This framework aligns with the goals of the recently signed 'Framework Convention on Artificial Intelligence' by embedding principles of justice, equity, and inclusivity throughout the AI lifecycle.
To ensure equitable AI governance, Lim suggests conducting equity audits to check algorithms for biases prior to their release, alongside involving diverse teams in the development process.
Lim warns of the 'algorithmic divide,' which highlights disparities in access to AI technologies and the potential for biases stemming from biased training data.
The treaty underscores the differing governance approaches among the U.S., EU, China, and Singapore, emphasizing the necessity for global collaboration to address AI governance challenges.
The 'Framework Convention on Artificial Intelligence' establishes a global framework that emphasizes human rights and democracy, signed by the U.S. and EU.
The article discusses the normative impact of AI on the rule of law, questioning whether existing legal frameworks can adequately address the challenges posed by AI technologies.
Lim emphasizes that prioritizing social responsibility in AI governance can mitigate risks such as discrimination and privacy invasions, fostering public trust in AI technologies.
The 'equity by design' approach integrates equity principles throughout the AI lifecycle, particularly focusing on improving access to justice for marginalized groups.
Responsible AI governance is crucial for maximizing benefits and minimizing harms, especially for underrepresented individuals, ensuring that AI development aligns with societal values and regulatory compliance.
Socially responsible AI emphasizes ethical, transparent, and beneficial development, safeguarding human rights and fairness while actively preventing bias and discrimination.
Daryl Lim, an associate dean at Penn State Dickinson Law, has proposed an 'equity by design' framework for AI governance, aimed at protecting marginalized communities, as detailed in his recent article.
Lim's governance model seeks to transition from reactive to proactive measures, addressing structural biases while advocating for a balance between innovation and robust safeguards in AI regulation.
