Paper by Alice Xiang: “Debates in AI ethics often hinge on comparisons between AI and humans: which is more beneficial, which is more harmful, which is more biased, the human or the machine? These questions, however, are a red herring. They ignore what is most interesting and important about AI ethics: AI is a mirror. If a person standing in front of a mirror asked you, “Who is more beautiful, me or the person in the mirror?” the question would seem ridiculous. Sure, depending on the angle, lighting, and personal preferences of the beholder, the person or their reflection might appear more beautiful, but the question is moot. AI reflects patterns in our society, just and unjust, and the worldviews of its human creators, fair or biased. The question then is not which is fairer, the human or the machine, but what can we learn from this reflection of our society and how can we make AI fairer? This essay discusses the challenges to developing fairer AI, and how they stem from this reflective property…(More)”.
AI doomsayers funded by billionaires ramp up lobbying
Article by Brendan Borderlon: “Two nonprofits funded by tech billionaires are now directly lobbying Washington to protect humanity against the alleged extinction risk posed by artificial intelligence — an escalation critics see as a well-funded smokescreen to head off regulation and competition.
The similarly named Center for AI Policy and Center for AI Safety both registered their first lobbyists in late 2023, raising the profile of a sprawling influence battle that’s so far been fought largely through think tanks and congressional fellowships.
Each nonprofit spent close to $100,000 on lobbying in the last three months of the year. The groups draw money from organizations with close ties to the AI industry like Open Philanthropy, financed by Facebook co-founder Dustin Moskovitz, and Lightspeed Grants, backed by Skype co-founder Jaan Tallinn.
Their message includes policies like CAIP’s call for legislation that would hold AI developers liable for “severe harms,” require permits to develop “high-risk” systems and empower regulators to “pause AI projects if they identify a clear emergency.”
“[The] risks of AI remain neglected — and are in danger of being outpaced by the rapid rate of AI development,” Nathan Calvin, senior policy counsel at the CAIS Action Fund, said in an email.
Detractors see the whole enterprise as a diversion. By focusing on apocalyptic scenarios, critics claim, these well-funded groups are raising barriers to entry for smaller AI firms and shifting attention away from more immediate and concrete problems with the technology, such as its potential to eliminate jobs or perpetuate discrimination.
Until late last year, organizations working to focus Washington on AI’s existential threat tended to operate under the radar. Instead of direct lobbying, groups like Open Philanthropy funded AI staffers in Congress and poured money into key think tanks. The RAND Corporation, an influential think tank that played a key role in drafting President Joe Biden’s October executive order on AI, received more than $15 million from Open Philanthropy last year…(More)”.
Rethinking Privacy in the AI Era: Policy Provocations for a Data-Centric World
Paper by Jennifer King, Caroline Meinhardt: “In this paper, we present a series of arguments and predictions about how existing and future privacy and data protection regulation will impact the development and deployment of AI systems.
➜ Data is the foundation of all AI systems. Going forward, AI development will continue to increase developers’ hunger for training data, fueling an even greater race for data acquisition than we have already seen in past decades.
➜ Largely unrestrained data collection poses unique risks to privacy that extend beyond the individual level—they aggregate to pose societal-level harms that cannot be addressed through the exercise of individual data rights alone.
➜ While existing and proposed privacy legislation, grounded in the globally accepted Fair Information Practices (FIPs), implicitly regulate AI development, they are not sufficient to address the data acquisition race as well as the resulting individual and systemic privacy harms.
➜ Even legislation that contains explicit provisions on algorithmic decision-making and other forms of AI does not provide the data governance measures needed to meaningfully regulate the data used in AI systems.
➜ We present three suggestions for how to mitigate the risks to data privacy posed by the development and adoption of AI:
1. Denormalize data collection by default by shifting away from opt-out to opt-in data collection. Data collectors must facilitate true data minimization through “privacy by default” strategies and adopt technical standards and infrastructure for meaningful consent mechanisms.
2. Focus on the AI data supply chain to improve privacy and data protection. Ensuring dataset transparency and accountability across the entire life cycle must be a focus of any regulatory system that addresses data privacy.
3. Flip the script on the creation and management of personal data. Policymakers should support the development of new governance mechanisms and technical infrastructure (e.g., data intermediaries and data permissioning infrastructure) to support and automate the exercise of individual data rights and preferences…(More)”.
Blockchain and public service delivery: a lifetime cross-referenced model for e-government
Paper by Maxat Kassen: “The article presents the results of field studies, analysing the perspectives of blockchain developers on decentralised service delivery and elaborating on unique algorithms for lifetime ledgers to reliably and safely record e-government transactions in an intrinsically cross-referenced manner. New interesting technological niches of service delivery and emerging models of related data management in the industry were proposed and further elaborated such as the generation of unique lifetime personal data profiles, blockchain-driven cross-referencing of e-government metadata, parallel maintenance of serviceable ledgers for data identifiers and phenomena of blockchain ‘black holes’ to ensure reliable protection of important public, corporate and civic information…(More)”.
How Mental Health Apps Are Handling Personal Information
Article by Erika Solis: “…Before diving into the privacy policies of mental health apps, it’s necessary to distinguish between “personal information” and “sensitive information,” which are both collected by such apps. Personal information can be defined as information that is “used to distinguish or trace an individual’s identity.” Sensitive information, however, can be any data that, if lost, misused, or illegally modified, may negatively affect an individual’s privacy rights. While health information not under HIPAA has previously been treated as general personal information, states like Washington are implementing strong legislation that will cover a wide range of health data as sensitive, and have attendant stricter guidelines.
Legislation addressing the treatment of personal information and sensitive information varies around the world. Regulations like the General Data Protection Regulation (GDPR) in the EU, for example, require all types of personal information to be treated as being of equal importance, with certain special categories, including health data having slightly elevated levels of protection. Meanwhile, U.S. federal laws are limited in addressing applicable protections of information provided to a third party, so mental health app companies based in the United States can approach personal information in all sorts of ways. For instance, Mindspa, an app with chatbots that are only intended to be used when a user is experiencing an emergency, and Elomia, a mental health app that’s meant to be used at any time, don’t make distinctions between these contexts in their privacy policies. They also don’t distinguish between the potentially different levels of sensitivity associated with ordinary and crisis use.
Wysa, on the other hand, clearly indicates how it protects personal information. Making a distinction between personal and sensitive data, its privacy policy notes that all health-based information receives additional protection. Similarly, Limbic labels everything as personal information but notes that data, including health, genetic, and biometric, fall within a “special category” that requires more explicit consent than other personal information collected to be used…(More)”.
Data as a catalyst for philanthropy
Article by Stefaan Verhulst: “…In what follows, we offer five thoughts on how to advance Data Driven Philanthropy. These are operational strategies, specific steps that philanthropic organisations can take in order to harness the potential of data for the public good. At its broadest level, then, this article is about data stewardship in the 21st century. We seek to define how philanthropic organisations can be responsible custodians of data assets, both theirs and those of society at large. Fulfilling this role of data stewardship is a critical mission for the philanthropic sector and one of the most important roles it can play in helping to ensure that our ongoing process of digital transformation is more fair, inclusive, and aligned with the broader public interest…(More)”.
How tracking animal movement may save the planet
Article by Matthew Ponsford: “Researchers have been dreaming of an Internet of Animals. They’re getting closer to monitoring 100,000 creatures—and revealing hidden facets of our shared world….There was something strange about the way the sharks were moving between the islands of the Bahamas.
Tiger sharks tend to hug the shoreline, explains marine biologist Austin Gallagher, but when he began tagging the 1,000-pound animals with satellite transmitters in 2016, he discovered that these predators turned away from it, toward two ancient underwater hills made of sand and coral fragments that stretch out 300 miles toward Cuba. They were spending a lot of time “crisscrossing, making highly tortuous, convoluted movements” to be near them, Gallagher says.
It wasn’t immediately clear what attracted sharks to the area: while satellite images clearly showed the subsea terrain, they didn’t pick up anything out of the ordinary. It was only when Gallagher and his colleagues attached 360-degree cameras to the animals that they were able to confirm what they were so drawn to: vast, previously unseen seagrass meadows—a biodiverse habitat that offered a smorgasbord of prey.
The discovery did more than solve a minor mystery of animal behavior. Using the data they gathered from the sharks, the researchers were able to map an expanse of seagrass stretching across 93,000 square kilometers of Caribbean seabed—extending the total known global seagrass coverage by more than 40%, according to a study Gallagher’s team published in 2022. This revelation could have huge implications for efforts to protect threatened marine ecosystems—seagrass meadows are a nursery for one-fifth of key fish stocks and habitats for endangered marine species—and also for all of us above the waves, as seagrasses can capture carbon up to 35 times faster than tropical rainforests.
Animals have long been able to offer unique insights about the natural world around us, acting as organic sensors picking up phenomena that remain invisible to humans. More than 100 years ago, leeches signaled storms ahead by slithering out of the water; canaries warned of looming catastrophe in coal mines until the 1980s; and mollusks that close when exposed to toxic substances are still used to trigger alarms in municipal water systems in Minneapolis and Poland…(More)”.
Situating Data Sets: Making Public Data Actionable for Housing Justice
Paper by Anh-Ton Tran et al: “Activists, governments and academics regularly advocate for more open data. But how is data made open, and for whom is it made useful and usable? In this paper, we investigate and describe the work of making eviction data open to tenant organizers. We do this through an ethnographic description of ongoing work with a local housing activist organization. This work combines observation, direct participation in data work, and creating media artifacts, specifically digital maps. Our interpretation is grounded in D’Ignazio and Klein’s Data Feminism, emphasizing standpoint theory. Through our analysis and discussion, we highlight how shifting positionalities from data intermediaries to data accomplices affects the design of data sets and maps. We provide HCI scholars with three design implications when situating data for grassroots organizers: becoming a domain beginner, striving for data actionability, and evaluating our design artifacts by the social relations they sustain rather than just their technical efficacy…(More)”.
The U.S. Census Is Wrong on Purpose
Blog by David Friedman: “This is a story about data manipulation. But it begins in a small Nebraska town called Monowi that has only one resident, 90 year old Elsie Eiler.
The sign says “Monowi 1,” from Google Street View.
There used to be more people in Monowi. But little by little, the other residents of Monowi left or died. That’s what happened to Elsie’s own family — her children grew up and moved out and her husband passed away in 2004, leaving her as the sole resident. Now she votes for herself for Mayor, and pays herself taxes. Her husband Rudy’s old book collection became the town library, with Elsie as librarian.
But despite what you might imagine, Elsie is far from lonely. She runs a tavern that’s been in her family for 50 years, and has plenty of regulars from the town next door who come by every day to dine and chat.
I first read about Elsie more than 10 years ago. At the time, it wasn’t as well known a story but Elsie has since gotten a lot of coverage and become a bit of a minor celebrity. Now and then I still come across a new article, including a lovely photo essay in the New York Times and a short video on the BBC Travel site.
A Google search reveals many, many similar articles that all tell more or less the same story.
But then suddenly in 2021, there was a new wrinkle: According to the just-published 2020 U.S. Census data, Monowi now had 2 residents, doubling its population.
This came as a surprise to Elsie, who told a local newspaper, “Then someone’s been hiding from me, and there’s nowhere to live but my house.”
It turns out that nobody new had actually moved to Monowi without Elsie realizing. And the census bureau didn’t make a mistake. They intentionally changed the census data, adding one resident.
Why would they do that? Well, it turns out the census bureau sometimes moves residents around on paper in order to protect people’s privacy.
Full census data is only made available 72 years after the census takes place, in accordance with the creatively-named “72 year rule.” Until then, it is only available as aggregated data with individual identifiers removed. Still, if the population of a town is small enough, and census data for that town indicates, for example, that there is just one 90 year old woman and she lives alone, someone could conceivably figure out who that individual is.
So the census bureau sometimes moves people around to create noise in the data that makes that sort of identification a little bit harder…(More)”.
Are Evidence-Based Medicine and Public Health Incompatible?
Essay by Michael Schulson: “It’s a familiar pandemic story: In September 2020, Angela McLean and John Edmunds found themselves sitting in the same Zoom meeting, listening to a discussion they didn’t like.
At some point during the meeting, McLean — professor of mathematical biology at the Oxford University, dame commander of the Order of the British Empire, fellow of the Royal Society of London, and then-chief scientific adviser to the United Kingdom’s Ministry of Defense — sent Edmunds a message on WhatsApp.
“Who is this fuckwitt?” she asked.
The message was evidently referring to Carl Heneghan, director of the Center for Evidence-Based Medicine at Oxford. He was on Zoom that day, along with McLean and Edmunds and two other experts, to advise the British prime minister on the Covid-19 pandemic.
Their disagreement — recently made public as part of a British government inquiry into the Covid-19 response — is one small chapter in a long-running clash between two schools of thought within the world of health care.
McLean and Edmunds are experts in infectious disease modeling; they build elaborate simulations of pandemics, which they use to predict how infections will spread and how best to slow them down. Often, during the Covid-19 pandemic, such models were used alongside other forms of evidence to urge more restrictions to slow the spread of the disease. Heneghan, meanwhile, is a prominent figure in the world of evidence-based medicine, or EBM. The movement aims to help doctors draw on the best available evidence when making decisions and advising patients. Over the past 30 years, EBM has transformed the practice of medicine worldwide.
Whether it can transform the practice of public health — which focuses not on individuals, but on keeping the broader community healthy — is a thornier question…(More)”.