Nancy Torres's Profile
To your first question, I think there are two measures that can be taken to analyze and combat the level of bias in PredPol’s algorithm. One is to conduct controlled experiments comparing the algorithm to a control as well as running simulations to assess hypothetical Type I and Type II errors that may result from the model. Upon detecting what may be some blind spots of the algorithm, human intervention protocols can be incorporated to confirm that the appropriate decisions are being made. Ultimately, given the bias inherent in policing data and the likelihood that it would result in the disproportionate policing of low-income communities and communities of color, both rigorous experimentation and a specific and effective human-machine partnership is necessary to ensure its longer term success.
I think one thing Spotify can do to promote creativity among developers of podcasts is to intentionally scout talent from different sources and give talent access to resources to create production quality content. YouTube has taken this approach by having employees who look for fresh talent as well as setting up studios where YouTube artists can film. There are also numerous gatherings today for the creator community. If Spotify doesn’t already have a presence at the events and in these partnerships, it should begin to form it. To the second question, I think Spotify in the long term will lose if its algorithms don’t promote diverse artists and podcasters. Spotify needs to able to adapt to changing demographics in the U.S. and around the world. Thus, it is not so much a responsibility for them to design inclusive algorithms, but a business imperative.
Your questions regarding the nature of privacy and data sharing as it relates to genomics research apply to many other sectors across industries leveraging machine learning models as well. In these instances, I do think the regulatory landscape play an increasingly important role here. As data and safety becoming increasingly intertwined, protecting the user (in whatever context that means) is critical and should be a priority of government. Another thought to consider is a decentralized future in which each individual owns their own data and can choose to sell it to different entities for a specific amount under certain conditions. With the rise of emerging technologies such as artificial intelligence and blockchain, other startups have started to focus on this topic. I would be curious to see its application in genetic data-sharing.
So interesting! To your first question, I think measuring the effectiveness of proposed solutions will require an assessment of the various short- medium- and long-term metrics that predict success. This will ultimately require gathering large amounts of data and applying different models. Interestingly, machine learning can and should play a big role in this exercise. Navient has a unique opportunity to get a rich training data set to analyze outcomes. One the second question, perhaps Navient could also use an open-source model with sharing best practices. This might include round tables, meet-ups, inbound marketing (e.g., blogs, webinars) to share best practices, and perhaps even form an interactive online community to continue crowdsource efforts through a different medium.
To your questions, it would be interesting to see as the future of work becomes more distributed (i.e., talent is not centralized within one organization and everyone is seen more as a contractor), if individuals with the skillsets needed for Citywide Analytics are able to perform this data science and engineering work sustainably for government without City leaders needing to actively attract and retain. I would be curious to see if the City can create a payment model for this type of contract work. While the 4-year horizon does constrain City leaders, more open innovation can hold new leaders more accountable because now they are part of and respond to a larger network (versus being more insular in previous development models).