Category: Artificial Intelligence (AI)

Just as the “autonomous” in lethal autonomous weapons allows the military to dissemble over responsibility for their effects, there are civilian companies leveraging “AI” to exert control without responsibility.

And so we arrive at “trustworthy AI” because, of course, we are building systems that people should trust and if they don’t it’s their fault, so how can we make them do that, right? Or, we’ve built this amazing “AI” system that can drive your car for you but don’t blame us when it crashes because you should have been paying attention. Or, we built it, sure, but then it learned stuff and it’s not under our control anymore—the world is a complex place.

The COVID-19 pandemic has exposed and exacerbated existing global inequalities. Whether at the local, national, or international scale, the gap between the privileged and the vulnerable is growing wider, resulting in a broad increase in inequality across all dimensions of society. The disease has strained health systems, social support programs, and the economy as a whole, drawing an ever-widening distinction between those with access to treatment, services, and job opportunities and those without.

We celebrated AI for mental health equity when access is augmented for marginalized populations. We applauded AI as a complement to current services; practitioners would be less overtaxed and more productive, thereby serving vulnerable populations better.

https://21stcenturywiener.org/ 22-25 July 2021, Chennai, INDIA N R Narayana Murthy to present Opening Speech on 22 July 2021. Infosys co-founder…  Read More

Introduction In 2019, IEEE Working Group P7014 began efforts to develop a ‘Standard for Ethical Considerations in Emulated Empathy in…  Read More

There is huge potential for artificial intelligence (AI) to bring massive benefits to under-served populations, advancing equal access to public services such as health, education, social assistance, or public transportation, AI can also drive inequality, concentrating wealth, resources, and decision-making power in the hands of a few countries, companies, or citizens. Artificial intelligence for equity (AI4Eq) calls upon academics, AI developers, civil society, and government policy-makers to work collaboratively toward a technological transformation that increases the benefits to society, reduces inequality, and aims to leave no one behind.

From the 1970s onward, we started to dream of the leisure society in which, thanks to technological progress and consequent increase in productivity, working hours would be minimized and we would all live in abundance. We all could devote our time almost exclusively to personal relationships, contact with nature, sciences, the arts, playful activities, and so on. Today, this utopia seems more unattainable than it did then. Since the 21st century, we have seen inequalities increasingly accentuated: of the increase in wealth in the United States between 2006 and 2018, adjusted for inflation and population growth, more than 87% went to the richest 10% of the population, and the poorest 50% lost wealth .

Understanding the societal trajectory induced by AI, and anticipating its directions so that we might apply it for achieving equity, is a sociological, ethical, legal, cultural, generational, educational, and political problem.

Video doorbells and related technologies, along with the data they generate, will continue to be abused, undermining the security of what is being pitched as a security technology.

Examining how face recognition software is used to identify and sort citizenship within mechanisms like the Biometric Air Exit (BAE) is immensely important; alongside this, the process of how “citizen” and “noncitizen” is defined, as data points within larger mechanisms like the BAE, need to be made transparent.

Technological determinism is a myth; there are always underlying economic motivations for emergence of new technologies. The idea that technology leads development is not necessarily true, for example, con-sider AI. It has been a topic of inter-est to researchers for decades, but only recently has the funding caught up, matching the motivation and enabling the development of AI-ori-ented technologies to really take off.

Ethical diversity refers to “diverse beliefs … as to what are the most ethically appropriate or inappropriate courses of actions,” and takes into account the different values and beliefs people hold [2]. This diversity is and has always been a source of confusion and conflict, from the personal to the international. The answer, however, is to have forums to debate and discuss the ethical choices embedded in everyday life, not algorithms that render the choice being made invisible.

Will We Make Our Numbers? The year 2020 has a majority of the planet asking the simple question: “How do we stay alive? Competition is not working for the long-term sustainability of human and environmental well-being.

As we work to decouple carbon emissions and economic growth on the path to net zero emissions — so-called “clean growth” — we must also meaningfully deliver sustainable, inclusive growth with emerging technologies.

While many of us hear about the latest and greatest breakthrough in AI technology, what we hear less about is its environmental impact. In fact, much of AI’s recent progress has required ever-increasing amounts of data and computing power. We believe that tracking and communicating the environmental impact of ML should be a key part of the research and development process.

Disruptions can have positive as well as negative impacts on natural and human systems. Among the most fundamental disruptions to global society over the last century is the rise of big data, artificial intelligence (AI), and other digital technologies. These digital technologies have created new opportunities to understand and manage global systemic risks.

Two major forces are shaping the future of human civilization: anthropogenic climate change and the digital revolution. The changing climate is driving systemic shifts that threaten to destabilize the health and wellbeing of humankind and the natural systems on which they depend.

It is important to discuss both the potential and risks of machine learning (ML) and to inspire practitioners to use ML for beneficial objectives.

IEEE ISTAS 2019 Plenary 4 on 16 November ’19 focused on sharing insights into Brave New Brain-Tech: IEEE Neuroethics Program…  Read More

As technology pervades all aspects of our existence, and Artificial Intelligence and machine learning systems become commonplace, a new era of human-computer interaction is emerging that will involve directing our focus beyond traditional approaches, to span other intricate interactions with computer-based systems.