Learn why it's absolutely crucial for AI-related data science work to be transparent, explainable, accountable, and ethical in its design and execution.
- [Barton] If you've ever had a photo application tag your family or friends automatically or if you've had an online map reroute you to avoid traffic, then you've benefited from artificial intelligence or AI. Or maybe you've had your life saved by an AI assisted diagnosis of heart disease. There's so many amazing developments in AI, that, really it's impossible to describe all the ways that it affects your life now and in the future. But, it's also worth pointing out that AI is not a universal cure for every problem and that, like all developing technologies, it runs into unexpected technical difficulties and introduces new legal, ethical and social challenges.
I'm Barton Poulson and in this brief course, we'll take a non-technical, conceptually oriented look at the promise and the perils of artificial intelligence, along with potential solutions to these challenges. We'll explore the different ways that an AI's accuracy can be defined. The effects of bias and training datasets. The ways that it concepts the fairness and relationships work into AI. The major legal frameworks of AI and how AI operates in life and death situations. And we'll make specific suggestions on how programmers, executives, PR professionals, regulators and consumers can best approach the challenges to AI.
We'll mention some of the important technology of AI, but more importantly, we'll explore the effects of AI in real life. Because this is a non-technical overview, anyone with an interest in artificial intelligence, regardless of their technical background, can get a better understanding of how this critical technology affects their lives and what they can do to get the greatest benefit out of it. Let's get started with AI Accountability Essential Training.
- Bias in AI
- Navigating the social challenges of AI
- Moral reasoning and relational ethics
- General Data Protection Regulation (GDPR) and AI
- Discrimination in data
- Liability and AI
- AI in life-and-death situations
- Confronting the challenges of AI as a developer, executive, and consumer