'60 Leaders' is an initiative that brings together diverse views from global thought leaders on a series of topics – from innovation and artificial intelligence to social media and democracy. It is created and distributed on principles of open collaboration and knowledge sharing. Created by many, offered to all.
ABOUT 60 LEADERS
'60 Leaders on Artificial Intelligence' brings together unique insights on the topic of Artificial Intelligence - from the latest technical advances to ethical concerns and risks for humanity. The book is organized into 17 chapters - each addressing one question through multiple answers reflecting a variety of backgrounds and standpoints. Learn how AI is changing how businesses operate, how products are built, and how companies should adapt to the new reality. Understand the risks from AI and what should be done to protect individuals and humanity. View the leaders.
'60 Leaders on Innovation' is the book that brings together unique insights and ‘practical wisdom’ on innovation. The book is organized into 22 chapters, each presenting one question and multiple answers from 60 global leaders. Learn how innovative companies operate and how to adopt effective innovation strategies. Understand how innovation and experimentation methods blend with agile product development. Get insights from the experts on the role of the C-Suite for innovation. Discover ways that Innovation can help humanity solve big problems like climate change.
What is the impact of AI on society and everyday life?
“The rise of powerful AI will be either the best or the worst thing ever to happen to humanity. We do not yet know which.” . As per Stephen Hawking’s quote, the effects of AI remain unclear and unknown. However, increasing digitalization and AI utilization could radically transform work and society, knowledge, learning, and power (re)distribution. Given the dangers of lost individual knowledge through the increased use of AI and algorithmic decision-making, the overreliance on AI and algorithms might hamper learning, decision-making, and innovation. For example, AI has assumptions about knowledge, particularly tacit knowledge, which are currently highly problematic and require considerable improvement prior to the reliable use of AI and its predetermined codified (and encoded) knowledge. At this point, it is helpful to differentiate between knowledge, information, and data.
Information, and data, are an ingredient to knowledge but do not represent knowledge. Data are facts, information is the processed data and knowledge is the interpreted and actionable information. Knowledge can be explicit and tacit. Explicit knowledge can be easily captured, codified, processed, stored, and distributed. Tacit knowledge cannot easily get captured, codified, processed, stored, and distributed - tacit knowledge is accrued through experience and is explained as an ongoing accomplishment through practice and participation.
The problem with knowledge encoded in the AI is that it is narrow and brittle, and AI systems are only reliable in a narrow topic and domain, which are predetermined, and when the topic and domain are challenged or changed, AI systems “fall off the knowledge cliff”. Therefore, while AI might help for narrow, routine, predetermined tasks, it is (as yet) unreliable and inaccurate to help with complex problems and decision-making, where automation and the use of AI are yet currently impossible because tacit knowledge cannot be easily codified. AI can analyse volumes of data, however, the knowledge aspect needs further development.
Despite the danger of AI systems falling off the knowledge cliff, the use of AI, automated and algorithmic decision-making has increased in organizations and societies. For example, the use of Decision Support Systems and ‘big data’ has limited the power of individuals in strategic decision-making and has (in some instances) replaced their tacit knowledge, experience, and expertise on the assumption that their calculated rationality leads to superior outcomes. For example, Fernando Alonso lost the 2010 Formula 1 Grand Prix Championship because the race simulation algorithm provided a poor decision, and the Chief Race Strategist did not have the power to participate in the decision-making or to change (or overrule) the decision of the algorithm, which led to Alonso and Ferrari losing the championship and to the Chief Race Strategist losing their role.
The issue of tacit knowledge encoded in AI is also demonstrated in the challenges around the development of the decision-making algorithms for autonomous cars where decisions need to be predetermined and context and interpretation of a situation are currently limited. Therefore, how AI is used needs considerable thought and consideration as in its current state, it does not have enough knowledge capabilities, and its current use hampers knowledge, learning, decision-making, innovation, and society.
In the context of a digital economy, AI and automation could advance the power of the influential through control, surveillance, monitoring, discrimination, information asymmetries, manipulation, ‘algorithmification’, and ‘datafication’. Such uses of AI, examples of which currently dominate, lead to exploitation, exclusion, marginalization, discrimination, and manipulation. For example, Cambridge Analytica, which ran the American presidential digital campaign, arguably manipulated the opinions of people and their votes or voting intentions or behaviors, through the provision of filtered information to influence their votes. AI has been used to exploit the practices of people and their opinions, resulting in manipulating the vote. Other examples of exploitation are the automation and ‘algorithmification’ of influential technology organizations that collect and exploit data, eliminate competition, and coerce organizations to follow their algorithms . Therefore, influential technology organizations may further consolidate and increase their power because of their technology leadership and the opportunities for exploitation practices.
AI could lead to emancipation through empowerment, autonomy, inclusion, participation, and collaboration. However, such examples are scarce, and the emancipatory use of AI, or the emancipatory outcomes of the use of AI are limited. Organizations, developers, governments, workers, and societies need to collaborate on determining how these AI systems are developed and used to enable emancipation and empowerment.