wma:acm-athens-july2017
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| Entrambe le parti precedenti la revisioneRevisione precedenteProssima revisione | Revisione precedente | ||
| wma:acm-athens-july2017 [14/07/2017 alle 15:31 (7 anni fa)] – Dino Pedreschi | wma:acm-athens-july2017 [15/07/2017 alle 09:53 (7 anni fa)] (versione attuale) – [Lecture slides] Dino Pedreschi | ||
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| * Lecture 1: The architecture of complexity {{ : | * Lecture 1: The architecture of complexity {{ : | ||
| - | * Lecture 2: The power of complex networks {{ : | + | * Lecture 2: The power of complex networks {{ : |
| ===== Network data ===== | ===== Network data ===== | ||
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| ]] | ]] | ||
| + | ===== Networks for hands-on session ===== | ||
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| + | * {{ : | ||
| + | * {{ : | ||
| ===== Link to Social Network Analysis course at University of Pisa ===== | ===== Link to Social Network Analysis course at University of Pisa ===== | ||
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| Often, the achievements of data science are the result of re-interpreting available data for analysis goals that differ from the original reasons motivating data collection. Examples include mobile phone call records, originally collected by telecom operators for billing and operations, used for accurate and timely demography and human mobility analysis at country orregional scale. This re-purposing of data clearly shows the importance of legal compliance and data ethics technologies and safeguards to protect privacy and anonymity, secure data, engage users, avoid discrimination and misuse, account for transparency and fair use — to the purpose of seizing the opportunities of data science while controlling the associated risks. This is the focus of my lecture. | Often, the achievements of data science are the result of re-interpreting available data for analysis goals that differ from the original reasons motivating data collection. Examples include mobile phone call records, originally collected by telecom operators for billing and operations, used for accurate and timely demography and human mobility analysis at country orregional scale. This re-purposing of data clearly shows the importance of legal compliance and data ethics technologies and safeguards to protect privacy and anonymity, secure data, engage users, avoid discrimination and misuse, account for transparency and fair use — to the purpose of seizing the opportunities of data science while controlling the associated risks. This is the focus of my lecture. | ||
| - | ====== Syllabus | + | ===== Syllabus ===== |
| * Fairness, Accountability, | * Fairness, Accountability, | ||
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| * Algorithmic bias and ethical challenges of machine learning | * Algorithmic bias and ethical challenges of machine learning | ||
| * Discrimination-aware data mining | * Discrimination-aware data mining | ||
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| + | ===== Lecture slides ===== | ||
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| + | * Lecture 3: Data ethics & privacy-preserving analytics {{ : | ||
wma/acm-athens-july2017.1500046300.txt.gz · Ultima modifica: 14/07/2017 alle 15:31 (7 anni fa) da Dino Pedreschi