Cracking the Code: Are ESG Reports Truly Accessible?

In the pursuit of sustainability, Environmental, Social, and Governance (ESG) reports have become a staple for companies committed to transparency. These documents are supposed to cater not just to financial experts but to everyone. Still, how accessible are they really?

Readability: A Subjective Affair

Recent research delves into the readability of German ESG reports, extending an existing dataset with crowdsourced annotations. The findings are a mixed bag. While native speakers generally find sentences in these reports easy to read, readability, it seems, is highly subjective. What one person finds clear, another might find convoluted.

Various readability scoring methods were put to the test. The standout? A finely tuned transformer model that closely mimicked human readability judgments. It's a testament to how far machine learning models have come. But let's not forget, slapping a model on a GPU rental isn't a convergence thesis. The true test lies in the results.

The Trade-Off Between Performance and Speed

Averaging predictions from multiple models did show a slight improvement in performance, yet it came at a cost, slower inference times. And in our fast-paced digital age, latency could be a dealbreaker. Decentralized compute sounds great until you benchmark the latency. A balance between accuracy and speed is important. After all, if the AI can hold a wallet, who writes the risk model?

Why This Matters

Why should we care about the readability of ESG reports? Because these documents are more than corporate buzzwords. They hold companies accountable and inform the public about sustainability efforts. If most people can't understand them, aren't they just another exercise in corporate greenwashing?

The intersection of machine learning and real-world utility is real. Yet, we must ask ourselves: Are we truly making information more accessible, or are we just layering complexity with more complexity? Show me the inference costs. Then we'll talk.