On November 29th, Fordham University hosted a conference entitled, “Ethical Vision Artificial Intelligence: Creating an Effective AI Compliance Framework” in New York. The conference was hosted by Professor Shlomit Yanisky-Ravid, a visiting professor at Fordham Law School and a Fellow Professor at Yale Law School.
Ethical-legal AI guidelines
“This is our first step toward the establishment of a forum that will discuss, propose and advance ethical-legal AI guidelines for future regulations,” said Professor Yanisky-Ravid. “In light of the rapid and constant growth in uses of AI and lack of regulations, we are holding this international conference that addresses the challenges and solutions.”
“A recent report unveils that sensitive facial recognition technology is being adopted by law enforcement across the globe, including U.S. law enforcement agencies that are increasingly using body-worn cameras, which may challenge and potentially violate human and civil rights of citizens when paired with facial recognition abilities.”
Transparency and accuracy of AI systems
Establishing ethical-legal principles should be based upon fairness, equality, privacy, responsibility, accountability"
Prof. Yanisky-Ravid noted, “Our goal is to fill the existing gap resulting from the lack of U.S. laws and regulations relating to AI systems. It also aims to cultivate dialogue that is currently lacking between policymakers and private industry by building bridges of trust between these entities to foster a better understanding of various perspectives.”
“We share the same goals in establishing ethical-legal principles, guidelines, and norms. These principles should be based upon fairness, equality, privacy, responsibility, accountability, transparency, and accuracy of AI systems.”
Addressing the ethical and legal questions
“This international conference is our first step toward the establishment of an ‘incubator’ for exchanging ideas, conducting research, and promoting discourse and publications,” stated Prof. Yanisky-Ravid.
“We envision a forum that will discuss, propose and advance ethical-legal AI guidelines and principles for future regulations using academic tools, including research, roundtables, presentations, discussions, and publications. This forum is critical to tackling the ethical and legal questions stemming from the ever-changing AI ecosystem which currently lacks proper regulation.”
AI compliance framework
Ideas for companies to regulate their use of AI with active government oversight of biometrics, and facial recognition
Prof. Carole Basri, Chief Advisor of the Association of Corporate In-House Counsel Program, discussed the challenge of creating an ethical and effective AI compliance framework. Prof. Basri proposed several ideas for companies to better regulate their use of AI with active government oversight of machine vision, biometrics, and facial recognition technology.
Prof. Basri said, “There is a deep and common concern in modern society that AI technology will become uncontrollable. There is, therefore, a call for social, legal, and ethical tools for regulating AI’s functions and outcomes. An effective compliance framework can help organisations address concerns about the technology.”
AI-based face and object recognition technologies
Dean Nicolls, Oosto’s Chief Marketing Officer, represented the Visual AI and facial recognition industry in the discussion.
Oosto is a pioneering visual AI platform enabling enterprises to protect customers, guests, and employees by identifying security and safety threats in real-time by exploiting the power of AI-based face and object recognition technologies.
Facial recognition sensitivity
Mr. Nicolls presented a scale of sensitivity of facial recognition use cases ranging from unlocking users’ mobile phones for authentication to mass public surveillance of citizens by government agencies.
“The media’s focus on law enforcement’s use of facial recognition and the wrongful arrests resulting from its application has cast a negative perception of facial recognition technology -- even though these examples represent a small fraction of the total use cases in production.”
- Network / IP
- Biometrics
- Retail surveillance
- Digital video surveillance
- Commercial security
- Private sector security
- Public sector security
- Security management
- Security cameras
- Covert cameras
- Security camera systems
- Security monitoring system
- Voice recognition systems
- Facial recognition systems
- School security
- Institute security
- Public security
- Industrial security systems
- Wireless security
- Hybrid surveillance systems
- Mobile surveillance
- Day/Night surveillance
- Covert Surveillance
- Security communication
- Integration software
- Mobile communications
- Internet of Things (IoT)
- Corporate Security
- Digital Video Recorders
- Artificial intelligence (AI)
- Mobile access
- Video surveillance
- Machine Learning
