LEARNER: Addressing Bias in AI Hiring System Policies

LEARNER: Addressing Bias in AI Hiring System Policies

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Inclusive Design Research Centre

wecount@inclusivedesign.ca

The Inclusive Design Research Centre is an international community of open source developers, designers, researchers, educators and co-designers who work together to proactively ensure that emerging technology and practices are designed inclusively. Learn more at idrc.ocadu.ca

Tags:

Artificial-Intelligence, Bias, EDI-principles, Machine-Learning, WeCount, Word-Embedding
The Future of Work and Disability Project of the Inclusive Design Research Centre (IDRC) examined the barriers and opportunities that artificial intelligence (AI) and other “smart” technologies present for persons with disabilities (PWD) in the sphere of employment. The learner of this badge has understood and gained insight into how machine learning algorithms within workforce hiring software tools are biased and can adversely affect persons with disabilities in the job application process. The learner focused on many legal and ethical implications of machine learning bias, explored best practices and policies that ought to be adopted by technology vendors in designing their algorithms and the employment organizations that use them. The learner has a preliminary understanding of the policy issues at stake within this area of algorithmic bias. The learner viewed a webinar on identifying and addressing bias in machine learning models on selection of candidates from a policy perspective, the risks and opportunities of artificial intelligence for persons with disabilities, delivered by Alexandra Reeve Givens (CEO of the Center for Democracy & Technology), Julia Stoyanovich (Assistant Professor of Computer Science and Engineering and of Data Science at New York University), and Vera Roberts (Senior Manager at IDRC). A summative assessment, in the form of a knowledge recall quiz including distinguishing terminology, frameworks, and relevant use, was completed, and then evaluated by a qualified We Count assessor. Estimated learning time including assessment: 2- 4 hours.

For more information, please visit We Count’s Home Page.


This Learner assessment consists of 5 short-answer questions based on the content of We Count’s AI Hiring System Policies webinar. The reflection questions will allow you to connect what you have learned from your own experiences and the We Count initiatives.

This assessment is not time-restricted, featuring an option at the bottom of the form for it to be saved and continued later. Remember to read through all of the questions carefully, and to take your time.

The average learning time for this badge is 2-4 hours.


100-200 words
100-200 words
100-200 words; Hint: You can re-visit the webinar from 17:00 to 23:00 to help you answer this question.
100-200 words; Hint: You can re-visit the webinar from 36:00 to 46:00 to help you answer this question.
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