Welcome to part 2 /3 of the impact of AI being leveraged. Here are some potential challenges and limitations to be aware of when using AI for ESG risk assessment:
- Data bias - if the training data itself reflects historical biases or gaps, models may replicate unfair outcomes. Ensuring representativeness is critical.
- Explainability - complex AI may behave unpredictably requiring extra effort to explain drivers behind outcomes. Transparency is important for accountability.
- Accuracy - numbers alone don't capture the complexity of ESG issues. There remains a role for humans to apply deeper contextual understanding.
- Gaming strategies - rated entities could try to manipulate data inputs/outputs undermining integrity of assessment if weaknesses exploited.
Careful model design, extensive testing, transparency and human oversight are needed to ensure AI augments rather than replaces traditional assessment approaches given its limitations.
- Model drift - without ongoing monitoring/retraining, models can become outdated as risks/issues evolve over time.
- Lack of interpretability - neural networks are not inherently interpretable so attribute importance or feature effects may be unclear.
- Generalizability - models trained on one region/sector may not transfer well to others without representative data.
- Data availability - lack of comprehensive structured/unstructured data to sufficiently power some AI techniques like NLP in all markets.
- Technological challenges - processing unstructured data remains difficult despite advances. Quality varies widely.
- Reliance on infrastructure - systems require fast computing/storage resources and robust data/tech governance to operate at scale.
Careful model design, extensive testing, transparency and human oversight are needed to ensure AI augments rather than replaces traditional assessment approaches given its limitations.
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