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July, 01 2021

Data Decentralization

Farmers could soon be hedging their risks with decentralized weather data

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What: Blockchain-based climate data marketplace dClimate is expected to launch this summer. With an automated quality score for each publisher, dClimate is looking to improve reliability for climate data, aiming to build an ecosystem where publishers are rewarded based on the quality of their data and predictions. 

Why it matters: Data reliability and ease of use are both key aspects of how any company uses data. dClimate is trying to fix that problem for climate data. Insurers of all sizes face the same problem with their data and need the technology in place to make it easy to get data in and out of their systems. 

Read: dClimate: a decentralized network for climate data
Arbol founders launch climate data marketplace

Data & Risk Mitigation

7 Major Cyber insurers form a data company to address cyber risks

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What: With data pooled from AIG, AXIS, Beazley, Chubb, The Hartford, Liberty Mutual Insurance and Travelers, CyberAcuView aims to help tackle the Cyber Insurance problem through better analysis and risk mitigation. 

Why it matters: Cyber is likely to be one of the dominant perils insurers are going to struggle with, and they’ll be looking for innovative advantages to manage loss ratios. A very logical first step is to pool data, and unbundling and specialization will further evolve insurer capabilities.

Read: 7 Major Cyber Insurers Form Company to Coordinate Cyber Analysis, Risk Mitigation

Artificial Intelligence

NIST looking to quantify consumers' trust in Artificial Intelligence

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What: AI is already transforming how consumers work and now the National Institute of Standards and Technology (NIST) is studying just how much consumers trust Artificial Intelligence.

Quote: “No longer are we asking automation to do human tasks, we are asking it to do tasks that we can’t. Moreover, AI has been built to dynamically update its set of beliefs (i.e. “learn”), a process that is not easily understood even by its designers. Because of this complexity and unpredictability, the AI user has to trust the AI, changing the dynamic between user and system into a relationship. Alongside research toward building trustworthy systems, understanding user trust in AI will be necessary in order to achieve the benefits and minimize the risks of this new technology.” 

Why it matters: Trust in AI determines how consumers interact with it, and building trust in AI tools that will deeply affect how consumers live is key to further development. AI ethics are fraught with challenges. Check out this video to see what we mean.

Read: Trust and Artificial Intelligence

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