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AI ethics groups are repeating one of society’s classic mistakes

Artificial intelligence / AI ethics groups are repeating one of society’s classic mistakes

Too many councils and advisory boards still consist mostly of people based in Europe or the United States.

International organizations and corporations are racing to develop global guidelines for the ethical use of artificial intelligence. Declarations, manifestos, and recommendations are flooding the internet. But these efforts will be futile if they fail to account for the cultural and regional contexts in which AI operates.

AI systems have repeatedly been shown to cause problems that disproportionately affect marginalized groups while benefiting a privileged few. The global AI ethics efforts under way today—of which there are dozens—aim to help everyone benefit from this technology, and to prevent it from causing harm. Generally speaking, they do this by creating guidelines and principles for developers, funders, and regulators to follow. They might, for example, recommend routine internal audits or require protections for users’ personally identifiable information.

We believe these groups are well-intentioned and are doing worthwhile work. The AI community should, indeed, agree on a set of international definitions and concepts for ethical AI. But without more geographic representation, they’ll produce a global vision for AI ethics that reflects the perspectives of people in only a few regions of the world, particularly North America and northwestern Europe.

This work is not easy or straightforward. “Fairness,” “privacy,” and “bias” mean different things (pdf) in different places. People also have disparate expectations of these concepts depending on their own political, social, and economic realities. The challenges and risks posed by AI also differ depending on one’s locale.

If organizations working on global AI ethics fail to acknowledge this, they risk developing standards that are, at best, meaningless and ineffective across all the world’s regions. At worst, these flawed standards will lead to more AI systems and tools that perpetuate existing biases and are insensitive to local cultures.

In 2018, for example, Facebook was slow to act on misinformation spreading in Myanmar that ultimately led to human rights abuses. An assessment (pdf) paid for by the company found that this oversight was due in part to Facebook’s community guidelines and content…

Abhishek Gupta

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