If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The 1st of May is celebrated as International Labor Day, as it historically marks the relentless struggle of the working class to get the workday reduced to 8 hours and the workweek to 40 hours (Al Jazeera, 2019). The history of International Labor Day is rooted in the struggle for freedom and rights. It was initially called the "day of demonstrations," as peaceful protests for the demand of reducing working hours by workers in Chicago were countered by violence by the state. It also led to the sentencing to death of revolutionary leaders, who were tried only because of their political beliefs, without any evidence linking them to violence. Although this movement for labor rights started in the West, it soon reached other parts of the globe as well, where non-Western countries like India, Bangladesh, and Pakistan also initiated similar demonstrations to support better labor rights and opportunities.
Merhej, Ramona | Santos, Fernando P. (Informatics Institute, University of Amsterdam) | Melo, Francisco S. (INESC-ID and Instituto Superior Tecnico, Universidade de Lisboa) | Santos, Francisco C. (INESC-ID and Instituto Superior Tecnico, Universidade de Lisboa)
We examine how wealth inequality and diversity in the perception of risk of a collective disaster impact cooperation levels in the context of a public goods game with uncertain and non-linear returns. In this game, individuals face a collective-risk dilemma where they may contribute or not to a common pool to reduce their chances of future losses. We draw our conclusions based on social simulations with populations of independent reinforcement learners with diverse levels of risk and wealth. We find that both wealth inequality and diversity in risk assessment can hinder cooperation and augment collective losses. Additionally, wealth inequality further exacerbates long term inequality, causing rich agents to become richer and poor agents to become poorer. On the other hand, diversity in risk only amplifies inequality when combined with bias in group assortment--i.e., high probability that agents from the same risk class play together. Our results also suggest that taking wealth inequality into account can help to design effective policies aiming at leveraging cooperation in large group sizes, a configuration where collective action is harder to achieve. Finally, we characterize the circumstances under which risk perception alignment is crucial and those under which reducing wealth inequality constitutes a deciding factor for collective welfare.
The focus of the United Nations on Sustainable Development is unquestionable. It seeks to permeate the concept into every aspect of its projects and programmes all over the world. One of the most popular, yet simplest, definitions of Sustainable Development is "development that meets the needs of the present without compromising the ability of future generations to meet their own needs." This means thinking not just of ourselves and our consumption, but of the generations to come as well. Sustainable development also means equitable development.
Anja Kaspersen and Wendell Wallach are senior fellows at Carnegie Council for Ethics in International Affairs. In November 2021, they published an article that changed the AI ethics conversation: Why Are We Failing at the Ethics of AI? Six months later, the questions the article raised are no closer to resolution. This article was a don't-hold-your-punches review on the state of AI ethics, with which I am in almost complete agreement. If we want to advance the AI conversation, this is still a good place to start. I've quoted a portion of their article, with my comments interspersed: While it is clear that AI systems offer opportunities across various areas of life, what amounts to a responsible perspective on their ethics and governance is yet to be realized.
Human capital'--the economic value of our cognitive and noncognitive capacities--is our most important asset. According to recent World Bank estimates, the value of human capital globally amounts to 64 per cent of total capital, while in the advanced-country members of the Organisation for Economic Co-operation and Development it is typically worth four to six times as much as physical capital. Human capital is decisive not only for welfare but also for growth, social mobility and income distribution. Among these latter variables, the link between growth and inequality has been contentious in economic research. Three or four decades ago, the consensus in the profession was that inequality was beneficial for growth--indeed this was deemed so self-evident that empirical testing was unnecessary.
The debate on AI has focused mainly on its potential effect on employment. The impact on equality should not however be missed. 'Human capital'--the economic value of our cognitive and noncognitive capacities--is our most important asset. According to recent World Bank estimates, the value of human capital globally amounts to 64 per cent of total capital, while in the advanced-country members of the Organisation for Economic Co-operation and Development it is typically worth four to six times as much as physical capital. Human capital is decisive not only for welfare but also for growth, social mobility and income distribution.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. For those who understand its real-world applications and its potential, artificial intelligence is among the most valuable tools we have today. From disease detection and drug discovery to climate change models, AI is continually offering the insights and solutions that are helping us address the most pressing challenges of our time. In financial services, one of the main problems we are faced with is inequality when it comes to financial inclusion. Though this inequality is driven by many factors, the common denominator in each case is likely to be data (or lack thereof).
Facial recognition algorithms – which have repeatedly been demonstrated to be less accurate for people with darker skin – are just one example of how racial bias gets replicated within and perpetuated by emerging technologies. There's an urgency as AI is used to make really high-stakes decisions. The stakes are higher because new systems can replicate historical biases at scale. One of the fundamental questions of the work is: how to build AI models that deal with systemic inequality more effectively? Inequality is perpetuated by technology in many ways across many sectors.
Artificial intelligence (AI) currently plays a central role in the digitisation and modernisation strategies of public administrations and companies throughout Europe, the United States and China. The potential improvements and advances in efficiency that the incorporation of AI can offer strategic sectors in different countries have made it indispensable in a new era of technological transformation. And while no one wants to be left behind, the main players of this new digital era have from the very beginning approached these technologies in significantly different ways. While the United States and China have already embraced AI as one more component of their geopolitical strategies, the European Union (EU) is positioning itself as a global leader in its ethical use. According to the EU, in order to be considered ethical, any AI technology used in its territory must ensure respect for the fundamental rights of EU citizens.