This lesson gives an in-depth introduction of ethics in the field of artificial intelligence, particularly in the context of its impact on humans and public interest. As the healthcare sector becomes increasingly affected by the implementation of ever stronger AI algorithms, this lecture covers key interests which must be protected going forward, including privacy, consent, human autonomy, inclusiveness, and equity.
This lesson describes a definitional framework for fairness and health equity in the age of the algorithm. While acknowledging the impressive capability of machine learning to positively affect health equity, this talk outlines potential (and actual) pitfalls which come with such powerful tools, ultimately making the case for collaborative, interdisciplinary, and transparent science as a way to operationalize fairness in health equity.
This lesson contains both a lecture and a tutorial component. The lecture (0:00-20:03 of YouTube video) discusses both the need for intersectional approaches in healthcare as well as the impact of neglecting intersectionality in patient populations. The lecture is followed by a practical tutorial in both Python and R on how to assess intersectional bias in datasets. Links to relevant code and data are found below.
This lecture provides an introduction to Plato’s concept of rationality and Aristotle’s concept of empiricism, and the enduring discussion between rationalism and empiricism to this day.
This lecture covers different perspectives on the study of the mental, focusing on the difference between Mind and Brain.
This lecture goes into further detail about the hard problem of developing a scientific discipline for subjective consciousness.
This lesson provides an overview of how to conceptualize, design, implement, and maintain neuroscientific pipelines in via the cloud-based computational reproducibility platform Code Ocean.
In this workshop talk, you will receive a tour of the Code Ocean ScienceOps Platform, a centralized cloud workspace for all teams.
This lesson provides an overview of how to construct computational pipelines for neurophysiological data using DataJoint.
This talk describes approaches to maintaining integrated workflows and data management schema, taking advantage of the many open source, collaborative platforms already existing.
This hands-on tutorial walks you through DataJoint platform, highlighting features and schema which can be used to build robost neuroscientific pipelines.
This lesson provides an introduction to the DataLad, a free and open source distributed data management system that keeps track of your data, creates structure, ensures reproducibility, supports collaboration, and integrates with widely used data infrastructure.
This lesson introduces several open science tools like Docker and Apptainer which can be used to develop portable and reproducible software environments.
This lecture provides a detailed description of how to incorporate HED annotation into your neuroimaging data pipeline.
This lesson gives a brief introduction to the course Neuroscience for Machine Learners (Neuro4ML).
This lesson covers the history of neuroscience and machine learning, and the story of how these two seemingly disparate fields are increasingly merging.
In this lesson, you will learn about the current challenges facing the integration of machine learning and neuroscience.
This lesson provides a short reel on who we are, what we're doing and why we're doing it.
In this webinar, educators currently implementing collaborative annotation in their classrooms discuss their experiences with collaborative annotation and using Hythothes.is and Canvas App.
This tutorial provides an overview of how to use the feature of Hypothes.is.