ArtificiaI Intelligence: Can you trust the answers?
Artificial intelligence and machine learning are today’s hot topic with significant investment across industries and geographies. This means that computers are making more and more routine decisions, and, in the future will also drive strategy, leading us to the obvious question: can we trust the decisions made by computers?
The Building Blocks for Trusted AI
Trusting the answers from Artificial Intelligence (AI) and Machine Learning models depends on several factors, including:
Data Quality
The quality of the data AI is trained on: As the saying goes, “garbage in, garbage out.” If an AI model is trained on biased or inaccurate data, its output will likely be biased or inaccurate as well. It’s crucial to understand the data sources and training processes used by an AI model before relying on its outputs.
Data Transparency
The transparency of the AI model: Some AI models are transparent, meaning you can understand how they arrive at their answers. Others are black boxes, where the internal workings are hidden. Transparent models allow for greater trust, as you can assess the reasoning behind their outputs.
The stakes at hand
The specific task the AI is performing: Some AI tasks are better suited for AI than others. For example, an AI model that identifies images with 95% accuracy is generally considered trustworthy for that task. However, an AI model that makes complex financial decisions might not be trustworthy, even if it has a high accuracy on historical data.
User Expectations
The user’s own understanding and expectations: It’s important to remember that AI is still a young technology, and even the best models can make mistakes. Users should have realistic expectations about what AI can and cannot do and avoid relying solely on AI outputs for critical decisions.
Tips for evaluating the trustworthiness of AI answers:
The potential and opportunities created by AI are enormous, but so are the risks.
Like any other tool, artificial intelligence must be used responsibly. Some simple tips for the responsible use of AI and ML include:
- Confirming sources: If an AI model provides a factual answer, ask for sources or evidence to support its claim. Data lineage can help to ensure that data sources are accurately defined.
- Considering context: AI answers should be considered within the context of the larger conversation or task.
- Beware of overconfidence: Just because an AI model is confident in its answer doesn’t mean it’s correct. Data quality and data observability capabilities help to ensure that underlying data supports accurate decision making.
- Use multiple sources: Don’t rely solely on AI for information. Cross-check its answers with other sources, especially for critical decisions.