Data ethics and AI ethics

1. Data ethics and AI ethics

Hi there!

2. What is AI?

AI or Artificial Intelligence combines computer algorithms with datasets to solve problems, and provide insights due to their unprecedented computational power. The most powerful AI algorithms require massive amounts of data to function. The power of AI technologies is a double-edged sword that poses risks and benefits, which is a fertile topic for ethical considerations.

3. Data is fuel for AI

While building and deploying AI models, most of the efforts are spent on data collection, access, and preparation. AI expert Andrew Ng compares data sourcing and preparation to procuring good quality ingredients to cook a meal, that is, to train an AI model with the data— poor data results in poor AI outcomes. While data is fundamental for AI success, AI ethics are far more wide-ranging than data ethics. And that's why it's essential to know both the similarities and differences between data and AI ethics to focus on the relevant issues and strategies. Let's see why and how.

4. The good and the amazing

AI has become an integral part of modern life, from voice assistants to conversational chatbots to Netflix recommendations. On the bigger picture, AI benefits society through applications like early cancer detection, precision agriculture to minimize water consumption, and pesticide use.

5. The bad and the ugly

On the flip side, AI algorithms are known to cause harmful discrimination against people based on gender and race in high-stakes scenarios such as hiring, access to healthcare, and fair trials in a court of law. The reasons for these unfortunate outcomes are many. Some are due to biased datasets, and others are due to the complex black-box nature of AI algorithms that don't explain decisions. The harmful effects of AI lead to several ethical dilemmas that go beyond data ethics.

6. AI ethics dilemmas

AI ethics can range from the consequences on individuals due to the automated decision-making systems for loan approvals and hiring to societal aspects such as the risk of discrimination against specific groups and the loss of livelihoods due to automation. AI ethics also deal with issues that might change the course of humanity, like what happens when robots fight wars instead of humans, what it even means to be a human, whether we can control it, and how we can encode morality and ethics within an AI. Let's see a practical example.

7. What should an AI do?

An excellent example of an AI ethical dilemma is what a self-driving car should do when its brake suddenly fails. It has two options, either to go straight or to swerve to the other side. If it continues straight, it will kill a woman, a man, and an older woman. If it swerves, it will kill two children and a young woman. It's morally complex even for humans. How can we encode morality into AI decisions and who is responsible? How do we deal with such AI risks?

8. AI ethics guidelines in a nutshell

Many government, policy, and even private organizations are working on AI ethics guidelines for businesses and governments to manage the risk. Most of the guidelines contain a variation or combination of the following aspects to design and deploy AI systems. Humans need to have control and oversight on the AI, the AI systems need to be technically robust and safe, they need to respect the principles of privacy, data governance, transparency and fairness. They also should take the well being of the environment and society and should have clear accountability framework. Algorithm Watch, a non-profit that tracks AI development and impact, collects the various AI ethics guidelines globally in their publicly available inventory.

9. The data overlap

Many of these principles may seem similar to data ethics principles. In fact the aspects of privacy, fairness, transparency and accountability are the core tenets of data ethics. This should be of no surprise because data is the fuel that runs AI.

10. Let's practice!

Nice work so far! Now let's test your knowledge.

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