Lectures


Lecture 1: Deep Learning in Science 1/3

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Lecture 2: Deep Learning in Science 2/3

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Lecture 3: Deep Learning in Science 3/3

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Lecture 1: Is hiding fair?

Violations of privacy as well as unfairness and discrimination have been highlighted as two of the biggest ethical challenges for AI. At the same time, computer scientists have proposed a large number of methods for enhancing (data) privacy and fairness. Can these strategies support one another, or is there a tradeoff between privacy and fairness? And how can interdisciplinary perspectives inspire, enhance or correct computational ones? In this talk, I will present and discuss several answers that have been given to this question and discuss the assumptions that lead to “support” or “tradeoff” results. Specific attention will be given to the use of obfuscation for enhancing fairness, and the larger question of whether, when or how information hiding is fair (or not).

Lecture 2: Is hiding fair?

Violations of privacy as well as unfairness and discrimination have been highlighted as two of the biggest ethical challenges for AI. At the same time, computer scientists have proposed a large number of methods for enhancing (data) privacy and fairness. Can these strategies support one another, or is there a tradeoff between privacy and fairness? And how can interdisciplinary perspectives inspire, enhance or correct computational ones? In this talk, I will present and discuss several answers that have been given to this question and discuss the assumptions that lead to “support” or “tradeoff” results. Specific attention will be given to the use of obfuscation for enhancing fairness, and the larger question of whether, when or how information hiding is fair (or not).

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Lecture 1: Knowledge Processing, Logic, and the Future of AI (1/2)

Nowadays, when people speak about AI, they usually mean machine learning. Machine learning, in particular, deep learning, is a  powerful method for generating a type of knowledge that could be classified as self-learned knowledge. We humans, on the other hand, make heavy use of two types of knowledge: (i) self-learned knowledge and (ii) transferable knowledge learned or generated by others. If you read this and/or attend the talk, this is  mainly because of this second type of Knowledge. In this talk, I will argue that the combination of both types of knowledge is needed for more powerful and fair automated decision making or decision support,  and thus for the next level of AI. I will discuss various requirements for reasoning formalisms towards this purpose.  After discussing logical languages for knowledge-representation and reasoning, I will briefly  introduce the VADALOG  system developed at Oxford and give an outlook on my recent project RAISON DATA funded by the Royal Society.

Lecture 2: Knowledge Processing, Logic, and the Future of AI (2/2)

Nowadays, when people speak about AI, they usually mean machine learning. Machine learning, in particular, deep learning, is a  powerful method for generating a type of knowledge that could be classified as self-learned knowledge. We humans, on the other hand, make heavy use of two types of knowledge: (i) self-learned knowledge and (ii) transferable knowledge learned or generated by others. If you read this and/or attend the talk, this is  mainly because of this second type of Knowledge. In this talk, I will argue that the combination of both types of knowledge is needed for more powerful and fair automated decision making or decision support,  and thus for the next level of AI. I will discuss various requirements for reasoning formalisms towards this purpose.  After discussing logical languages for knowledge-representation and reasoning, I will briefly  introduce the VADALOG  system developed at Oxford and give an outlook on my recent project RAISON DATA funded by the Royal Society.



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