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5 recommended free AI-Ethics online courses to get started right away
AI ethics is becoming more and more important. Discussions about AI Ethics are still mostly conducted in academic circles. But one can already see that many companies are seriously dealing with it. One thing that seems clear to me:
Graduates of philosophy and ethics will be in high demand in the future to investigate AI-related processes through a human lens.
(taken from the article: Do Companies need a Chief AI-Ethics Officer?)
Below are five recommended online courses on AI Ethics.
1. Ethics of AI (University of Helsinki)
The Ethics of AI is a free online course created by the University of Helsinki. The course is for anyone who is interested in the ethical aspects of AI — we want to encourage people to learn what AI ethics means, what can and can’t be done to develop AI in an ethically sustainable way, and how to start thinking about AI from an ethical point of view.
Chapter 1: What is AI ethics? – What does AI ethics mean and what role do values and norms play? We’ll also look at the principles of AI ethics that we will follow in this course.
Chapter 2: What should we do? – What do the principles of beneficence (do good) and non-maleficence (do no harm) mean for AI, and how do they relate to the concept of the “common good?
Chapter 3: Who should be blamed? – What does accountability actually mean, and how does it apply to AI ethics? We’ll also discuss what moral agency and responsibility mean and the difficulty of assigning blame.
Chapter 4: Should we know how AI works – Why is transparency in AI important and what major issues are affected by transparency — and what are some of the risks associated with transparency in AI systems?
Chapter 5: Should AI respect and promote rights? – What are human rights, and how do they tie into the current ethical guidelines and principles of AI? We’ll also look more closely at three rights of particular importance to AI: the right to privacy, security, and inclusion.
Chapter 6: Should AI be fair and non-discriminative – What does fairness mean in relation to AI, how does discrimination manifest through AI — and what can we do to make these systems less biased?
Chapter 7: AI ethics in practice – What are some of the current challenges for AI ethics, what role do AI guidelines play in shaping the discussion, and how might things develop in the future?
Course: Ethics of AI
2. AI-Ethics: Global Perspectives (aiethicscourse.org)
Data-intensive and AI-based technologies can solve the world’s biggest challenges, but they also pose risks to individuals and groups. As we deploy new technology, we must consider ethical ramifications of AI use to identify and rectify harms.
This course is designed to raise awareness of the societal impacts of technology and to give individuals and institutions the tools to pursue responsible AI use. Intended for current and future data scientists, policymakers, and business leaders, this course contains 19 modules on topics related to artificial intelligence. Each module consists of a video lecture accompanied by additional resources such as podcasts, videos, and readings.
Course: AI Ethics: Global Perspectives
3. AI Ethics for Business (Seattle University)
This course is meant as an introduction to the ethical dimension of the uses of AI technologies. Upon successful completion of the course, students will 1) be sensitive to ethical issues surrounding transformative technologies, and 2) be able to articulate possible courses of action when there are ethically sensitive issues.
Mod 0 — Course Orientation
Mod 1 — Values and Moral Excuses in AI Technologies
Mod 2 — Transformative Technologies and their Impacts
Mod 3 — Users of Technology
Time commitment: 6–10 hours
Course: AI Ethics for Business
4. Bias and Discrimination in AI (Université de Montréal)
Discover how even computer algorithms may be biased and have a serious impact on our every day lives. In this MOOC, based on an IVADO School involving various international experts in the field, you will learn how to identify and alleviate bias and discrimination in Artificial Intelligence.
Engage in this course pertaining to a highly impactful yet, too rarely discussed, AI-related topic. You will learn from international experts in the field, also speakers at IVADO’s International School on Bias and Discrimination in AI, which took place in Montreal, and explore the social and technical aspects of bias, discrimination and fairness in machine learning and algorithm design.
The main focus of this course is: gender, race and socioeconomic-based bias as well as bias in data-driven predictive models leading to decisions. The course is primarily intended for professionals and academics with basic knowledge in mathematics and programming, but the rich content will be of great use to whomever uses, or is interested in, AI in any other way. These sociotechnical topics have proven to be great eye-openers for technical professionals! (Université de Montréal)
What you’ll learn
- Understanding bias and discrimination in all its aspects
- Exploring the harmful effects of bias in machine learning (discriminatory effects of algorithmic decision-making)
- Identifying the sources of bias and discrimination in machine learning
- Mitigating bias in machine learning (strategies for addressing bias)
- Recommendations to guide the ethical development and evaluation of algorithms
Course: Bias and Discrimination in AI
5. Data Science Ethics (University of Michigan)
Learn how to think through the ethics surrounding privacy, data sharing, and algorithmic decision-making.
This course focused on ethics specifically related to data science will provide you with the framework to analyze these concerns. This framework is based on ethics, which are shared values that help differentiate right from wrong. Ethics are not law, but they are usually the basis for laws.
Everyone, including data scientists, will benefit from this course. No previous knowledge is needed.
What you’ll learn
- Who owns data
- How we value different aspects of privacy
- How we get informed consent
- What it means to be fair
Course: Data Science Ethics
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