AI is becoming more powerful and impactful, touching everything from how we work to make decisions. But with that power comes responsibility. It’s no longer just about what AI can do but what it should do.
Recognizing the Risks of AI
Even the smartest AI has limits if left unchecked. The following limits can lead to big problems.
Hallucinations (when AI creates or imagines inaccurately)
AI can be smart while delivering inaccuracies. Large language models sometimes "hallucinate," by seemingly generating imitation citations, dates, or facts.
Example: A chatbot suggesting a legal case that doesn't exist.
Tip: Ground AI in verified, up-to-date, well-vetted data by adding human review.
Frozen in Time
AI models don’t automatically know what happened yesterday. Without access to current data, their answers can be outdated and invalid.
Tip: Techniques, like Retrieval-Augmented Generation (RAG), allow AI to access live company data.
Weak in Complex Mathematical Reasoning
AI requires improvements in math or complex reasoning. It might guess at an answer instead of calculating it.
Tip: Human oversight or pairing AI with tools for better accuracy.
Bias and Fairness
AI can reflect real-world inequalities hidden in training data, like the biased use of specific demographics in hiring or credit scoring.
Tip: Diverse training data, fairness audits, and strong governance.
Limited Human-Like Logic
AI doesn’t fully understand the world's nuances. It just predicts patterns, and it can’t grasp human intent.
Tip: Keep humans involved. AI is a powerful assistant, not a replacement.
Drift Over Time
AI models can become less accurate as the world changes. This challenge is called "model drift."
Tip: Continuous monitoring, regular updates, and real-world testing.
Building Trust: SAP's Approach to Responsible AI
Building responsible AI is a proactive effort at SAP, grounded in policy, training, and technology. Here’s how SAP approaches responsible AI:
- Ethical AI Design: Our development process includes fairness, privacy, and human oversight checks.
- Upskilling Employees: SAP teams are trained in ethical AI use across functions.
- Ethics Policy: Our global AI ethics principles are public and active.
Assessing Sustainability with AI: Can AI Help?
While AI can help reduce waste and optimize operations, it’s essential to acknowledge that training and running AI models consume significant energy, especially with large models like Large Language Models (LLMs).
How AI Supports Sustainability:
- Carbon Tracking: Automating emissions reporting and audits
- Energy Optimization: Helping data centers and factories reduce consumption
- Smart Reporting: Generating ESG reports quickly and accurately
- Supply Chain Insight: Identifying inefficiencies that waste energy or resources
SAP solutions already help businesses monitor and improve their environmental impact with AI, like the Emission Factor Mapping with AI in SAP Sustainability Footprint Management, which automates and refines carbon footprint calculations, and the ESG Report Generation with AI in SAP Sustainability Control Tower, which streamlines the creation of reliable, data-driven sustainability reports.
Lesson Summary:
- AI isn’t perfect. It can hallucinate, forget the latest updates, or reflect bias, but these risks can be managed with human oversight, good governance, and the right tools.
- Responsibility is shared. Developers, businesses, and users each play a part in ensuring AI is used ethically and fairly.
- Sustainability matters. AI consumes energy but can also help reduce environmental impact when used wisely.
- SAP leads by example. SAP is committed to developing and deploying trustworthy, transparent, sustainable AI.
Resources:
Explore more resources to stay current with SAP’s AI roadmap, and keep building your knowledge: