AI Ethics: How to Use Artificial Intelligence Responsibly
AI Ethics: How to Use Artificial Intelligence Responsibly
Skills you’ll gain
Harness the capabilities of AI responsibly with Brett Vanderblock in this comprehensive course on Generative AI Business Ethics.
Brett will guide you through the ethical considerations and challenges of using AI for business applications. Learn to identify and mitigate biases, understand the importance of transparency, and establish accountability in AI-driven processes. Brett’s real-world examples and case studies will help you grasp the potential consequences of unethical AI usage.
Upon completion, you’ll be equipped with the knowledge and best practices to ensure that your business's AI applications are ethical, fair, and aligned with societal values.
Syllabus
Download syllabus-
1
Demystify AI Ethics Business ethics focuses on balancing economic objectives with social responsibility and ethical considerations. After this lesson, you'll be able to define ethics as it relates to business, and you'll be able to identify important aspects of business ethics. 3m
-
2
What is generative AI? When beginning to consider the ethics involved with using generative AI, it's important to have a base level understanding of generative AI, such as what large language models are, how to train large language models, as well as applications and limitations of generative AI. After this lesson, you'll be able to define large language models, describe how they are trained, identify what they can and cannot do, and recognize ways they are already being integrated into society. 2m
-
1
Consider implications of AI automation Automating processes and tasks is one of the main functionalities of generative AI that users leverage. After this lesson, you'll be able to consider the implications for self and society of the automation capabilities of generative AI, and you'll be able to identify some of the positive and potentially negative side effects these automation capabilities pose. 2m
-
2
Promote data privacy and security Data privacy and security are vital in the context of generative AI to protect sensitive information, prevent fraud, safeguard intellectual property, maintain user trust, comply with regulations, and mitigate bias. After this lesson, you'll be able to identify and apply strategies to avoid data privacy concerns with large language models, and you'll be able to describe how user-provided data could be misused by large language models. 2m
-
3
Honoring intellectual property rights The intersection of AI and copyright is a complex maze. In this lesson, you'll explore the challenges of copyright infringement and generative AI models. After this lesson, you'll recognize the need to safeguard against potential copyright issues when using generative AI in your organization. 3m
-
4
Value transparency in GenAI When it comes to generative AI, there are many interconnected layers that work together behind the scenes in order to present the information to the user. After this lesson, you'll be able to describe how to measure the transparency of an AI tool. 2m
-
5
Open source vs proprietary models Knowing the similarities and differences between open source and proprietary AI models can help you better recognize the potential benefits and drawbacks of each type. After this lesson, you'll be able to compare and contrast open source and proprietary models, and you'll be able to consider potential ethical concerns for both types of models. 3m
-
6
Other impacts to consider As generative AI continues to evolve, additional ethical implications will continue to arise. After this lesson, you'll be able to describe additional areas of ethical implications, such as the environmental impact of training and using large language models, intellectual property rights concerns, and misrepresentation of knowledge. 2m
-
1
Considering compliance and regulation Although you do have some control over your ability to effectively mitigate the ethical considerations of using generative AI, many of these considerations are also beginning to work their way into the more collective areas of regulation and compliance. After this lesson, you'll be able to identify some of the emerging trends in AI compliance and regulation, and you'll be able to recognize that not all ethical concerns are to be solved by you as an individual, but rather collectively as a society. 2m
-
2
Follow legislation and evolving law As artificial intelligence continues to evolve, legislation around artificial intelligence will also continue to change. After this lesson, you'll be able to identify ways that you can access up-to-date information regarding relevant generative AI law and corresponding ethical implications. 2m
-
1
Form your AI council Once you've built a fundamental understanding of generative AI ethical considerations in a business setting, it's important to begin reflecting on how you'd like to establish an AI ethic for both yourself and your organization. After this lesson, you'll be able to establish an AI ethic for yourself, and you'll be able to kick off and/or participate in an organization-wide exercise to establish an AI ethic. 1m
-
2
Put your AI council to work In order to promote responsible AI usage within your organization, it's important to map out use cases for AI that are relevant to you, your role, and your organization. After this lesson, you'll be able to identify use cases for AI, either with existing vendors or new product offerings. 3m
-
3
Sustain your AI ethic Once you've begun using generative AI across your organization, it's important to have systems in place to ensure generative AI is continuing to be used responsibly and ethically. After this lesson, you'll be able to describe the importance of monitoring how generative AI is used within your organization, and you'll be able to describe some methodologies, tools, and frameworks for ensuring responsible and sustainable generative AI usage. 2m
-
1
Next steps Thank you for watching this course! Now that you've completed this course, you're ready to begin reaping the benefits of generative AI in a business setting while implementing and following thoughtful ethical considerations and guidelines that will help you and your organization use AI responsibly. 1m
Certificate
Certificate of Completion
Awarded upon successful completion of the course.
Instructor
Brett Vanderblock
Brett Vanderblock is a data scientist with Patagonia and co-founder of Think Fast Analytics. He has proven experience surfacing insights across industries, from start-ups, state and local governments, higher education, healthcare, to retail. He is always furthering his deep expertise in machine learning, data visualization, and data pipelines. An educator at heart, Brett enjoys providing "data therapy" to enable others to realize the benefits of their data through self-service analytics.
Brett Vanderblock
Data Scientist
Accreditations
Link to awards
How GoSkills helped Chris
I got the promotion largely because of the skills I could develop, thanks to the GoSkills courses I took. I set aside at least 30 minutes daily to invest in myself and my professional growth. Seeing how much this has helped me become a more efficient employee is a big motivation.
