presentation on machine learning

1. Unpacking AI Bias: Towards
Fairer Intelligent Systems
Artificial Intelligence (AI) is reshaping industries and daily life, yet embedded
biases risk perpetuating inequality. This presentation explores the origins of AI
bias, its impacts, detection methods, mitigation strategies, and ethical futures to
foster fair, responsible AI systems.

2. What is AI Bias? A Critical
Introduction
AI bias occurs when algorithms produce systematically prejudiced results due to
flawed assumptions or data imbalances. Bias reflects and amplifies human
prejudices, potentially disadvantaging groups by ethnicity, gender, age, or
socioeconomic status. Understanding bias enables critical scrutiny of AI’s fairness
and inclusivity.
Recognising bias requires awareness that AI models learn patterns from existing
data which often encode societal inequalities, making bias an intrinsic challenge
in AI development.

3. Where Bias Begins: Data, Algorithms, and Human Factors
Data Bias: Collected data may be incomplete, unrepresentative or reflect past
discrimination.
Algorithmic Bias: Model design choices and training approaches can distort
outcomes.
Human Factors: Developers’ own implicit biases can influence AI objectives and
evaluation.

4. Bias in Action: Real-World
Consequences & Case Studies
Healthcare Disparities
AI tools underdiagnosing diseases in minority populations, worsening health gaps.
Hiring Algorithms
Recruitment AI favouring certain genders or backgrounds due to skewed training data.
Criminal Justice
Risk assessment models disproportionately flagging minorities for harsher sentencing.

5. Identifying Bias: Tools and Techniques for Detection
Detecting AI bias requires systematic testing with fairness metrics, including demographic parity, equal opportunity, and false
positive/negative rates. Tools like “AI Fairness 360” and “Fairlearn” help developers analyse and visualise bias to diagnose disparities early in
development.

6. Strategies for Mitigation: Building
Equitable AI
Inclusive Data Collection
Ensure diverse and representative data sets to reduce sampling bias.
Algorithm Adjustments
Use fairness-aware algorithms and bias correction techniques during training.
Human Oversight
Implement transparent review processes and multidisciplinary teams to catch hidden biases.

7. The Future of Fair AI: Ethics, Regulation, and
Responsibility
Ethical AI requires embedding fairness, accountability, and transparency principles into design and deployment. Governments and
organisations are increasingly introducing regulations to audit AI systems. Collaboration between technologists, ethicists, and policymakers
is vital to develop frameworks that safeguard against AI bias and uphold human rights.

8. Key Takeaways & Our Collective Path
Forward
AI bias stems from data, design, and human influence but can be mitigated with awareness
and tools.
Real-world impacts demand ethical responsibility and inclusive AI development.
Detection and correction tools empower fairness, but ongoing oversight and regulation are
essential.
Collective collaboration paves the way for AI that benefits all society equitably.

9. THANK YOU
MADE BY-
1.SUNIDHI MISHRA
2.SHAGUN MITTAL