In the coming months, we will have a searchable resource library to support your learning. Until then, here are a few items to get you started.
AI and disability, small minorities and outliers
A discussion of the challenges persons with disabilities face with current AI systems and the approaches that need to be adopted to ensure fairness in AI development.
A report capturing critical themes and discussions from a workshop at the AI Now Institute at New York University, the NYU Center for Disability Studies.
Identifies how several AI technologies, such as automated speech recognition tools and language prediction algorithms, may not be useful for persons with disabilities and may discriminate against them.
Work for people with disabilities in data science
A joint publication by Fundación ONCE and the ILO Global Business and Disability Network to connect different areas of debate, looking at the key trends of the future of work from a disability perspective and seeking to identify specific action needed in order to shape the future of work in a more disability-inclusive way.
AI ethics and policy
Using survey data from 211 software companies, this article provides needed insight into the current state of AI ethics in the data industry. For practitioners, the data can also serve as a way to benchmark where an organization stands.
An overview of ethical issues in AI, including privacy, transparency and bias, and what steps need to be taken to address these issues.
Technical information (for AI experts)
The WeBuildAI team designed, applied and evaluated this social participatory framework for engaging community stakeholders to enable people to create a decision-making algorithm that fits their needs.
A YouTube session from Google I/O ’19 that explains machine learning to those with coding experience, including a discussion of the image classification problem and the use of Tensor flow.
A YouTube video from Google I/O ’19 on machine learning fairness, with examples of lessons learned through their products and research and describes techniques, that enables developers to think proactively about fairness in product development.
From Google PAIR, a look at how Google is using Tensorflow to address the issue of fairness in machine learning.
An argument that traditional statistical methods were developed for small data sets and are not suitable for current large and complex data sets.