Data Ethics, AI and Responsible Innovation
Become an ethical AI practitioner by developing the ability to identify and resolve ethical challenges in AI and data science initiatives.
Description for Data Ethics, AI and Responsible Innovation
Critical Issues in Data Lifecycle: Understand the critical, social, legal, political, and ethical issues that arise throughout the data lifecycle.
Key Ethical Concepts: Gain an understanding of the concepts of ethics, morality, responsibility, digital rights, data governance, and human-data interaction in the context of data practices.
Ethical Issues in Data Science: Apply critical judgment to identify and assess the current ethical challenges in the data science and industry.
Ethically Driven Solutions: Ensure responsible decision-making by developing and applying ethically driven solutions to moral problems in your professional practice.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 1
Offered by: On edX provided by EdinburghX
Duration: 3�4 hours per week approx 5 weeks
Schedule: Flexible
Pricing for Data Ethics, AI and Responsible Innovation
Use Cases for Data Ethics, AI and Responsible Innovation
FAQs for Data Ethics, AI and Responsible Innovation
Reviews for Data Ethics, AI and Responsible Innovation
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Data Ethics, AI and Responsible Innovation
Learn to apply advanced machine learning and deep learning models to real-world challenges by immersing yourself in the cutting-edge world of AI-powered finance and insurance.
Gain extensive knowledge in AI technologies relevant to digital marketing, involving precise data analysis, content creation, and tools for optimizing social media and consumer segmentation.
Examine how to improve learning and preserve integrity by incorporating morally sound and useful AI tools into evaluation procedures.
Acquire practical expertise in the integration of machine learning models into pipelines, optimizing performance, and efficiently managing versioning and artifacts.
Study the ethical consequences of AI development and implementation, emphasizing generative AI, AI governance, and pragmatic ethical decision-making in practical contexts.
In this course, students gain the skills necessary to use Python for data science, machine learning, and foundational applications of artificial intelligence.
Gain an extensive understanding of TinyML applications, fundamental principles, and the ethical development of artificial intelligence.
A thorough grasp of artificial intelligence (AI) and machine learning, including its various forms, methods, and applications, is given in this course.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
From fundamental concepts to advanced methods such as deep learning and ensemble techniques, this program provides a comprehensive examination of machine learning techniques.
Featured Tools
This program instructs instructors on the ethical and successful integration of AI, while promoting innovation and critical thinking among students.
In order to balance or improve the integration of AI in education, this course examines conversational AI technologies and provides evaluation designs.
A practical guide to the use of generative AI for the purpose of composing, refining, and planning, utilizing structured and context-driven inputs.
The material equips data engineers to incorporate machine learning models into pipelines while adhering to best practices in collaboration, version control, and artifact management.
Explore the topic of AI-powered personalization by acquiring the skills necessary to utilize LangChain and ChatGPT.