BCS Hybrid Event: An Introduction to Responsible AI
AI, ethics
Asian girl holding robot’s hand, Kuromon Market in Osaka, PHOTO: Andy Kelly via Getty Images
In the current landscape of artificial intelligence, the prevailing approach revolves around data-driven methodologies, often disregarding the crucial social context in which these technologies are deployed. This myopic focus raises legitimate concerns about the widespread apprehension over the social and economic implications of AI adoption.
Nevertheless, we must not overlook the immense potential of AI systems to address some of society's most intricate and pressing challenges. From transforming healthcare and social care to revolutionizing finance, economics, industry and environmental conservation, AI's impact on diverse domains is undeniable.
To truly understand the path that led us to this juncture, we delve into the historical underpinnings, considering the pivotal industrial revolutions that shaped our world. The emergence of big data as a driving force behind the AI revolution cannot be ignored, as it laid the groundwork for the remarkable advancements in algorithmic technologies.
Our journey in the world of AI begins with the pioneers, Ada Lovelace and Alan Turing. They laid the groundwork for what would become a transformative technological revolution. From those early days of envisioning and theorizing, AI has come a long way, impacting every aspect of our lives today.
In essence, the journey towards harnessing the full potential of AI is a multifaceted one, entailing a comprehensive understanding of its historical evolution, the significance of big data and the pivotal role of Responsible AI in shaping a sustainable and harmonious future.
Big Data Rationale
By understanding the significance of big data and its role in generating insights, we can then delve into how AI leverages this vast amount of data to achieve intelligent outcomes and build advanced models. This approach will provide context and clarity, in order to further discuss AI's capabilities and its impact.
Data analytics, structured data visual dashboard
DATA: is defined as facts regarding things( such as people, objects, events) which can be digitally stored, transmitted or processed.
INFORMATION: is generally defined as data that have been processed and presented in a form suitable for human interpretation with the purpose of revealing meaning (such as patterns or rules).
KNOWLEDGE: the theoretical and practical comprehension of a certain domain (objects, concepts and relationships) that supports making decisions.
INTELLIGENGE: the capability of learning, understanding and finding solutions for problems in a specific domain.
MODELS are compact representation of patterns.
What Is Intelligence?
Intelligence is a multifaceted and complex ability that enables individuals to acquire, understand, process, solve problems, adapt to new situations and make decisions. It encompasses a wide range of mental abilities and skills, including critical thinking, adapt to new a environment, learn from experience, creativity, memory, language and engage in abstract thinking. Intelligence allows us to process information, acquire knowledge and apply it effectively in various contexts, making it a fundamental aspect of human cognition and behavior.
The Five Industrial Revolutions
Spanning from the 18th century to today, have been transformative periods in human history, driving significant technological advancements and societal changes.
First Industrial Revolution (late 18th to early 19th century): Marked by the mechanization of production through water and steam power, this era saw the rise of factories and the transition from agrarian economies to industrial ones.
Second Industrial Revolution (late 19th to early 20th century): Characterized by the advent of electricity, mass production, and the assembly line, this revolution led to widespread industrialization and the growth of consumer goods industries.
Third Industrial Revolution (late 20th century): Often referred to as the Digital Revolution, this era witnessed the rise of computers, automation, and information technology, revolutionizing communication, manufacturing, and services.
Fourth Industrial Revolution (present): Building upon the Third Revolution, this era is defined by the integration of digital technologies, artificial intelligence, big data, and the Internet of Things (IoT). It is reshaping industries, economies, and societies through smart systems and automation.
Fifth Industrial Revolution (emerging): Still in its infancy, the Fifth Revolution is expected to see advancements in fields like quantum computing, biotechnology, and sustainable technologies. It promises to further revolutionize industries and the way we live and interact with the world.
Each industrial revolution has brought about significant shifts in the global economy, labor markets, and daily life, and the ongoing Fifth Industrial Revolution holds the potential to shape a more technologically advanced and interconnected future.
The Journey of AI's History
Through various significant steps, initiating with the naive learning methods of the 1960s paved the way for progress. As we advanced, machine learning techniques took center stage, followed by the game-changing emergence of deep learning. This transformative phase led to the I learn to learn approach, setting the stage for the current era. Today, AI continues to evolve, incorporating distributed agents, swarm deep learning and reinforcement learning, marking a new chapter of sophisticated and adaptable AI systems.
AI's Risks and Challenges
AI risks and challenges are generated from various sources, including biased algorithms that perpetuate discrimination, the potential for mass unemployment due to automation, and the misuse of AI for malicious purposes, such as deepfake technology or cyber attacks. Additionally, concerns arise from the lack of transparent decision-making in complex AI systems, leading to potential errors and unintended consequences. Striking a balance between technological advancement and ethical considerations is crucial to harness the full potential of AI while mitigating its risks.
Responsibility
Responsibility refers to the obligation of individuals or organizations to carry out their tasks and duties in a reliable and trustworthy manner. This is an important aspect of ethical and moral behaviour, which means being accountable for the development, deployment, and maintenance of technology solutions and systems, ensuring they meet ethical standards, security requirements, and legal obligations.
Accountability: Accountability involves accepting responsibility for one's actions, decisions, and their consequences.
Reliability: trustworthy in fulfilling commitments, obligations and meeting expectations.
Ethical Decision-making: avoiding actions that harm others or violate societal norms.
Learning from mistakes: using mistakes as opportunities for growth and improvement.
Responsibility is a fundamental pillar in cultivating trust and fostering respect within relationships, laying the groundwork for a sense of integrity and dignity in both individuals and communities.
SPEAKER PROFILE
Professor Daniel Neagu, a visionary leader in AI research with two decades of groundbreaking experience. As a Professor of Computing and head of the AI Research (AIRe) Group at the University of Bradford, his expertise spans Machine Learning, AI model governance and big data analytics. With impactful work in various fields like social care, health, and engineering, he is a driving force in knowledge engineering and international AI conferences. Beyond his academic endeavors, Professor Neagu actively contributes to the advancement of knowledge engineering as an Associate Editor for the esteemed journal, Expert Systems.
Delivery based on resources produced by a team of AI Research (AIRe) students and experts, University of BRADFORD:
Dr Paul Trundle
Dr Amr Abdullatif
John Marko, PhD student
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