Algorithms Overview

Reinforcement Learning is a very vast list of algorithms. Let's look at this structure of the Reinforcement world and try to understand it.

  • MDP World:

This is the first division, Markov Decision Process(MDP). These are the algorithms that try to solve the problem, where we have an underlined vision of the world. There are states and rewards (which we get when we move from one state to another).


Bias & Fairness

Check out my previous blogs if you want to read more and understand topics such as what is bias, its real-world use cases, and the mitigation methods.

  1. Bias in AI
  2. Algorithmic Bias in real-world
  3. Bias Mitigation — Methods

We will conclude with an overview of Bias & Fairness in AI with some key takeaways:

  1. A lot of people think that AI is some kind of magic when it is not!! It reflects the data(training data). AI system takes the examples(datasets) and learns from it. The essential thing is to know when and where to use AI models. “If there is nothing to learn from learning is impossible”.
  2. The best way to check the model or the…


How to build a Fair Model

Understanding and solving “Bias in AI” is more important than ever before. The first step to solving bias is awareness of bias which is followed by bias mitigation.

AI is having a high impact on our daily lives. This is why understanding and implementing fairness is such an important to consider!!

Fairness: It is the quality of making impartial treatment or judgment that does not favor or discriminates.

Sounds good? that's what our Models need….Fairness!! Let's add it to our models now!! Sounds simple?….it's actually more nuanced than that. …


Practical Examples of Bias

***The Intent of this blog is just to show the importance of understanding Bias in Artificial Intelligence***

While there are many real and potential benefits of using AI, a flawed decision-making process caused by Human bias embedded in AI output makes this a big concern for its real-world implementation. The growth of Artificial Intelligence in sensitive areas such as hiring, criminal justice, and healthcare has sparked debates on bias and fairness.

Consider the following examples:

  1. Predictive Policing:

Predictive policing involves using algorithms to analyze data in order to predict and also help prevent potential crimes in the future. It is…


Inside AI

How bias can explode our AI models

There has been a lot of confusion over Bias in the field of Artificial Intelligence. Let's try to understand and uncomplicate some things!!

What do you see in this picture above?

  • Apple? Apples? A Bushel of apples?
  • Fruit? Bunch of fruits?

Okay, there is nothing wrong with these answers!! Just something to notice, how often do we tend to say “Red Apples”…. very typical right?? Agreed. So Categorization within Cognitive Science, where some categories are more typical than others is called Prototype Theory. It is a type of Human Bias or Cognitive Bias, which is a result of the brain’s…


Misinterpretation of Cause and effect relationship!!

“Correlation is not causation” or “Correlation does not imply Causation” are very commonly used today in the field of AI, which makes it more important!!

Let's start with an example by comparing “Cholesterol” with “Exercise” and try to understand the relationship

Observations:

  • On the left graphs, it seems like more exercise lead to higher Cholesterol
  • But in the right graphs, we see there is a confounder, “Age”, that influences both “Exercise” and “Cholesterol”
  • 2nd graph makes more sense?? right?? It agrees with the truth that more exercise will lead to lower cholesterol

Now lets define the terms we are trying…

Abhishek Dabas

Masters Student | Machine Learning | Artificial Intelligence | Causal Inference | Data Bias | Twitter: @adabhishekdabas

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