As principal architect of ethical AI practice at Salesforce, Kathy develops research-informed best practice to educate Salesforce employees, customers, and the industry on the development of responsible AI. She collaborates and partners with external AI and ethics experts to continuously evolve Salesforce policies, practices, and products. Prior to Salesforce, she worked at Google, eBay, and Oracle in User Experience Research. She received her MS in Engineering Psychology and BS in Applied Psychology from the Georgia Institute of Technology. The second edition of her book, "Understanding your users," was published in May 2015. You can read about her current research at einstein.ai/ethics.
If you are thinking about incorporating ethics into your company's culture or product development cycle, check out this list of dozens of ethical tools before you try recreating the wheel.
You may be the first person in your company focusing on the need to create or implement ethical AI. If so, I’d like to share some lessons learned that could help you in your work.
On December 19th, 2018, 2 dozen women working on AI ethics in tech companies, non-profits, and industry analysts came together for a day to share their experiences and insights, as well as to brainstorm solutions to big challenging we are facing.
This post is for those responsible for implementing AI systems but do not have a background in data science, statistics, or probability. The intention is to create an approachable introduction to the key concepts to identify potential bias (error) in their training data.
What kind of world do we want for ourselves, those we love, and future society? How do we are organizations, employees, customers, and members of society ensure that technology is in service of society and not the other way around?
