Ethics and Bias
Our previous experiments have shown us that bias can not be removed from any kind of data over an extended period of time, and that it is not ethical to believe that a persistent point of view or anchor can be achieved.
Claims of lack and/or removal of bias are shallow and are not fit models for a world that constantly changes.
Artificial Intelligence
One of our primary objectives is the use and application of AI to the ethics space where bias is not removed, but constantly re-observed, as ethics do change depending upon the society and observers. Properly applied technologies are tools, not answers. We are not the same. Every being is an individual. While many of our attributes are the same, not a single person can understand the whole of humanity and experience. It’s time to stop categorizing and move on to a new model. It’s time to rework our systems or be stuck in an infrastructure that was not designed nor could comprehend what is happening with billions of humans on the planet. Each of us, with our own dreams, problems and successes.
Flux is friend, not foe.
Steady State is over.
Information
Raw data is the core component of information. The distribution mechanisms of that information to the data consumers are trusted or untrusted entities. Understanding and creating models for different types of data types is key in understanding how data will be consumed and/or integrated into systems and consumers.
Components:
1. Trustworthiness
2. Accreditation
3. Origin and Tracing
Education
Creating a multi-dimensional, measurable education platform will allow data consumers to contribute to their own consumption habits and policies. This platform will also contribute to continual growth and understanding of intentional corruption and bias practices of data originators. It’s time to completely rethink formal and informal education as a result of today’s and upcoming challenges.
Components:
1. Proof of Fact
2. Media Consumption Education
Results Driven Accounting
We now have AI that is more often than not resulting in bias amplification. What is the line between ethical and unethical use of controls? Who decides? Universal adoption, member participation and self-governance must be part of the solutions. To date, no solutions have been shown sufficiently unhackable or co-optable by those wishing to take advantage of such systems. No system is unhackable, but by making it as difficult as possible for bad actors and adding traceability we can revolutionize information origination and dissemination.
Psychology
How do we use human psychology to not only measure results and reapply those results to continual tuning of algorithms?
What controls need be placed, and how do we account for the emergence of currently unknown variables as they appear? Those emergent new variables that will be used to define future objectives will have to be understood and shared within the scientific and mental health communities.
Component:
1. Ongoing Practice and Studies
Still Interested?
There’s a lot more coming as we flush out our timetable and continue recruitment and hiring. Stay tuned.