Lead the GenAI revolution. Future-proof your skills by understanding AI basics, machine learning, algorithms, ethical practices, and real-world applications. Stay ahead in tech leadership.

In one form or the other, AI is going to be infused in all the tech products. Meaning, that in order to stay a relevant leader, it has become essential to have a solid, broad understanding of AI. While you may not be implementing the solution, you need to speak the language of AI.
 
Whether you’re leading a team on creating AI solutions, or you just want a better understanding of AI, then this course is for you. In this course, I have deconstructed the concepts most relevant for you.
 
You will start by learning the basics of AI, its connection with machine learning, and the different types of algorithms you should be familiar with. You’ll then learn the rules, best practices, and infrastructure for creating great AI products that users trust. Then, you will go through some real case studies of AI application and do a deep dive into Ethical AI. You'll end by getting an outlook into the current status and future of AI. In a nutshell, this course is your no-fuss, comprehensive guide to the essentials of AI.

Grokking AI for Engineering & Product Managers

## Designing trust into AI systems
<br>

### A black-box that can be trusted 

The *Oracle of Silicon Valley* and the so-called web guru and trend spotter, Tim O'Reilly [says](https://www.linkedin.com/pulse/great-question-21st-century-whose-black-box-do-you-trust-tim-o-reilly): 

>*"The great question of the 21st century is going to be 'Whose black box do you trust?"*


AI is increasingly influencing our social, cultural, economic, and political interactions. More and more of our lives are in the hands of "black boxes," i.e., algorithms making decisions and shaping choices but whose inner workings are often a mystery even to their creators (deep learning models are an example of such black boxes). These black boxes are not only affecting us at the individual level, but algorithms are now shaping organizations and societies as a whole (hint: U.S. elections and Trump). In such a world comes the key question of trust. 

Sure, Alexa doesn’t make life-changing decisions for us. Or at least not yet! But if we consider autonomous cars, for example, it becomes crucial that we trust that system.

This means that understanding how to evaluate algorithms without knowing the exact rules they follow (missing transparency) is a key discipline in today's world. O'Reilly proposes four tests to determine whether a black box algorithm can be trusted:

1. **Clarity of intended outcome**: The creators should make clear what outcome they are seeking, and it should be possible for external observers to verify that outcome. *Take the example of an airplane autopilot. A clear outcome could be to respond correctly to wind and weather, in accordance with known principles of aeronautics.*

2. **Measurability**: Success should be measurable. *Alignment between the actual behavior of the plane and the laws of physics and aeronautics.*
3. **Goal alignment**: The goals of the algorithm's creators should be aligned with the goals of the algorithm's consumers. *For instance, a smooth ride without crashing but not added fuel consumption that increases global warming.*
4. **Long term decisions**: The algorithm should lead its creators and its users to make better longer-term decisions. *For example, does it mean that all the pilots lose their jobs?*

# Designing trust into AI systems
<br>

## A black-box that can be trusted 

The *Oracle of Silicon Valley* and the so-called web guru and trend spotter, Tim O'Reilly [says](https://www.linkedin.com/pulse/great-question-21st-century-whose-black-box-do-you-trust-tim-o-reilly): 

>*"The great question of the 21st century is going to be 'Whose black box do you trust?"*


AI is increasingly influencing our social, cultural, economic, and political interactions. More and more of our lives are in the hands of "black boxes," i.e., algorithms making decisions and shaping choices but whose inner workings are often a mystery even to their creators (deep learning models are an example of such black boxes). These black boxes are not only affecting us at the individual level, but algorithms are now shaping organizations and societies as a whole (hint: U.S. elections and Trump). In such a world comes the key question of trust. 

Sure, Alexa doesn’t make life-changing decisions for us. Or at least not yet! But if we consider autonomous cars, for example, it becomes crucial that we trust that system.

This means that understanding how to evaluate algorithms without knowing the exact rules they follow (missing transparency) is a key discipline in today's world. O'Reilly proposes four tests to determine whether a black box algorithm can be trusted:

1. **Clarity of intended outcome**: The creators should make clear what outcome they are seeking, and it should be possible for external observers to verify that outcome. *Take the example of an airplane autopilot. A clear outcome could be to respond correctly to wind and weather, in accordance with known principles of aeronautics.*

2. **Measurability**: Success should be measurable. *Alignment between the actual behavior of the plane and the laws of physics and aeronautics.*
3. **Goal alignment**: The goals of the algorithm's creators should be aligned with the goals of the algorithm's consumers. *For instance, a smooth ride without crashing but not added fuel consumption that increases global warming.*
4. **Long term decisions**: The algorithm should lead its creators and its users to make better longer-term decisions. *For example, does it mean that all the pilots lose their jobs?*

This lesson discusses how to incorporate trust into AI systems. 


The Fundamentals

AI in Practice

Real Case Studies

Responsible AI

What's Next

Creating AI Solutions That Users Trust

Designing trust into AI systems

A black-box that can be trusted