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Ed Webb

Artificial Intelligence and the Future of Humans | Pew Research Center - 0 views

www.pewinternet.org/...gence-and-the-future-of-humans

AI dystopia prediction future futures computing

shared by Ed Webb on 14 Dec 18 - No Cached
  • experts predicted networked artificial intelligence will amplify human effectiveness but also threaten human autonomy, agency and capabilities
  • most experts, regardless of whether they are optimistic or not, expressed concerns about the long-term impact of these new tools on the essential elements of being human. All respondents in this non-scientific canvassing were asked to elaborate on why they felt AI would leave people better off or not. Many shared deep worries, and many also suggested pathways toward solutions. The main themes they sounded about threats and remedies are outlined in the accompanying table.
  • CONCERNS Human agency: Individuals are  experiencing a loss of control over their lives Decision-making on key aspects of digital life is automatically ceded to code-driven, "black box" tools. People lack input and do not learn the context about how the tools work. They sacrifice independence, privacy and power over choice; they have no control over these processes. This effect will deepen as automated systems become more prevalent and complex. Data abuse: Data use and surveillance in complex systems is designed for profit or for exercising power Most AI tools are and will be in the hands of companies striving for profits or governments striving for power. Values and ethics are often not baked into the digital systems making people's decisions for them. These systems are globally networked and not easy to regulate or rein in. Job loss: The AI takeover of jobs will widen economic divides, leading to social upheaval The efficiencies and other economic advantages of code-based machine intelligence will continue to disrupt all aspects of human work. While some expect new jobs will emerge, others worry about massive job losses, widening economic divides and social upheavals, including populist uprisings. Dependence lock-in: Reduction of individuals’ cognitive, social and survival skills Many see AI as augmenting human capacities but some predict the opposite - that people's deepening dependence on machine-driven networks will erode their abilities to think for themselves, take action independent of automated systems and interact effectively with others. Mayhem: Autonomous weapons, cybercrime and weaponized information Some predict further erosion of traditional sociopolitical structures and the possibility of great loss of lives due to accelerated growth of autonomous military applications and the use of weaponized information, lies and propaganda to dangerously destabilize human groups. Some also fear cybercriminals' reach into economic systems.
  • ...18 more annotations...
  • AI and ML [machine learning] can also be used to increasingly concentrate wealth and power, leaving many people behind, and to create even more horrifying weapons
  • “In 2030, the greatest set of questions will involve how perceptions of AI and their application will influence the trajectory of civil rights in the future. Questions about privacy, speech, the right of assembly and technological construction of personhood will all re-emerge in this new AI context, throwing into question our deepest-held beliefs about equality and opportunity for all. Who will benefit and who will be disadvantaged in this new world depends on how broadly we analyze these questions today, for the future.”
  • SUGGESTED SOLUTIONS Global good is No. 1: Improve human collaboration across borders and stakeholder groups Digital cooperation to serve humanity's best interests is the top priority. Ways must be found for people around the world to come to common understandings and agreements - to join forces to facilitate the innovation of widely accepted approaches aimed at tackling wicked problems and maintaining control over complex human-digital networks. Values-based system: Develop policies to assure AI will be directed at ‘humanness’ and common good Adopt a 'moonshot mentality' to build inclusive, decentralized intelligent digital networks 'imbued with empathy' that help humans aggressively ensure that technology meets social and ethical responsibilities. Some new level of regulatory and certification process will be necessary. Prioritize people: Alter economic and political systems to better help humans ‘race with the robots’ Reorganize economic and political systems toward the goal of expanding humans' capacities and capabilities in order to heighten human/AI collaboration and staunch trends that would compromise human relevance in the face of programmed intelligence.
  • As AI matures, we will need a responsive workforce, capable of adapting to new processes, systems and tools every few years. The need for these fields will arise faster than our labor departments, schools and universities are acknowledging
  • We humans care deeply about how others see us – and the others whose approval we seek will increasingly be artificial. By then, the difference between humans and bots will have blurred considerably. Via screen and projection, the voice, appearance and behaviors of bots will be indistinguishable from those of humans, and even physical robots, though obviously non-human, will be so convincingly sincere that our impression of them as thinking, feeling beings, on par with or superior to ourselves, will be unshaken. Adding to the ambiguity, our own communication will be heavily augmented: Programs will compose many of our messages and our online/AR appearance will [be] computationally crafted. (Raw, unaided human speech and demeanor will seem embarrassingly clunky, slow and unsophisticated.) Aided by their access to vast troves of data about each of us, bots will far surpass humans in their ability to attract and persuade us. Able to mimic emotion expertly, they’ll never be overcome by feelings: If they blurt something out in anger, it will be because that behavior was calculated to be the most efficacious way of advancing whatever goals they had ‘in mind.’ But what are those goals?
  • AI will drive a vast range of efficiency optimizations but also enable hidden discrimination and arbitrary penalization of individuals in areas like insurance, job seeking and performance assessment
  • The record to date is that convenience overwhelms privacy
  • “I strongly believe the answer depends on whether we can shift our economic systems toward prioritizing radical human improvement and staunching the trend toward human irrelevance in the face of AI. I don’t mean just jobs; I mean true, existential irrelevance, which is the end result of not prioritizing human well-being and cognition.”
  • AI will eventually cause a large number of people to be permanently out of work
  • Newer generations of citizens will become more and more dependent on networked AI structures and processes
  • there will exist sharper divisions between digital ‘haves’ and ‘have-nots,’ as well as among technologically dependent digital infrastructures. Finally, there is the question of the new ‘commanding heights’ of the digital network infrastructure’s ownership and control
  • As a species we are aggressive, competitive and lazy. We are also empathic, community minded and (sometimes) self-sacrificing. We have many other attributes. These will all be amplified
  • Given historical precedent, one would have to assume it will be our worst qualities that are augmented
  • Our capacity to modify our behaviour, subject to empathy and an associated ethical framework, will be reduced by the disassociation between our agency and the act of killing
  • We cannot expect our AI systems to be ethical on our behalf – they won’t be, as they will be designed to kill efficiently, not thoughtfully
  • the Orwellian nightmare realised
  • “AI will continue to concentrate power and wealth in the hands of a few big monopolies based on the U.S. and China. Most people – and parts of the world – will be worse off.”
  • The remainder of this report is divided into three sections that draw from hundreds of additional respondents’ hopeful and critical observations: 1) concerns about human-AI evolution, 2) suggested solutions to address AI’s impact, and 3) expectations of what life will be like in 2030, including respondents’ positive outlooks on the quality of life and the future of work, health care and education
Ed Webb

Belfast is welcoming refugees with a radical new approach: speaking to them | openDemoc... - 0 views

www.opendemocracy.net/...community-peacebuilding-racism

diversity inclusion dialogue refugees migrants UK

shared by Ed Webb on 31 Aug 18 - No Cached
  • Belfast Friendship Club meets every Thursday evening, and over the months and years meaningful connections and friendships have been forged, irrespective of our backgrounds or identities. The club’s strength arises from an ethos of solidarity, equity, respect and the huge, loyal and expanding membership draws newcomers into its warm and welcoming space.
  • In a country still wrestling with its history of intolerance and suspicion of the ‘other’, introducing the table hosts as my friends immediately sets the scene and makes way for connection on a basic, human level. As table hosts share their purely personal perspectives about how they’ve coped with their lives as migrant workers, asylum seekers or refugees, participants are prompted to wonder how they, too, might fare if placed in similar situations.
Ed Webb

I unintentionally created a biased AI algorithm 25 years ago - tech companies are still... - 0 views

theconversation.com/making-the-same-mistake-203734

AI algorithms race racism bias DickinsonCollege dickinson technology opinion analysis

shared by Ed Webb on 28 May 23 - Cached
  • How and why do well-educated, well-intentioned scientists produce biased AI systems? Sociological theories of privilege provide one useful lens.
  • Their training data is biased. They are designed by an unrepresentative group. They face the mathematical impossibility of treating all categories equally. They must somehow trade accuracy for fairness. And their biases are hiding behind millions of inscrutable numerical parameters.
  • fairness can still be the victim of competitive pressures in academia and industry. The flawed Bard and Bing chatbots from Google and Microsoft are recent evidence of this grim reality. The commercial necessity of building market share led to the premature release of these systems.
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  • Scientists also face a nasty subconscious dilemma when incorporating diversity into machine learning models: Diverse, inclusive models perform worse than narrow models.
  • biased AI systems can still be created unintentionally and easily. It’s also clear that the bias in these systems can be harmful, hard to detect and even harder to eliminate.
  • with North American computer science doctoral programs graduating only about 23% female, and 3% Black and Latino students, there will continue to be many rooms and many algorithms in which underrepresented groups are not represented at all.
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