Governance of Artificial Intelligence
Christian Hunt
25 years: Behavioural science & compliance
In this video, Christian explores essential strategies for governing AI development, offering insights on how companies and governments can regulate and control AI to prevent negative outcomes.
In this video, Christian explores essential strategies for governing AI development, offering insights on how companies and governments can regulate and control AI to prevent negative outcomes.
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Governance of Artificial Intelligence
13 mins 20 secs
Key learning objectives:
Define artificial intelligence
Outline the three main subsets of artificial intelligence
Understand the role of government in regulating artificial intelligence
Identify the challenges of artificial intelligence
Overview:
Artificial intelligence is reshaping our lives across healthcare, transportation, and education by emulating human and natural intelligence. Within AI's three primary subsets, i.e., machine learning, natural language processing, and computer vision, we witness the transformational impact. Machine learning powers personalised recommendations on platforms like Netflix, NLP deciphers sentiments for brand monitoring, and computer vision enables object recognition for self-driving cars and facial recognition in security and social media. This overview encapsulates the dynamic influence of AI, driven by complex algorithms and extensive datasets, which is continually redefining our technological landscape.
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- Machine learning
- Natural language processing
- Computer vision
What challenges do governments face in legislating the use of AI?
Governments encounter significant challenges in legislating the use of AI due to the evolving nature of technology and its potential impact on society. One major concern is the use of facial recognition technology by law enforcement. While it offers the potential to identify criminals easily, it raises issues related to privacy, bias, and accuracy.
What are some of the challenges associated with regulating AI, and how do these challenges impact the development of effective rules and standards?
Regulating AI presents several challenges. The rapid evolution of AI technology can render regulations quickly outdated or ineffective, raising concerns about balancing innovation with safety and ethics. Additionally, the lack of standardisation and coordination across countries may result in regulatory fragmentation. An example is the 2021 European Union proposal, criticised for being too broad and potentially hindering innovation. The overarching challenge lies in creating rules that harness the societal benefits of AI while preventing unforeseen negative outcomes. This complexity is further highlighted by incidents like Facebook's AI-powered chatbot, underscoring the need for continuous research and understanding of AI system interactions to inform effective regulation.
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Christian Hunt
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