Conclusion

AI can be a force for good, potentially detecting tumours that humans cannot and Alzheimer’s before one’s family can or preserving indigenous languages. Throughout this module, we’ve shown the power of AI systems, but also their opacity. If we want AI to benefit society more than it harms it, we have to acknowledge the risks and take action to ensure AI systems are designed, developed, and used responsibly.

As technologists, even when we’re conscientious and deliberate in our approach there will be surprises along the way. We can’t always predict the interactions between datasets, models, and their cultural context. Datasets often contain biases that we’re not aware of, and it’s our responsibility to evaluate and assess training data and our models’ predictions to ensure they don’t yield any damaging results.

Developing ethical AI systems is a socio-technical process. Look at it not only in terms of its technical implementation but also through the way it’s developed across teams and the social contexts it will be used in. What’s more, assess who’s involved in the process—how are gender, race, ethnicity, and age represented? The people building AI products and the bias engendered by these systems are interconnected.

To realize safe, socially beneficial AI, we need to remember that humans are at the heart of it. AI is a tool and we choose how to use it. Regardless of someone’s role, their minor decisions can have serious, lasting consequences. At Salesforce, we strongly believe that we can do well and do good. You can make a profit without harming others, and, in fact, make a positive impact in the process.

Resources

• Blog: How to build ethics into AI - Part 2
• Research Paper: Path-Specific Counterfactual Fairness
• AI Now 2018 Report
• Book: Towards a Code of Ethics for Artificial Intelligence