- Big Data Desperately Needs Transparency - Mar 6, 2017.
If Big Data is to realize its potential, people need to understand what it is capable of, what information is out there and where every piece of data comes from. Without such transparency and understanding, it will be difficult to persuade people to rely on the findings.
- Cooperative Trust Among Neural Networks Drives Deeper Learning - Feb 28, 2017.
Machine learning developers need to model a growing range of multi-partner scenarios where many learning agents and data sources interact under varying degrees of trustworthiness. This IBM site helps to take next step towards continuous intelligence.
- Machine Learning Meets Humans – Insights from HUML 2016 - Jan 6, 2017.
Report from an important IEEE workshop on Human Use of Machine Learning, covering trust, responsibility, the value of explanation, safety of machine learning, discrimination in human vs. machine decision making, and more.
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- How Much Will A.I. Surprise Us? - Jun 15, 2016.
Why think about what neural networks (and AI in general) can do that we can already do, when he real question that we should be asking is this: What will A.I. be able to do that we can’t even dream of?
- Trust and Analytics in the Banking Sector - May 26, 2016.
This post explores the intricate relationship between customers, trust, and analytics in the banking sector, and offer actions that banks may need to take to assess the way they assure trust across the analytics lifecycle.
- The Anchors of Trust in Data Analytics - Mar 14, 2016.
An exploration of some of the critical questions and challenges emerging around trust in data and analytics. The four anchors of trust that will shape public confidence in D&A in the age of the analytical enterprise are highlighted.
- The Perpetual Quest for Digital Trust - Jul 22, 2015.
Digital Trust is at a deficit – concludes the 2015 Accenture Digital Consumer Survey report “Digital Trust in the IoT Era”
- Innocentive: INSTINCT – The IARPA Trustworthiness Challenge - Mar 16, 2014.
This challenge investigates novel statistical techniques to identify neurophysiological correlates of trustworthiness. Deadline: May 5.