Promote the Ethical Use of Data-Driven Technologies
Certified Ethical Emerging Technologist,
The greatest risk in emerging technology is the perpetuation of bias in automated technologies dependent upon data sets. Solutions created with racial, gender or demographic bias, whether unintentional or not can perpetuate tragic inequities socially and economically. This is the first of five courses within the Certified Ethical Emerging Technologist (CEET) professional certificate and it is designed for learners seeking to advocate and promote the ethical use of data-driven technologies. Students will learn what emerging technologies are and how they can be used to create data driven solutions. You will learn types of bias and common ethical theories and how they can be applied to emerging technology, and examine legal and ethical privacy concepts as they relate to technologies such as artificial intelligence, machine learning and data science fields. Throughout the course learners begin to distinguish which types of bias may cause the greatest risk and which principles to apply to strategically respond to ethical considerations.
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Rating | Not enough ratings |
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Length | 7 weeks |
Effort | 6 weeks of study, 2-3 hours per week. |
Starts | Jul 3 (46 weeks ago) |
Cost | $49 |
From | CertNexus via Coursera |
Instructors | Tania De Gasperis, Megan Smith Branch, Aaron Hui, Eleanor 'Nell' Watson, Renée Cummings |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Art & Design Humanities Programming |
Tags | Data Science Arts And Humanities Machine Learning Philosophy |
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Rating | Not enough ratings |
---|---|
Length | 7 weeks |
Effort | 6 weeks of study, 2-3 hours per week. |
Starts | Jul 3 (46 weeks ago) |
Cost | $49 |
From | CertNexus via Coursera |
Instructors | Tania De Gasperis, Megan Smith Branch, Aaron Hui, Eleanor 'Nell' Watson, Renée Cummings |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Art & Design Humanities Programming |
Tags | Data Science Arts And Humanities Machine Learning Philosophy |
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