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Solidifying the impact of open data innovation in the government – NYC311
AA
Posted on November 13, 2018 at 7:28 pm
NYC311 has made great strides in evidencing the power of data analysis and innovation within the government, through opening up its data. Now it needs to build capacity in-house and answer tough questions to solidify the long-term impacts of open [...]

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On November 15, 2018, AA commented on Additive manufacturing – Will the world’s next mega city drop out of a 3D printer? :

I hear you loud and clear re: regulations. But why do you think these need to be regulated? Is there a risk of improper use in a manufacturing point of view? I think there are risks for home usage, but how is this conceptually different from simply building something in your home or in a company’s manufacturing plant? I would love to talk more about what you think here, in person.

On November 15, 2018, AA commented on Using Open Innovation to Solve the Ocean Plastics Problem :

Never heard of this initiative – so cool to read about. My question has to do around the operations involved in collecting the plastics from these impoverished areas. Crowdsourcing seems great when it’s digital or local in nature – in collecting ideas or solutions – and it also seems to solve for a lot of the business costs in reaching far. However, does The Plastic Bank lose these savings through having to invest in infrastructure to reach impoverished areas?

On November 15, 2018, AA commented on Challenge.Gov – A Model for Government Crowdsourcing :

Totally agree. We need to figure out how to get to the next level – both in terms of implementing the solutions, but also understanding how these solutions fit into the grand context of the government. I think, while crowdsourcing is a valuable tool to bring forth new ideas, we cannot run away from hiring technical talent in the government to internalize these ideas and prototypes.

On November 15, 2018, AA commented on Metal Additive Manufacturing at General Electric (GE) as future Competitive Advantage across Business Units :

Very cool! Are there capabilities in which additive manufacturing can solve for ‘big’ parts? This seems great at a small scale. But I wonder if GE indeed has identified enough use cases for this. Some of those may include ‘bigger’ parts. In general, I agree with GE’s strategy in investing in the technology of the future, and figuring out the concrete applications later, perhaps.

On November 15, 2018, AA commented on What’s your alibAI: rise of recidivism reducing robots :

The challenge with this is that we don’t know who are building this algorithms. With COMPAS, at least, we do not have an visibility into the types of machine learning algorithms being used. There is no transparency and accountability in these algorithms. Computers are not wise. Everything they know are taught by our biases. And I worry that if we don’t check our computers, just like we need to check ourselves for biases, then these might prove riskier than we thought.

On November 15, 2018, AA commented on Tala: Providing loans for those without a financial identity :

I love the work that Tala is doing. You list these two data sets that seem incredibly important, but I wonder how Tala can get this information from a person’s phone. What are the data sets that indeed can predict a debt to income ratio? And how can – I imagine, through text messages or data in other apps – Tala determine a client’s willingness to pay for something? I think to the interactions on my cell phone, and I am not sure how much detail I provide in terms of my debt or income. I suspect part of Tala being successful in this is being able to use the data to predict credit capacity and behavior well. Thus, are there other sources of data that Tala can collect from?

Credit capacity: What’s the client inferred debt to income ratio?
Behavioral data: What’s the client willingness to pay?