Paper by Peter Dabrock: “Ethical considerations and governance approaches of AI are at a crossroads. Either one tries to convey the impression that one can bring back a status quo ante of our given “onlife”-era, or one accepts to get responsibly involved in a digital world in which informational self-determination can no longer be safeguarded and fostered through the old fashioned data protection principles of informed consent, purpose limitation and data economy. The main focus of the talk is on how under the given conditions of AI and machine learning, data sovereignty (interpreted as controllability [not control (!)] of the data subject over the use of her data throughout the entire data processing cycle) can be strengthened without hindering innovation dynamics of digital economy and social cohesion of fully digitized societies. In order to put this approach into practice the talk combines a presentation of the concept of data sovereignty put forward by the German Ethics Council with recent research trends in effectively applying the AI ethics principles of explainability and enforceability…(More)”.
Realizing the Potential of AI Localism
Stefaan G. Verhulst and Mona Sloane at Project Syndicate: “Every new technology rides a wave from hype to dismay. But even by the usual standards, artificial intelligence has had a turbulent run. Is AI a society-renewing hero or a jobs-destroying villain? As always, the truth is not so categorical.
As a general-purpose technology, AI will be what we make of it, with its ultimate impact determined by the governance frameworks we build. As calls for new AI policies grow louder, there is an opportunity to shape the legal and regulatory infrastructure in ways that maximize AI’s benefits and limit its potential harms.
Until recently, AI governance has been discussed primarily at the national level. But most national AI strategies – particularly China’s – are focused on gaining or maintaining a competitive advantage globally. They are essentially business plans designed to attract investment and boost corporate competitiveness, usually with an added emphasis on enhancing national security.
This singular focus on competition has meant that framing rules and regulations for AI has been ignored. But cities are increasingly stepping into the void, with New York, Toronto, Dubai, Yokohama, and others serving as “laboratories” for governance innovation. Cities are experimenting with a range of policies, from bans on facial-recognition technology and certain other AI applications to the creation of data collaboratives. They are also making major investments in responsible AI research, localized high-potential tech ecosystems, and citizen-led initiatives.
This “AI localism” is in keeping with the broader trend in “New Localism,” as described by public-policy scholars Bruce Katz and the late Jeremy Nowak. Municipal and other local jurisdictions are increasingly taking it upon themselves to address a broad range of environmental, economic, and social challenges, and the domain of technology is no exception.
For example, New York, Seattle, and other cities have embraced what Ira Rubinstein of New York University calls “privacy localism,” by filling significant gaps in federal and state legislation, particularly when it comes to surveillance. Similarly, in the absence of a national or global broadband strategy, many cities have pursued “broadband localism,” by taking steps to bridge the service gap left by private-sector operators.
As a general approach to problem solving, localism offers both immediacy and proximity. Because it is managed within tightly defined geographic regions, it affords policymakers a better understanding of the tradeoffs involved. By calibrating algorithms and AI policies for local conditions, policymakers have a better chance of creating positive feedback loops that will result in greater effectiveness and accountability….(More)”.
Smarter government or data-driven disaster: the algorithms helping control local communities
Release by MuckRock: “What is the chance you, or your neighbor, will commit a crime? Should the government change a child’s bus route? Add more police to a neighborhood or take some away?
Every day government decisions from bus routes to policing used to be based on limited information and human judgment. Governments now use the ability to collect and analyze hundreds of data points everyday to automate many of their decisions.
Does handing government decisions over to algorithms save time and money? Can algorithms be fairer or less biased than human decision making? Do they make us safer? Automation and artificial intelligence could improve the notorious inefficiencies of government, and it could exacerbate existing errors in the data being used to power it.
MuckRock and the Rutgers Institute for Information Policy & Law (RIIPL) have compiled a collection of algorithms used in communities across the country to automate government decision-making.
Go right to the database.
We have also compiled policies and other guiding documents local governments use to make room for the future use of algorithms. You can find those as a project on DocumentCloud.
View policies on smart cities and technologies
These collections are a living resource and attempt to communally collect records and known instances of automated decision making in government….(More)”.
An Algorithm That Grants Freedom, or Takes It Away
Cade Metz and Adam Satariano at The New York Times: “…In Philadelphia, an algorithm created by a professor at the University of Pennsylvania has helped dictate the experience of probationers for at least five years.
The algorithm is one of many making decisions about people’s lives in the United States and Europe. Local authorities use so-called predictive algorithms to set police patrols, prison sentences and probation rules. In the Netherlands, an algorithm flagged welfare fraud risks. A British city rates which teenagers are most likely to become criminals.
Nearly every state in America has turned to this new sort of governance algorithm, according to the Electronic Privacy Information Center, a nonprofit dedicated to digital rights. Algorithm Watch, a watchdog in Berlin, has identified similar programs in at least 16 European countries.
As the practice spreads into new places and new parts of government, United Nations investigators, civil rights lawyers, labor unions and community organizers have been pushing back.
They are angered by a growing dependence on automated systems that are taking humans and transparency out of the process. It is often not clear how the systems are making their decisions. Is gender a factor? Age? ZIP code? It’s hard to say, since many states and countries have few rules requiring that algorithm-makers disclose their formulas.
They also worry that the biases — involving race, class and geography — of the people who create the algorithms are being baked into these systems, as ProPublica has reported. In San Jose, Calif., where an algorithm is used during arraignment hearings, an organization called Silicon Valley De-Bug interviews the family of each defendant, takes this personal information to each hearing and shares it with defenders as a kind of counterbalance to algorithms.
Two community organizers, the Media Mobilizing Project in Philadelphia and MediaJustice in Oakland, Calif., recently compiled a nationwide database of prediction algorithms. And Community Justice Exchange, a national organization that supports community organizers, is distributing a 50-page guide that advises organizers on how to confront the use of algorithms.
The algorithms are supposed to reduce the burden on understaffed agencies, cut government costs and — ideally — remove human bias. Opponents say governments haven’t shown much interest in learning what it means to take humans out of the decision making. A recent United Nations report warned that governments risked “stumbling zombie-like into a digital-welfare dystopia.”…(More)”.
Enchanted Determinism: Power without Responsibility in Artificial Intelligence
Paper by Alexander Campolo and Kate Crawford: “Deep learning techniques are growing in popularity within the field of artificial intelligence (AI). These approaches identify patterns in large scale datasets, and make classifications and predictions, which have been celebrated as more accurate than those of humans. But for a number of reasons, including nonlinear path from inputs to outputs, there is a dearth of theory that can explain why deep learning techniques work so well at pattern detection and prediction. Claims about “superhuman” accuracy and insight, paired with the inability to fully explain how these results are produced, form a discourse about AI that we call enchanted determinism. To analyze enchanted determinism, we situate it within a broader epistemological diagnosis of modernity: Max Weber’s theory of disenchantment. Deep learning occupies an ambiguous position in this framework. On one hand, it represents a complex form of technological calculation and prediction, phenomena Weber associated with disenchantment.
On the other hand, both deep learning experts and observers deploy enchanted, magical discourses to describe these systems’ uninterpretable mechanisms and counter-intuitive behavior. The combination of predictive accuracy and mysterious or unexplainable properties results in myth-making about deep learning’s transcendent, superhuman capacities, especially when it is applied in social settings. We analyze how discourses of magical deep learning produce techno-optimism, drawing on case studies from game-playing, adversarial examples, and attempts to infer sexual orientation from facial images. Enchantment shields the creators of these systems from accountability while its deterministic, calculative power intensifies social processes of classification and control….(More)”.
Whose Side are Ethics Codes On?
Paper by Anne L. Washington and Rachel S. Kuo: “The moral authority of ethics codes stems from an assumption that they serve a unified society, yet this ignores the political aspects of any shared resource. The sociologist Howard S. Becker challenged researchers to clarify their power and responsibility in the classic essay: Whose Side Are We On. Building on Becker’s hierarchy of credibility, we report on a critical discourse analysis of data ethics codes and emerging conceptualizations of beneficence, or the “social good”, of data technology. The analysis revealed that ethics codes from corporations and professional associations conflated consumers with society and were largely silent on agency. Interviews with community organizers about social change in the digital era supplement the analysis, surfacing the limits of technical solutions to concerns of marginalized communities. Given evidence that highlights the gulf between the documents and lived experiences, we argue that ethics codes that elevate consumers may simultaneously subordinate the needs of vulnerable populations. Understanding contested digital resources is central to the emerging field of public interest technology. We introduce the concept of digital differential vulnerability to explain disproportionate exposures to harm within data technology and suggest recommendations for future ethics codes….(More)”.
Principled Artificial Intelligence: Mapping Consensus in Ethical and Rights-Based Approaches to Principles for AI
Paper by Fjeld, Jessica and Achten, Nele and Hilligoss, Hannah and Nagy, Adam and Srikumar, Madhulika: “The rapid spread of artificial intelligence (AI) systems has precipitated a rise in ethical and human rights-based frameworks intended to guide the development and use of these technologies. Despite the proliferation of these “AI principles,” there has been little scholarly focus on understanding these efforts either individually or as contextualized within an expanding universe of principles with discernible trends.
To that end, this white paper and its associated data visualization compare the contents of thirty-six prominent AI principles documents side-by-side. This effort uncovered a growing consensus around eight key thematic trends: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values.
Underlying this “normative core,” our analysis examined the forty-seven individual principles that make up the themes, detailing notable similarities and differences in interpretation found across the documents. In sharing these observations, it is our hope that policymakers, advocates, scholars, and others working to maximize the benefits and minimize the harms of AI will be better positioned to build on existing efforts and to push the fractured, global conversation on the future of AI toward consensus…(More)”.
Change of heart: how algorithms could revolutionise organ donations
Tej Kohli at TheNewEconomy: “Artificial intelligence (AI) and biotechnology are both on an exponential growth trajectory, with the potential to improve how we experience our lives and even to extend life itself. But few have considered how these two frontier technologies could be brought together symbiotically to tackle global health and environmental challenges…
For example, combination technologies could tackle a global health issue such as organ donation. According to the World Health Organisation, an average of around 100,800 solid organ transplants were performed each year as of 2008. Yet, in the US, there are nearly 113,000 people waiting for a life-saving organ transplant, while thousands of good organs are discarded each year. For years, those in need of a kidney transplant had limited options: they either had to find a willing and biologically viable living donor, or wait for a viable deceased donor to show up in their local hospital.
But with enough patients and willing donors, big data and AI make it possible to facilitate far more matches than this one-to-one system allows, through a system of paired kidney donation. Patients can now procure a donor who is not a biological fit and still receive a kidney, because AI can match donors to recipients across a massive array of patient-donor relationships. In fact, a single person who steps forward to donate a kidney – to a loved one or even to a stranger – can set off a domino effect that saves dozens of lives by resolving the missing link in a long chain of pairings….
The moral and ethical implications of today’s frontier technologies are far-reaching. Fundamental questions have not been adequately addressed. How will algorithms weigh the needs of poor and wealthy patients? Should a donor organ be sent to a distant patient – potentially one in a different country – with a low rejection risk or to a nearby patient whose rejection risk is only slightly higher?
These are important questions, but I believe we should get combination technologies up and working, and then decide on the appropriate controls. The matching power of AI means that eight lives could be saved by just one deceased organ donor; innovations in biotechnology could ensure that organs are never wasted. The faster these technologies advance, the more lives we can save…(More)”.
Artificial intelligence, geopolitics, and information integrity
Report by John Villasenor: “Much has been written, and rightly so, about the potential that artificial intelligence (AI) can be used to create and promote misinformation. But there is a less well-recognized but equally important application for AI in helping to detect misinformation and limit its spread. This dual role will be particularly important in geopolitics, which is closely tied to how governments shape and react to public opinion both within and beyond their borders. And it is important for another reason as well: While nation-state interest in information is certainly not new, the incorporation of AI into the information ecosystem is set to accelerate as machine learning and related technologies experience continued advances.
The present article explores the intersection of AI and information integrity in the specific context of geopolitics. Before addressing that topic further, it is important to underscore that the geopolitical implications of AI go far beyond information. AI will reshape defense, manufacturing, trade, and many other geopolitically relevant sectors. But information is unique because information flows determine what people know about their own country and the events within it, as well as what they know about events occurring on a global scale. And information flows are also critical inputs to government decisions regarding defense, national security, and the promotion of economic growth. Thus, a full accounting of how AI will influence geopolitics of necessity requires engaging with its application in the information ecosystem.
This article begins with an exploration of some of the key factors that will shape the use of AI in future digital information technologies. It then considers how AI can be applied to both the creation and detection of misinformation. The final section addresses how AI will impact efforts by nation-states to promote–or impede–information integrity….(More)”.
Artificial Morality
Essay by Bruce Sterling: “This is an essay about lists of moral principles for the creators of Artificial Intelligence. I collect these lists, and I have to confess that I find them funny.
Nobody but AI mavens would ever tiptoe up to the notion of creating godlike cyber-entities that are much smarter than people. I hasten to assure you — I take that weird threat seriously. If we could wipe out the planet with nuclear physics back in the late 1940s, there must be plenty of other, novel ways to get that done.
What I find comical is a programmer’s approach to morality — the urge to carefully type out some moral code before raising unholy hell. Many professions other than programming have stern ethical standards: lawyers and doctors, for instance. Are lawyers and doctors evil? It depends. If a government is politically corrupt, then a nation’s lawyers don’t escape that moral stain. If a health system is slaughtering the population with mis-prescribed painkillers, then doctors can’t look good, either.
So if AI goes south, for whatever reason, programmers are just bound to look culpable and sinister. Careful lists of moral principles will not avert that moral judgment, no matter how many earnest efforts they make to avoid bias, to build and test for safety, to provide feedback for user accountability, to design for privacy, to consider the colossal abuse potential, and to eschew any direct involvement in AI weapons, AI spyware, and AI violations of human rights.
I’m not upset by moral judgments in well-intentioned manifestos, but it is an odd act of otherworldly hubris. Imagine if car engineers claimed they could build cars fit for all genders and races, test cars for safety-first, mirror-plate the car windows and the license plates for privacy, and make sure that military tanks and halftracks and James Bond’s Aston-Martin spy-mobile were never built at all. Who would put up with that presumptuous behavior? Not a soul, not even programmers.
In the hermetic world of AI ethics, it’s a given that self-driven cars will kill fewer people than we humans do. Why believe that? There’s no evidence for it. It’s merely a cranky aspiration. Life is cheap on traffic-choked American roads — that social bargain is already a hundred years old. If self-driven vehicles doubled the road-fatality rate, and yet cut shipping costs by 90 percent, of course those cars would be deployed.
I’m not a cynic about morality per se, but everybody’s got some. The military, the spies, the secret police and organized crime, they all have their moral codes. Military AI flourishes worldwide, and so does cyberwar AI, and AI police-state repression….(More)”.