Ethical AI

 Via DSC, below the intro, much more at the link:

The Ethical AI Application Pyramid

Posted by Bill Schmarzo  In the blog “How Can Your Organization Manage AI Model Biases?”, I wrote:

“In a world more and more driven by AI models, Data Scientists cannot effectively ascertain on their own the costs associated with the unintended consequences of False Positives and False Negatives. Mitigating unintended consequences requires the collaboration across a diverse set of stakeholders in order to identify the metrics against which the AI Utility Function will seek to optimize.”

I’ve been fortunate enough to have had some interesting conversations since publishing that blog, especially with an organization who is championing data ethics and “Responsible AI” (love that term). As was so well covered in Cathy O’Neil’s book “Weapons of Math Destruction”, the biases built into many of the AI models that are being used to approve loans and mortgages, hire job applicants, and accept university admissions are yielding unintended consequences that severely impact both individuals and society.

AI models only optimize against the metrics against which it has been programmed to optimize. If the AI model yields unintended consequences, that’s not the AI models fault. It’s the fault of the data science team and the operational stakeholders who are responsible for defining the AI Utility Function against which the AI model will judge model progress and success (see Figure 1).  ... " 

On the Ethics of AI

Concise piece on the topic from Cisco

Analytics & Automation

Ethics of Artificial Intelligence  in Cisco Blog

Utkarsh Srivastava

Intelligent machines have helped humans in achieving great endeavors. Artificial Intelligence (AI) combined with human experiences have resulted in quick wins for stakeholders across multiple industries, with use cases ranging from finance to healthcare to marketing to operations and more. There is no denying the fact that Artificial Intelligence (AI) has helped in quicker product innovation and an enriching user experience. However, few of these use cases include context-aware marketing, sales forecasting, conversational analytics, fraud detection, credit scoring, drug testing, pregnancy monitoring, self-driving cars – a never-ending list of applications.

But the very idea of developing smart machines (AI-powered systems) raises numerous ethical concerns. What is the probability that these smart machines won’t harm humans or other morally relevant beings? Matthew Hutson, a research scientist from the Massachusetts Institute of Technology mentioned in one of his studies that AI algorithms embedded in digital and social media technologies can reinforce societal biases, accelerate the spread of rumors and disinformation, amplify echo chambers of public opinion, hijack our attention, and impair mental well-being. Earlier discussions related to this concept of “Data and AI ethics” were only limited to non-profit organizations and academic institutions. But with the rapidly changing industry spectrum, global tech giants are putting together fast-growing teams to handle the ethics of AI. And as these companies have invested more due diligence into the challenge, they’ve discovered that the majority of these ethical issues arise during the lifecycle of data resulting from the widespread collection and processing of data to train AI models. ... '