AI Calories Scam

• Near Pose - X Calories
• Far Pose - X/2 Calories
• Side Pose - 3X Calories

Ref - Link

Keep Thinking!!!

information asymmetry, Common-Knowledge Effect

It is a very relatable experience to information asymmetry from project experiences, domain understanding.

Some instances when 

• Customers share limited information 
• Unexplained features about transactions
• Correlations were removed and data anonymized

This usually happens and results in poor forecasting

When two people do not have same level of info, the perception and understanding varies

Common-Knowledge Effect: A Harmful Bias in Team Decision Making

The common-knowledge effect is a decision-making bias where teams overemphasize the information most team members understand instead of pursuing and incorporating the unique knowledge of team members.

Preference Bias

• We are more likely to discuss information that aligns with our initial preferences or preconceived notions.
• Even when all information is shared with the group, we still process that information according to our initial preferences.

Social Comparison

• We seek social acceptance and avoid conflict with teammates. We tend to adopt the group's prevailing view when evaluating information in unclear situations.
• Information familiar to multiple team members becomes socially validated and more likely to be repeated and affirmed.

Keep Exploring!!!

Bias and AI

Comparing Human and Machine Bias in Face Recognition

Key Notes

• Disparities between groups of people based on perceived gender, skin type, lighting condition
• poor light exposure, blurriness, facial obstruction

A survey on bias in visual datasets

• Selection bias is the type of bias that “occurs when individuals or groups differ systematically from the population of interest
• We refer to framing bias as any associations or disparities that can be used to convey different messages and/or that can be traced back to the way in which the visual content has been composed.
• We define label bias as any errors in the labelling of visual data, with respect to some ground truth, or the use of poorly defined or inappropriate semantic categories.

Keep Checking!!!

Weekend lessons - Bias and Fairness

Key Lessons

• What is Algo Bias
• How we can identify Bias / Mitigate Bias

• Appreciate and recognize this severity
• Image - Watermelon

• Based on culture, we may be inbuilt perceptions
• Categorize, simplify, general representations

• Sources of Algo Bias
• Facial Bias across demographics
• Age Detection - Performed worst on darker females

• Different cultures different interpretations

• Object recognition

• Bias correlation with Income and Geography
• World population vs dataset distribution

• Types of Bias in Deep Learning Systems
• Data does not include all representations
• Data is not real-world scenarios
• General conclusions

Interpretation Driven

• Trends in two variables
• cs graduates PhD trend
• unrelated correlations

• Does not capture fundamental driving force
• Overgeneralization
• Different perspectives

• The improved dataset that accounts distribution 16

• Procuring data of only certain situations
• Not covering complete 100% options

Class/ Feature Imbalances in Data

• Real-world distribution vs Model distribution
• Frequency in dataset vs real world

• Binary classification class
• Moving decision boundary
• Decision boundary shifts due to class imbalance

• Cancer from medical images MRI Scan

Mitigation Techniques

1. Select and Feed-in batches of class balance
2. During learning, they will see equal distributions
3. Reasonable decision boundary

• Weight likelihood of individual data points for training
• More frequent - lower weight
• Less frequent - Higher weight
• Inverse of frequency 

• Lack of diversity in feature spaces
• Hair color of images

1. Ground truth distribution of hair color

2. Ground truth distribution of Lip stick

3. Ground truth distribution of Face type

4. Ground truth distribution of Skin color

• Bias exists in commercial-grade systems

Improve Fairness

• Bias Mitigation
• Bias model dataset learning pipeline

• Evaluate Bias / Fairness
• Fair with respect to variables when conditioned

• Multitask learning / Adversarial Training
• Start by specifying the attribute
• Train model to jointly predict output

• Skin color, pose, illumination
• VAE to learn the underlying distribution
• Find the distribution of latent variables

• Approximate distribution by histogram
• Estimated joint distribution
• Adaptive Adjustment of Resampling probability
• Distribution of dark vs light skin tone distribution

Ref - Link

Happy Learning!!!