Recent years have seen an eruption of concern about machine learning. When the systems we attempt to teach don’t do what we want or what we expect, ethical and potentially existential risks emerge. In this talk, Brian Christian discusses the inherent biases in algorithms and the varying complexities of the
Tag: biases
Implications Of The Biases That Have Shaped Society In The Past (Or Shape It Today), On How AI Systems Work – Or Fail To Work Followed By Q&A
The datasets on which AI technologies are trained to carry out a task reflect society, and can contain the biases that were embedded in processes, relationships, or structures at the point of data collection. When this data is used to develop AI, the resulting systems reflect back the social and
Algorithmic Biases: How An Algorithmic Tool Will Be Perceived And Create A Targeted Plan To Build Trust Among Targeted Users
While companies may spend a lot of time testing models before launch, many spend too little time considering how they will work in wild. In particular, they fail to fully consider how rates of adoption can warp developers’ intent. For instance, Airbnb launched a pricing algorithm to close the earnings
How To Avoid Exploiting Customers’ Behavioral Biases: Make Sure Your Digital Design Functions Are In The Best Interests Of Users
Executives must play a proactive role in making sure their digital design functions in the best interests of users. Doing so has the potential to give companies a deeper and more positive relationship with its customers. Brands that design their sites to exploit consumer behavioral bias (or those that fail
Can We Protect AI From Our Biases
As humans, we’re inherently biased. Sometimes it’s explicit and other times it’s unconscious, but as we move forward with technology how do we keep our biases out of the algorithms we create? Documentary filmmaker Robin Hauser argues that we need to have a conversation about how AI should be governed and ask who is responsible for overseeing the ethical standards of these supercomputers. “We need to figure this out now,” she says. “Because once skewed data gets into deep learning machines, it’s very difficult to take it out.”
