The document discusses the balance between transparency, trust, and regulation in artificial intelligence systems, highlighting challenges like algorithmic bias, accountability, and the importance of human oversight. It examines the implications of opaque algorithms and ethical considerations in AI development while advocating for transparency strategies such as open-source models and explainable AI. Furthermore, it emphasizes the need for regulatory frameworks and international cooperation to ensure ethical practices in AI deployment.

1. ARTIFICIAL INTELLIGENCE:
BALANCING
TRANSPARENCY, TRUST &
REGULATION

2. Goal
To find the equillibrium between transaparency, trust and regulation
in AI algorithmic systems
Content
• Transparency in AI developernt
• Trust in AI Systems
• Ethical AI
• Regulatory Perspective

3. Introduction
• AI is machines learning from data to mimic human
cognition.
• AI algorithms are everywhere today :- Augmenting
humans in virtually every aspect of life.
• Big Question: Where do we draw the line for AI as
developers, & Regulators.
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4. Case Study: Algocratic governance
(Algocracy)
• Case Management and legal decision support tool.
• Data – Defendant bio history, criminal record etc.
• What proponents say: “Hungry judge effect”.
• What Critiques Say: “Bias, Transparency, Accuracy, GIGA”
“Government by algorithm - usage of computer algorithms is applied to
regulations, law enforcement, and generally any aspect of everyday life
such as transportation or land registration” ~ Wikipedia
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Correctional Offender Management Profiling for Alternative Sanctions (COMPAS).

5. Bias in
algorithms
• Leading to discriminatory
outcomes.
Accountability
• Who is responsible when
AI fails ?
Hullucination
• DO AI systems know it all
or they make things up ?
Trust in AI systems
Factors affecting trust in AI
systems
• The goal is to enhance trust in AI systems by ensuring fairness, minimize bias, hullucination.
• Opaque algorithms disproportinately harm volunabrable communities.

6. Autonomy vs. Control
How much autonomy should AI have? Human
oversight is crucial in high-stakes areas like
criminal justice and healthcare.
Data Privacy
AI relies heavily on data, raising questions of
consent and privacy. Regulatory frameworks
like GDPR focus on ensuring users’ control over
their data.
Bias and Fairness
Training data often reflects existing societal
biases. For example, ProPublica’s analysis of
COMPAS, a criminal justice algorithm bias.
Ethical Challenges in AI Development
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7. Transparency in AI
• Key challenge: the “black box” problem - AI algorithms are opaque.
• Explainability Vs. Interpretability:
- Explainable AI (XAI) systems provide reasoning behind AI Decisions
* Intrinsic Explainability
* Post-hoc Explainability
- Feature Importance e.g. SHAP
- LIME (Local Interpretable Model-agnostic Explanations)
- Partial Dependence Maps (PDP)
- Saliency Maps
- Counter Factual Explanations
- Interpretability: Algorithmic decisions are understood by non-experts.
• Lack of Transparency can perpetuate biases, erode trust and fuel
social injustice
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8. 1.Open-Source Models
Make certain algorithms open-source to allow
public scrutiny, contribution and analysis e.g
Hugging Face models.
4. Explainable AI (XAI)
Embed explainability features so users
understand why an AI system made certain
decisions (e.g., Microsoft’s LIME framework).
Transparency Strategies for AI Developers
2. Data Transparency
• Data Provenance: Document the source,
collection methods and quality of data used
in AI models.
• Data Privacy: Robust data privacy measures
to protect user data, comply with
regulations.
3. Model Transparency
• Model Documentation: Document model architecture,
training process and evaluation metrics.
• Model Interpretability: Techniques like saliency maps,
feature importance to help users decipher model
decisions.
• Model Auditing: conduct model audits for re-assuarance

9. Human-in-the-Loop
Ensure human oversight in critical decision-making
systems (e.g., autonomous driving, criminal
sentencing algorithms).
Communication and Education
• Public Engagement: Train, Engage stakeholders including
policymakers and the public to build trust and address
concerns.
• Transparency Reporting: Regularly report on AI
development efforts, including progress, challenges, and

10. Algorithmic
Accountability
• Algorithmic Impact Assessments
• Data Protection and breach notification.
• Bias Mitigation
• Algorithm Audits
Consumer Protection
• Explainable AI for users to make right
decision.
• Consumer rights to be informed and
challenge AI decisions.
• Prohibit Algorithmic Discrimination
Regulatory Perspective: Ensuring Ethical & Transparent AI
International Cooperation
• Global Standards: Develop global standards
for AI development and use.
• Data Localization: Address Cross boarder
data movement in the context of AI
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11. “The real risk with AI isn’t that it
will become super-intelligent and
overthrow us, but that it will be
programmed with goals
misaligned with ours.”
Conclusion
Stuart Russell, Human Compatible: Artificial Intelligence and the Problem of
Control

12. THANK
YOU
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