I was recently asked to moderate an academic panel on the role of universities in training the data science workforce. I preceded each question with opinionated introductions which I have fused into this blog post. These are weakly held opinions so please consider commenting if you disagree with anything.
To discuss data science education we first need to clearly state what it means. The panel organizers defined data science as “an emerging discipline that draws upon knowledge in statistical methodology and computer science to create impactful predictions and insights for a wide range of traditional scholarly fields.“ But is it an academic discipline? If so, what are the shared fundamental principles, expertise, skills, and knowledge-based shared by data scientists? Is there a core curriculum for Data Science? Providing a more detailed definition might help. Read More
Daily Archives: April 22, 2019
The Growing Marketplace For AI Ethics
As companies have raced to adopt artificial intelligence (AI) systems at scale, they have also sped through, and sometimes spun out, in the ethical obstacle course AI often presents.
AI-powered loan and credit approval processes have been marred by unforeseen bias. Same with recruiting tools. Smart speakers have secretly turned on and recorded thousands of minutes of audio of their owners.
Unfortunately, there’s no industry-standard, best-practices handbook on AI ethics for companies to follow—at least not yet. Some large companies, including Microsoft and Google, are developing their own internal ethical frameworks.
A number of think tanks, research organizations, and advocacy groups, meanwhile, have been developing a wide variety of ethical frameworks and guidelines for AI. Below is a brief roundup of some of the more influential models to emerge—from the Asilomar Principles to best-practice recommendations from the AI Now Institute. Read More
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