policymakers at national and international levels work to govern AI development, deployment, and use, it is essential to bring ideas from across sectors and disciplines to the policy discussion to center solutions that work for people, not just companies. In this blog we emphasize the importance of sociotechnical expertise in developing meaningful AI policy.
As we move into 2025, we emphasize the importance of a multistakeholder focus on the ethical and responsible development of AI technology. As rapid innovation continues to accelerate the development of these technologies, it is now more important than ever for all actors across the AI value chain to understand their responsibility in creating safe and responsible technology for all.
Our work at PAI to bring together voices and communities from across civil society, industry, academia, and government continues to advance. As the AI landscape evolves over the next year, our diverse voices will be more important than ever in shaping AI that benefits society. Here’s to another year of insightful discussions! To follow along with our efforts sign up for our
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