Paper by Hani Safadi and Richard Thomas Watson: “The rise of digital platforms creates knowledge monopolies that threaten innovation. Their power derives from the imposition of data obligations and persistent coupling on platform participation and their usurpation of the rights to data created by other participants to facilitate information asymmetries. Knowledge monopolies can use machine learning to develop competitive insights unavailable to every other platform participant. This information asymmetry stifles innovation, stokes the growth of the monopoly, and reinforces its ascendency. National or regional governance structures, such as laws and regulatory authorities, constrain economic monopolies deemed not in the public interest. We argue the need for legislation and an associated regulatory mechanism to curtail coercive data obligations, control, eliminate data rights exploitation, and prevent mergers and acquisitions that could create or extend knowledge monopolies…(More)”.
National Experimental Wellbeing Statistics (NEWS)
US Census: “The National Experimental Wellbeing Statistics (NEWS) project is a new experimental project to develop improved estimates of income, poverty, and other measures of economic wellbeing. Using all available survey, administrative, and commercial data, we strive to provide the best possible estimates of our nation and economy.
In this first release, we estimate improved income and poverty statistics for 2018 by addressing several possible sources of bias documented in prior research. We address biases from (1) unit nonresponse through improved weights, (2) missing income information in both survey and administrative data through improved imputation, and (3) misreporting by combining or replacing survey responses with administrative information. Reducing survey error using these techniques substantially affects key measures of well-being. With this initial set of experimental estimates, we estimate median household income is 6.3 percent higher than in survey estimates, and poverty is 1.1 percentage points lower. These changes are driven by subpopulations for which survey error is particularly relevant. For householders aged 65 and over, median household income is 27.3 percent higher, and poverty is 3.3 percentage points lower than in survey estimates. We do not find a significant impact on median household income for householders under 65 or on child poverty.
We will continue research (1) to estimate income at smaller geographies, through increased use of American Community Survey data, (2) addressing other potential sources of bias, (3) releasing additional years of statistics, particularly more timely estimates, and (4) extending the income concepts measured. As we advance the methods in future releases, we expect to revise these estimates…(More)”.
We need a much more sophisticated debate about AI
Article by Jamie Susskind: “Twentieth-century ways of thinking will not help us deal with the huge regulatory challenges the technology poses…The public debate around artificial intelligence sometimes seems to be playing out in two alternate realities.
In one, AI is regarded as a remarkable but potentially dangerous step forward in human affairs, necessitating new and careful forms of governance. This is the view of more than a thousand eminent individuals from academia, politics, and the tech industry who this week used an open letter to call for a six-month moratorium on the training of certain AI systems. AI labs, they claimed, are “locked in an out-of-control race to develop and deploy ever more powerful digital minds”. Such systems could “pose profound risks to society and humanity”.
On the same day as the open letter, but in a parallel universe, the UK government decided that the country’s principal aim should be to turbocharge innovation. The white paper on AI governance had little to say about mitigating existential risk, but lots to say about economic growth. It proposed the lightest of regulatory touches and warned against “unnecessary burdens that could stifle innovation”. In short: you can’t spell “laissez-faire” without “AI”.
The difference between these perspectives is profound. If the open letter is taken at face value, the UK government’s approach is not just wrong, but irresponsible. And yet both viewpoints are held by reasonable people who know their onions. They reflect an abiding political disagreement which is rising to the top of the agenda.
But despite this divergence there are four ways of thinking about AI that ought to be acceptable to both sides.
First, it is usually unhelpful to debate the merits of regulation by reference to a particular crisis (Cambridge Analytica), technology (GPT-4), person (Musk), or company (Meta). Each carries its own problems and passions. A sound regulatory system will be built on assumptions that are sufficiently general in scope that they will not immediately be superseded by the next big thing. Look at the signal, not the noise…(More)”.
Can A.I. and Democracy Fix Each Other?
Peter Coy at The New York Times: “Democracy isn’t working very well these days, and artificial intelligence is scaring the daylights out of people. Some creative people are looking at those two problems and envisioning a solution: Democracy fixes A.I., and A.I. fixes democracy.
Attitudes about A.I. are polarized, with some focusing on its promise to amplify human potential and others dwelling on what could go wrong (and what has already gone wrong). We need to find a way out of the impasse, and leaving it to the tech bros isn’t the answer. Democracy — giving everyone a voice on policy — is clearly the way to go.
Democracy can be taken hostage by partisans, though. That’s where artificial intelligence has a role to play. It can make democracy work better by surfacing ideas from everyone, not just the loudest. It can find surprising points of agreement among seeming antagonists and summarize and digest public opinion in a way that’s useful to government officials. Assisting democracy is a more socially valuable function for large language models than, say, writing commercials for Spam in iambic pentameter.The goal, according to the people I spoke to, is to make A.I. part of the solution, not just part of the problem…(More)” (See also: Where and when AI and CI meet: exploring the intersection of artificial and collective intelligence towards the goal of innovating how we govern…)”.
Protecting the integrity of survey research
Paper by Jamieson, Kathleen Hall, et al: “Although polling is not irredeemably broken, changes in technology and society create challenges that, if not addressed well, can threaten the quality of election polls and other important surveys on topics such as the economy. This essay describes some of these challenges and recommends remediations to protect the integrity of all kinds of survey research, including election polls. These 12 recommendations specify ways that survey researchers, and those who use polls and other public-oriented surveys, can increase the accuracy and trustworthiness of their data and analyses. Many of these recommendations align practice with the scientific norms of transparency, clarity, and self-correction. The transparency recommendations focus on improving disclosure of factors that affect the nature and quality of survey data. The clarity recommendations call for more precise use of terms such as “representative sample” and clear description of survey attributes that can affect accuracy. The recommendation about correcting the record urges the creation of a publicly available, professionally curated archive of identified technical problems and their remedies. The paper also calls for development of better benchmarks and for additional research on the effects of panel conditioning. Finally, the authors suggest ways to help people who want to use or learn from survey research understand the strengths and limitations of surveys and distinguish legitimate and problematic uses of these methods…(More)”.
Advancing Technology for Democracy
The White House: “The first wave of the digital revolution promised that new technologies would support democracy and human rights. The second saw an authoritarian counterrevolution. Now, the United States and other democracies are working together to ensure that the third wave of the digital revolution leads to a technological ecosystem characterized by resilience, integrity, openness, trust and security, and that reinforces democratic principles and human rights.
Together, we are organizing and mobilizing to ensure that technologies work for, not against, democratic principles, institutions, and societies. In so doing, we will continue to engage the private sector, including by holding technology platforms accountable when they do not take action to counter the harms they cause, and by encouraging them to live up to democratic principles and shared values…
Key deliverables announced or highlighted at the second Summit for Democracy include:
- National Strategy to Advance Privacy-Preserving Data Sharing and Analytics. OSTP released a National Strategy to Advance Privacy-Preserving Data Sharing and Analytics, a roadmap for harnessing privacy-enhancing technologies, coupled with strong governance, to enable data sharing and analytics in a way that benefits individuals and society, while mitigating privacy risks and harms and upholding democratic principles.
- National Objectives for Digital Assets Research and Development. OSTP also released a set of National Objectives for Digital Assets Research and Development, whichoutline its priorities for the responsible research and development (R&D) of digital assets. These objectives will help developers of digital assets better reinforce democratic principles and protect consumers by default.
- Launch of Trustworthy and Responsible AI Resource Center for Risk Management. NIST announced a new Resource Center, which is designed as a one-stop-shop website for foundational content, technical documents, and toolkits to enable responsible use of AI. Government, industry, and academic stakeholders can access resources such as a repository for AI standards, measurement methods and metrics, and data sets. The website is designed to facilitate the implementation and international alignment with the AI Risk Management Framework. The Framework articulates the key building blocks of trustworthy AI and offers guidance for addressing them.
- International Grand Challenges on Democracy-Affirming Technologies. Announced at the first Summit, the United States and the United Kingdom carried out their joint Privacy Enhancing Technology Prize Challenges. IE University, in partnership with the U.S. Department of State, hosted the Tech4Democracy Global Entrepreneurship Challenge. The winners, selected from around the world, were featured at the second Summit….(More)”.
Law, AI, and Human Rights
Article by John Croker: “Technology has been at the heart of two injustices that courts have labelled significant miscarriages of justice. The first example will be familiar now to many people in the UK: colloquially known as the ‘post office’ or ‘horizon’ scandal. The second is from Australia, where the Commonwealth Government sought to utilise AI to identify overpayment in the welfare system through what is colloquially known as the ‘Robodebt System’. The first example resulted in the most widespread miscarriage of justice in the UK legal system’s history. The second example was labelled “a shameful chapter” in government administration in Australia and led to the government unlawfully asserting debts amounting to $1.763 billion against 433,000 Australians, and is now the subject of a Royal Commission seeking to identify how public policy failures could have been made on such a significant scale.
Both examples show that where technology and AI goes wrong, the scale of the injustice can result in unprecedented impacts across societies….(More)”.
China’s fake science industry: how ‘paper mills’ threaten progress
Article by Eleanor Olcott, Clive Cookson and Alan Smith at the Financial Times: “…Over the past two decades, Chinese researchers have become some of the world’s most prolific publishers of scientific papers. The Institute for Scientific Information, a US-based research analysis organisation, calculated that China produced 3.7mn papers in 2021 — 23 per cent of global output — and just behind the 4.4mn total from the US.
At the same time, China has been climbing the ranks of the number of times a paper is cited by other authors, a metric used to judge output quality. Last year, China surpassed the US for the first time in the number of most cited papers, according to Japan’s National Institute of Science and Technology Policy, although that figure was flattered by multiple references to Chinese research that first sequenced the Covid-19 virus genome.
The soaring output has sparked concern in western capitals. Chinese advances in high-profile fields such as quantum technology, genomics and space science, as well as Beijing’s surprise hypersonic missile test two years ago, have amplified the view that China is marching towards its goal of achieving global hegemony in science and technology.
That concern is a part of a wider breakdown of trust in some quarters between western institutions and Chinese ones, with some universities introducing background checks on Chinese academics amid fears of intellectual property theft.
But experts say that China’s impressive output masks systemic inefficiencies and an underbelly of low-quality and fraudulent research. Academics complain about the crushing pressure to publish to gain prized positions at research universities…(More)”.
What We Gain from More Behavioral Science in the Global South
Article by Pauline Kabitsis and Lydia Trupe: “In recent years, the field has been critiqued for applying behavioral science at the margins, settling for small but statistically significant effect sizes. Critics have argued that by focusing our efforts on nudging individuals to increase their 401(k) contributions or to reduce their so-called carbon footprint, we have ignored the systemic drivers of important challenges, such as fundamental flaws in the financial system and corporate responsibility for climate change. As Michael Hallsworth points out, however, the field may not be willfully ignoring these deeper challenges, but rather investing in areas of change that are likely easier to move, measure, and secure funding.
It’s been our experience working in the Global South that nudge-based solutions can provide short-term gains within current systems, but for lasting impact a focus beyond individual-level change is required. This is because the challenges in the Global South typically navigate fundamental problems, like enabling women’s reproductive choice, combatting intimate partner violence and improving food security among the world’s most vulnerable populations.
Our work at Common Thread focuses on improving behaviors related to health, like encouraging those persistently left behind to get vaccinated, and enabling Ukrainian refugees in Poland to access health and welfare services. We use a behavioral model that considers not just the individual biases that impact people’s behaviors, but the structural, social, interpersonal, and even historical context that triggers these biases and inhibits health seeking behaviors…(More)”.
The wisdom of crowds for improved disaster resilience: a near-real-time analysis of crowdsourced social media data on the 2021 flood in Germany
Paper by Mahsa Moghadas, Alexander Fekete, Abbas Rajabifard & Theo Kötter: “Transformative disaster resilience in times of climate change underscores the importance of reflexive governance, facilitation of socio-technical advancement, co-creation of knowledge, and innovative and bottom-up approaches. However, implementing these capacity-building processes by relying on census-based datasets and nomothetic (or top-down) approaches remains challenging for many jurisdictions. Web 2.0 knowledge sharing via online social networks, whereas, provides a unique opportunity and valuable data sources to complement existing approaches, understand dynamics within large communities of individuals, and incorporate collective intelligence into disaster resilience studies. Using Twitter data (passive crowdsourcing) and an online survey, this study draws on the wisdom of crowds and public judgment in near-real-time disaster phases when the flood disaster hit Germany in July 2021. Latent Dirichlet Allocation, an unsupervised machine learning technique for Topic Modeling, was applied to the corpora of two data sources to identify topics associated with different disaster phases. In addition to semantic (textual) analysis, spatiotemporal patterns of online disaster communication were analyzed to determine the contribution patterns associated with the affected areas. Finally, the extracted topics discussed online were compiled into five themes related to disaster resilience capacities (preventive, anticipative, absorptive, adaptive, and transformative). The near-real-time collective sensing approach reflected optimized diversity and a spectrum of people’s experiences and knowledge regarding flooding disasters and highlighted communities’ sociocultural characteristics. This bottom-up approach could be an innovative alternative to traditional participatory techniques of organizing meetings and workshops for situational analysis and timely unfolding of such events at a fraction of the cost to inform disaster resilience initiatives…(More)”.