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Building Responsible | Ethical AI Systems-Risk of GEN AI

Name: Building Responsible | Ethical AI Systems-Risk of GEN AI
Rating: 3.9 (13 reviews)

Understand the risks associated with AI and how to navigate the ethical side when creating Generative AI Systems

Created byDan Andrei Bucureanu

Last updated 12/2024

English

English [Auto],

What you'll learn

Introduction to Artificial Intelligence
Learn the main capabilities of Chat GPT
Lean the main capabilities of Google Gemini
See real life examples where generative AI has biases
Ethical Considerations for Generative AI
Properties of Responsible AI Systems
General consideration on Ethical AI
Risk associated with AI systems
Understand how training data impacts AI ethical aspects
See live a framework that will test for toxicity and hate in Gen AI

Course content

7 sections • 56 lectures • 3h 54m total length

Introduction3:28
About your instructor2:00
Asimov's 3 Laws of Robotics - First regulation of AI4:00
Explore Asimov's three laws of robotics and the zero law, illustrating early efforts to standardize AI ethics while examining harm, obedience, and AI risks and biases.
What is GEN AI Used for1:59
Uncover how generative AI generates content with meaning and logic, not exact answers, across video, images, text, and stories, and why an ethical approach guides possibilities.
Demo On AI Capabilities4:27
Explore generative AI capabilities through practical demos with ChatGPT, Google Gemini, Adobe Firefly, and DALL-E, and discuss ethical and responsible AI use.
DEMO - Why we need ethical and responsible AI Systems4:53
Explore how biases arise in AI from training data, with examples of racial and gender bias, errors, and hallucinations. Learn why ethical, responsible AI governance is essential.
Poisoning Attacks8:31
Explore what poisoning attacks are, how corrupted data and backdoors undermine model outputs, and how red teams, data integrity, and data sanitization mitigate these threats.
Example Of Poisoning attack6:18
History of AI from 1950 to 20247:16
Trace the history of AI from the Turing Test and Dartmouth Conference to Eliza, Deep Blue, and AlphaGo, highlighting deep learning, ethical AI, and regulation.

What makes up AI3:47
Get a brief introduction on what are the main components of AI
Where do Large Language Models(LLM) fit into AI1:50
Understand how LLMS and foundation models are part of the AI Science field.
What are Tokens2:38
Natural Language Processing4:56
How NLP actually makes the AI more human.
Types of Machine Learning4:08
Understand what is machine learning and how algorithms make the core of AI
Machine Learning - Supervised ML4:01
Understand the basics concepts around supervised Machine Learning
Machine Learning - Unsupervised ML5:03
Gain basic understanding of Unsupervised ML and Clustering
Machine Learning - Reinforced ML3:20
In this lecture you will get a basic idea of how Reinforced Learning is working together with ML Algorithms
Neural Networks and Deep Learning5:26
In this material you will understand how critical good quality training data actually is.
Importance of Training Data4:09
In this material you will understand how critical good quality training data actually is.
Generative AI - What it is2:42
In this lecture we will put all the pieces together and explain what GEN AI actually is.

How Model Benchmarking Works3:13
Install Vs Code - a great IDE2:40
Install Python2:18
Install Python Dependencies - PIP2:07
Install python dependencies using pip by downloading get-pip.py, running python get-pip.py, and then using pip install to add modules like openai.
About Bias in Bios evaluation benchmark4:59
Model Selection and Download2:59
Understand BiasinBios Data structure4:03
Data Download and Tokenization3:06
Run untrained BERT Model Benchmark4:53
Run Fine Tuned Bert Model Benchmark7:40

EU AI Regulation3:48
The EU AI Regulation act and how the EU sees the future use of AI. -> https://www.globalcompliancenews.com/2024/02/03/https-insightplus-bakermckenzie-com-bm-technology-media-telecommunications_1-european-union-eu-reaches-landmark-deal-on-ai-regulation_01092024/#:~:text=In%20brief,use%20of%20AI%20in%20Europe.
Google Approach to Ethical AI3:07
Meta Approach to Ethical AI3:11
Explore Meta's responsible AI framework, grounded in five pillars—privacy, fairness and inclusion, robustness and safety, transparency and control, and accountability and governance—to benefit people and society.
Microsoft Approach to Ethical AI4:17
Understand how MSFT is approaching Responsible AI

https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE5dlCb?culture=en-us&country=us
https://www.microsoft.com/en-us/ai/principles-and-approach

Perspective Service - Bias Detection Service4:47
Understand what PerspectiveAPI can offer in matter in toxicity and censorship
Get a Perspective API Key6:26
Step by step demo how to obtain a ChatGPT API Key
What is a token in LLMs2:38
Understand that a token is a unit of text in large language models, which can be a character, a word, or a space.
Get an OPENAI API Key1:00
Obtain your API key, keep it secret as it’s tied to your account, and create a new secret for demos to use with all ChatGPT requests.
Installing Postman and first API Test4:42
Demo - VS Code - Call Perspective API3:24
See a live demo how we will implement an API call to Perspective API
Github Code: calculator/src/test/Perspecttive_api at master · danteachqe/calculator · GitHub
Demo - Python - Test AI Response against Perspective APIs5:06
Understand how Perspective API And ChatGPT work together to create an automated testing framework for Toxic content.

AI and Biases5:07
Can we truly eliminate Biases in AI?
GEN AI and Privacy4:09
In this lecture you will see some example of how modern AI Models have shown private data.
GEN AI and Intellectual Property4:18
Gen AI and Deep Fake6:33
See this Deep Fake Video of Obama -> https://ars.electronica.art/center/en/obama-deep-fake/
Hallucinations4:33
Explore AI hallucinations, their causes like data bias and insufficient training. Learn prevention by limiting outputs, training on relevant data, and using a double-check to verify information.
GEN AI and Fair Access to Information3:39
Gen AI and Misinformation or Disinformation4:50
Explore how unsupervised learning and association identify data patterns and generate frequently bought together recommendations. Analyze how such AI-driven suggestions can enable manipulation, misinformation, disinformation, and biases in recommender systems.
OPENAI-CHAT GPT Moderation Service5:42
Google Moderation Service4:20
Spot a Fake - Demo Chat GPT Watermark on Dall E2:57
Spot a fake—how OpenAI's Dall-E 3 watermark and content credentials verify tool reveal AI-generated images, and why watermarks can be removed or circumvented.
GEN AI and Manipulation4:05
Analyze manipulation in ai systems, from social engineering and Cambridge Analytics to fake ratings and bot reviews, and learn safeguards to prevent mass manipulation by gen ai.
Economic Impact of GEN AI3:52
Over Reliance on AI Content2:21
Testing the Ethical consideration for GEN AI9:23

Requirements

No previous experience required
Motivation to lean the hottest skill in 2024

Description

Course Overview:

This course provides an in-depth exploration of Artificial Intelligence (AI), fostering a critical understanding of both its transformative potential and associated ethical risks. It emphasizes responsible AI development while preparing participants to engage with AI technologies in a thoughtful and informed manner.

Course Objectives:

Acquire a solid foundation in Artificial Intelligence (AI) concepts and real-world applications.
Explore the capabilities of large language models (LLMs) such as ChatGPT and Google Gemini, gaining insights into their functionalities and inherent limitations.
Identify and critically evaluate the risks associated with AI, including bias, security vulnerabilities, and societal implications.
Develop a comprehensive framework for implementing responsible AI practices, with a strong emphasis on ethical considerations and mitigation strategies.
Learn how to connect with an API to evaluate and test for toxicity using ResponsibleAI tools, ensuring that AI systems meet ethical and safety standards.

Course Content:

Introduction to AI:

Demystifying foundational AI concepts, including machine learning, deep learning, and natural language processing.
Understanding AI’s pervasive influence across industries and sectors.

Unveiling Large Language Models (LLMs):

Exploring the functionalities of LLMs such as ChatGPT and Google Gemini, focusing on their applications in text generation, translation, and code creation.
Discussing the limitations and challenges of LLMs, particularly in specialized or nuanced contexts.

Navigating AI Risks:

Identifying potential biases embedded within AI algorithms and understanding their broader downstream impacts.
Examining security vulnerabilities, data privacy concerns, and ethical challenges in AI deployments.
Analyzing the societal implications of AI, including labor market disruption and ethical dilemmas in decision-making.

Building Responsible AI:

Exploring strategies to mitigate AI risks and promote fairness, transparency, and accountability in AI systems.
Learning how to test AI systems for toxicity using ResponsibleAI's API, with a focus on ensuring ethical AI usage and minimizing harmful outputs.

Target Audience:

This course is designed for:

Individuals with a general interest in the potential and challenges of AI.
Professionals seeking to deepen their understanding of the ethical risks surrounding AI development and deployment.
Developers and programmers interested in integrating responsible AI practices, particularly in addressing toxicity and bias in AI models.

Learning Outcomes:

By the end of this course, participants will have the skills and knowledge to navigate the evolving landscape of AI responsibly. They will also be equipped to test AI models for toxicity through APIs, ensuring ethical and responsible AI deployment.

Who this course is for:

Any person that want to have basic understanding of AI
Anyone that uses AI or is assisted by AI systems
Professionals involved in the creation of AI systems

Building Responsible | Ethical AI Systems-Risk of GEN AI

What you'll learn

Explore related topics

Course content

Introduction9 lectures • 43min

Introduction to Generative AI - a quick look under the hood.11 lectures • 42min

Python Framework for Evaluating Bias In Bios10 lectures • 38min

General Consensus on Regulating AI4 lectures • 14min

Toxicity | Hate and Ethical Testing Framework7 lectures • 28min

Risks associated with AI14 lectures • 1hr 6min

The end1 lecture • 3min

Requirements

Description

Who this course is for: