Report by Nicki Lisa Cole, Eva Kormann, Thomas Klebel, Simon Apartis and Tony Ross-Hellauer: “Open Science (OS) aims, in part, to drive greater societal impact of academic research. Government, funder and institutional policies state that it should further democratize research and increase learning and awareness, evidence-based policy-making, the relevance of research to society’s problems, and public trust in research. Yet, measuring the societal impact of OS has proven challenging and synthesized evidence of it is lacking. This study fills this gap by systematically scoping the existing evidence of societal impact driven by OS and its various aspects, including Citizen Science (CS), Open Access (OA), Open/FAIR Data (OFD), Open Code/Software and others. Using the PRISMA Extension for Scoping Reviews and searches conducted in Web of Science, Scopus and relevant grey literature, we identified 196 studies that contain evidence of societal impact. The majority concern CS, with some focused on OA, and only a few addressing other aspects. Key areas of impact found are education and awareness, climate and environment, and social engagement. We found no literature documenting evidence of the societal impact of OFD and limited evidence of societal impact in terms of policy, health, and trust in academic research. Our findings demonstrate a critical need for additional evidence and suggest practical and policy implications…(More)”.
AI, data governance and privacy
OECD Report: “Recent AI technological advances, particularly the rise of generative AI, have raised many data governance and privacy questions. However, AI and privacy policy communities often address these issues independently, with approaches that vary between jurisdictions and legal systems. These silos can generate misunderstandings, add complexities in regulatory compliance and enforcement, and prevent capitalising on commonalities between national frameworks. This report focuses on the privacy risks and opportunities stemming from recent AI developments. It maps the principles set in the OECD Privacy Guidelines to the OECD AI Principles, takes stock of national and regional initiatives, and suggests potential areas for collaboration. The report supports the implementation of the OECD Privacy Guidelines alongside the OECD AI Principles. By advocating for international co-operation, the report aims to guide the development of AI systems that respect and support privacy…(More)”.
How Philanthropy Can Make Sure Data Is Used to Help — Not Harm
Article by Ryan Merkley: “We are living in an extractive data economy. Every day, people generate a firehose of new data on hundreds of apps and services. These data are often sold by data brokers indiscriminately, embedded into user profiles for ad targeting, and used to train large language models such as Chat GPT. Communities and individuals should benefit from data made by and about them, but they don’t.
That needs to change. A report released last month by the Aspen Institute, where I work, calls on foundations and other donors to lead the way in addressing these disparities and promoting responsible uses of data in their own practices and in the work of grantees. Among other things, it suggests that funders encourage grantees to make sure their data accurately represents the communities they serve and support their efforts to make that data available and accessible to constituents…(More)”.
Unlocking the Potential of Data: Innovative Policies for Responsible Data Reuse and Addressing Data Asymmetries
Testimony by Stefaan Verhulst to the German Bundestag: “Let me begin by highlighting the potential of data when used and reused responsibly. Although we hear much about the risks of using data–and many of the fears are indeed justified–it’s also important to keep in mind the very real possibilities that data offers for advancing the public good.
We live in a datafied world, characterized by an unprecedented supply–even glut–of data. In this world, data has become a critical resource for informing policy and decision-making processes. When properly analyzed and utilized, data can play a critical role in helping policymakers–and other stakeholders–address a range of critical problems, in sectors as diverse as public health, climate, innovation and economic development, combating urban decay–and much more.
Sometimes this data is readily available. Most of the time it is not. One of the areas with the biggest potential–yet also significant challenges–is data reuse – data already collected for one purpose using it for another. Data reuse can provide invaluable insights into current phenomena, help us understand the causes of emerging trends, and guide us in developing effective solutions to pressing challenges. Moreover, analysis from data re-use can serve as a powerful tool for anticipating future developments and prescribing targeted interventions…
Despite the very potential of data and data reuse, it’s undeniable we face significant challenges in realizing data’s full societal value.
One of the primary obstacles is a lack of access to high-quality, timely data by the public sector, civil society, and other groups that are working toward the public good.
We live in a paradoxical situation today, marked both by the availability of an unprecedented amount of data, but also by unprecedented asymmetries in access to that data for reuse in the public interest.
I believe that the growing asymmetries between those who have data (often from the private sector) and those who are best positioned to use it for the public good, represents one of the major challenges of our era.
Data policy to date has primarily focused on preventing the misuse of data, often for valid reasons as mentioned earlier. However, this approach has inadvertently overlooked the missed uses of data – the opportunities we fail to capitalize on due to overly restrictive policies or lack of innovative frameworks for data sharing and utilization…
Given these challenges, what can policymakers do? What steps can policymakers such as yourselves – and other stakeholders, from the private sector, academia and civil society – take to help maximize the potential of our datafied society and economy, and to ensure that the benefits of our data age are maximized in as equitable and inclusive a manner as possible?..(More)” (German) (See also: Experten: Innovative Ansätze in der Datenpolitik nötig).
The 4M Roadmap: A Higher Road to Profitability by Using Big Data for Social Good
Report by Brennan Lake: “As the private sector faces conflicting pressures to either embrace or shun socially responsible practices, companies with privately held big-data assets must decide whether to share access to their data for public good. While some managers object to data sharing over concerns of privacy and product cannibalization, others launch well intentioned yet short-lived CSR projects that fail to deliver on lofty goals.
By embedding Shared-Value principles into ‘Data-for-Good’ programs, data-rich firms can launch responsible data-sharing initiatives that minimize risk, deliver sustained impact, and improve overall competitiveness in the process.
The 4M Roadmap by Brennan Lake, a Big-Data and Social Impact professional, guides managers to adopt a ‘Data-for-Good’ model that emphasizes four key pillars of value-creation: Mission, Messaging, Methods, and Monetization. Through deep analysis and private-sector case studies, The 4M Roadmap demonstrates how companies can engage in responsible data sharing to benefit society and business alike…(More)”.
Preparing Researchers for an Era of Freer Information
Article by Peter W.B. Phillips: “If you Google my name along with “Monsanto,” you will find a series of allegations from 2013 that my scholarly work at the University of Saskatchewan, focused on technological change in the global food system, had been unduly influenced by corporations. The allegations made use of seven freedom of information (FOI) requests. Although leadership at my university determined that my publications were consistent with university policy, the ensuing media attention, I feel, has led some colleagues, students, and partners to distance themselves to avoid being implicated by association.
In the years since, I’ve realized that my experience is not unique. I have communicated with other academics who have experienced similar FOI requests related to genetically modified organisms in the United States, Canada, England, Netherlands, and Brazil. And my field is not the only one affected: a 2015 Union of Concerned Scientists report documented requests in multiple states and disciplines—from history to climate science to epidemiology—as well as across ideologies. In the University of California system alone, researchers have received open records requests related to research on the health effects of toxic chemicals, the safety of abortions performed by clinicians rather than doctors, and the green energy production infrastructure. These requests are made possible by laws that permit anyone, for any reason, to gain access to public agencies’ records.
These open records campaigns, which are conducted by individuals and groups across the political spectrum, arise in part from the confluence of two unrelated phenomena: the changing nature of academic research toward more translational, interdisciplinary, and/or team-based investigations and the push for more transparency in taxpayer-funded institutions. Neither phenomenon is inherently negative; in fact, there are strong advantages for science and society in both trends. But problems arise when scholars are caught between them—affecting the individuals involved and potentially influencing the ongoing conduct of research…(More)”
Exploring Visitor Density Trends in Rest Areas Through Google Maps Data and Data Mining
Paper by Marita Prasetyani, R. Rizal Isnanto and Catur Edi Widodo: “Rest areas play a vital role in ensuring the safety and comfort of travelers. This study examines the visitor density at the toll and non-toll rest areas using data mining techniques applied to Google Maps Places data. By utilizing extensive information from Google Maps, the research aims to uncover patterns and trends in visitor behavior and pinpoint peak usage times. The findings can guide improved planning and management of rest areas, thereby enhancing the overall travel experience for road users and further research to determine the location of the new rest area.Understanding patterns or trends in visitor density at rest areas involves analyzing the time of day, location, and other factors influencing the density level. Understanding these trends can provide essential insights for rest area management, infrastructure planning, and the establishment of new rest areas.Data from Google Maps provides an invaluable source of real-time and historical information, enabling accurate and in-depth analysis of visitor behavior.Data mining helps identify relationships not immediately apparent in the data, providing a deeper understanding and supporting data-driven decision-making…(More)”.
Not all ‘open source’ AI models are actually open: here’s a ranking
Article by Elizabeth Gibney: “Technology giants such as Meta and Microsoft are describing their artificial intelligence (AI) models as ‘open source’ while failing to disclose important information about the underlying technology, say researchers who analysed a host of popular chatbot models.
The definition of open source when it comes to AI models is not yet agreed, but advocates say that ’full’ openness boosts science, and is crucial for efforts to make AI accountable. What counts as open source is likely to take on increased importance when the European Union’s Artificial Intelligence Act comes into force. The legislation will apply less strict regulations to models that are classed as open.
Some big firms are reaping the benefits of claiming to have open-source models, while trying “to get away with disclosing as little as possible”, says Mark Dingemanse, a language scientist at Radboud University in Nijmegen, the Netherlands. This practice is known as open-washing.
“To our surprise, it was the small players, with relatively few resources, that go the extra mile,” says Dingemanse, who together with his colleague Andreas Liesenfeld, a computational linguist, created a league table that identifies the most and least open models (see table). They published their findings on 5 June in the conference proceedings of the 2024 ACM Conference on Fairness, Accountability and Transparency…(More)”.
Artificial Intelligence Is Making The Housing Crisis Worse
Article by Rebecca Burns: “When Chris Robinson applied to move into a California senior living community five years ago, the property manager ran his name through an automated screening program that reportedly used artificial intelligence to detect “higher-risk renters.” Robinson, then 75, was denied after the program assigned him a low score — one that he later learned was based on a past conviction for littering.
Not only did the crime have little bearing on whether Robinson would be a good tenant, it wasn’t even one that he’d committed. The program had turned up the case of a 33-year-old man with the same name in Texas — where Robinson had never lived. He eventually corrected the error but lost the apartment and his application fee nonetheless, according to a federal class-action lawsuit that moved towards settlement this month. The credit bureau TransUnion, one of the largest actors in the multi-billion-dollar tenant screening industry, agreed to pay $11.5 million to resolve claims that its programs violated fair credit reporting laws.
Landlords are increasingly turning to private equity-backed artificial intelligence (AI) screening programs to help them select tenants, and resulting cases like Robinson’s are just the tip of the iceberg. The prevalence of incorrect, outdated, or misleading information in such reports is increasing costs and barriers to housing, according to a recent report from federal consumer regulators.
Even when screening programs turn up real data, housing and privacy advocates warn that opaque algorithms are enshrining high-tech discrimination in an already unequal housing market — the latest example of how AI can end up amplifying existing biases…(More)”.
What the Arrival of A.I. Phones and Computers Means for Our Data
Article by Brian X. Chen: “Apple, Microsoft and Google are heralding a new era of what they describe as artificially intelligent smartphones and computers. The devices, they say, will automate tasks like editing photos and wishing a friend a happy birthday.
But to make that work, these companies need something from you: more data.
In this new paradigm, your Windows computer will take a screenshot of everything you do every few seconds. An iPhone will stitch together information across many apps you use. And an Android phone can listen to a call in real time to alert you to a scam.
Is this information you are willing to share?
This change has significant implications for our privacy. To provide the new bespoke services, the companies and their devices need more persistent, intimate access to our data than before. In the past, the way we used apps and pulled up files and photos on phones and computers was relatively siloed. A.I. needs an overview to connect the dots between what we do across apps, websites and communications, security experts say.
“Do I feel safe giving this information to this company?” Cliff Steinhauer, a director at the National Cybersecurity Alliance, a nonprofit focusing on cybersecurity, said about the companies’ A.I. strategies.
All of this is happening because OpenAI’s ChatGPT upended the tech industry nearly two years ago. Apple, Google, Microsoft and others have since overhauled their product strategies, investing billions in new services under the umbrella term of A.I. They are convinced this new type of computing interface — one that is constantly studying what you are doing to offer assistance — will become indispensable.
The biggest potential security risk with this change stems from a subtle shift happening in the way our new devices work, experts say. Because A.I. can automate complex actions — like scrubbing unwanted objects from a photo — it sometimes requires more computational power than our phones can handle. That means more of our personal data may have to leave our phones to be dealt with elsewhere.
The information is being transmitted to the so-called cloud, a network of servers that are processing the requests. Once information reaches the cloud, it could be seen by others, including company employees, bad actors and government agencies. And while some of our data has always been stored in the cloud, our most deeply personal, intimate data that was once for our eyes only — photos, messages and emails — now may be connected and analyzed by a company on its servers…(More)”.