Paper by Julian House, Nicola Lacetera, Mario Macis & Nina Mazar: “We conducted a randomized controlled trial involving nearly 700 customer-service representatives (CSRs) in a Canadian government service agency to study whether providing CSRs with performance feedback with or without peer comparison affected their subsequent organ donor registration rates. Despite having no tie to remuneration or promotion, the provision of individual performance feedback three times over one year resulted in a 25% increase in daily signups, compared to otherwise similar encouragement and reminders. Adding benchmark information that compared CSRs performance to average and top peer performance did not further enhance this effect. Registrations increased more among CSRs whose performance was already above average, and there was no negative effect on lower-performing CSRs. A post-intervention survey showed that CSRs found the information included in the treatments helpful and encouraging. However, performance feedback without benchmark information increased perceived pressure to perform…(More)”.
Global healthcare fairness: We should be sharing more, not less, data
Paper by Kenneth P. Seastedt et al: “The availability of large, deidentified health datasets has enabled significant innovation in using machine learning (ML) to better understand patients and their diseases. However, questions remain regarding the true privacy of this data, patient control over their data, and how we regulate data sharing in a way that does not encumber progress or further potentiate biases for underrepresented populations. After reviewing the literature on potential reidentifications of patients in publicly available datasets, we argue that the cost—measured in terms of access to future medical innovations and clinical software—of slowing ML progress is too great to limit sharing data through large publicly available databases for concerns of imperfect data anonymization. This cost is especially great for developing countries where the barriers preventing inclusion in such databases will continue to rise, further excluding these populations and increasing existing biases that favor high-income countries. Preventing artificial intelligence’s progress towards precision medicine and sliding back to clinical practice dogma may pose a larger threat than concerns of potential patient reidentification within publicly available datasets. While the risk to patient privacy should be minimized, we believe this risk will never be zero, and society has to determine an acceptable risk threshold below which data sharing can occur—for the benefit of a global medical knowledge system….(More)”.
Data and displacement: Ethical and practical issues in data-driven humanitarian assistance for IDPs
Blog by Vicki Squire: “Ten years since the so-called “data revolution” (Pearn et al, 2022), the rise of “innovation” and the proliferation of “data solutions” has rendered the assessment of changing data practices within the humanitarian sector ever more urgent. New data acquisition modalities have provoked a range of controversies across multiple contexts and sites (e.g. Human Rights Watch, 2021, 2022a, 2022b). Moreover, a range of concerns have been raised about data sharing (e.g. Fast, 2022) and the inequities embedded within humanitarian data (e.g. Data Values, 2022).
With this in mind, the Data and Displacement project set out to explore the practical and ethical implications of data-driven humanitarian assistance in two contexts characterised by high levels of internal displacement: north-eastern Nigeria and South Sudan. Our interdisciplinary research team includes academics from each of the regions under analysis, as well as practitioners from the International Organization for Migration. From the start, the research was designed to centre the lived experiences of Internally Displaced Persons (IDPs), while also shedding light on the production and use of humanitarian data from multiple perspectives.
We conducted primary research during 2021-2022. Our research combines dataset analysis and visualisation techniques with a thematic analysis of 174 semi-structured qualitative interviews. In total we interviewed 182 people: 42 international data experts, donors, and humanitarian practitioners from a range of governmental and non-governmental organisations; 40 stakeholders and practitioners working with IDPs across north-eastern Nigeria and South Sudan (20 in each region); and 100 IDPs in camp-like settings (50 in each region). Our findings point to a disconnect between international humanitarian standards and practices on the ground, the need to revisit existing ethical guidelines such informed consent, and the importance of investing in data literacies…(More)”.
Can Smartphones Help Predict Suicide?
Ellen Barry in The New York Times: “In March, Katelin Cruz left her latest psychiatric hospitalization with a familiar mix of feelings. She was, on the one hand, relieved to leave the ward, where aides took away her shoelaces and sometimes followed her into the shower to ensure that she would not harm herself.
But her life on the outside was as unsettled as ever, she said in an interview, with a stack of unpaid bills and no permanent home. It was easy to slide back into suicidal thoughts. For fragile patients, the weeks after discharge from a psychiatric facility are a notoriously difficult period, with a suicide rate around 15 times the national rate, according to one study.
This time, however, Ms. Cruz, 29, left the hospital as part of a vast research project which attempts to use advances in artificial intelligence to do something that has eluded psychiatrists for centuries: to predict who is likely to attempt suicide and when that person is likely to attempt it, and then, to intervene.
On her wrist, she wore a Fitbit programmed to track her sleep and physical activity. On her smartphone, an app was collecting data about her moods, her movement and her social interactions. Each device was providing a continuous stream of information to a team of researchers on the 12th floor of the William James Building, which houses Harvard’s psychology department.
In the field of mental health, few new areas generate as much excitement as machine learning, which uses computer algorithms to better predict human behavior. There is, at the same time, exploding interest in biosensors that can track a person’s mood in real time, factoring in music choices, social media posts, facial expression and vocal expression.
Matthew K. Nock, a Harvard psychologist who is one of the nation’s top suicide researchers, hopes to knit these technologies together into a kind of early-warning system that could be used when an at-risk patient is released from the hospital…(More)”.
Cutting through complexity using collective intelligence
Blog by the UK Policy Lab: “In November 2021 we established a Collective Intelligence Lab (CILab), with the aim of improving policy outcomes by tapping into collective intelligence (CI). We define CI as the diversity of thought and experience that is distributed across groups of people, from public servants and domain experts to members of the public. We have been experimenting with a digital tool, Pol.is, to capture diverse perspectives and new ideas on key government priority areas. To date we have run eight debates on issues as diverse as Civil Service modernisation, fisheries management and national security. Across these debates over 2400 civil servants, subject matter experts and members of the public have participated…
From our experience using CILab on live policy issues, we have identified a series of policy use cases that echo findings from the government of Taiwan and organisations such as Nesta. These use cases include: 1) stress-testing existing policies and current thinking, 2) drawing out consensus and divergence on complex, contentious issues, and 3) identifying novel policy ideas.
1) Stress-testing existing policy and current thinking
CI could be used to gauge expert and public sentiment towards existing policy ideas by asking participants to discuss existing policies and current thinking on Pol.is. This is well suited to testing public and expert opinions on current policy proposals, especially where their success depends on securing buy-in and action from stakeholders. It can also help collate views and identify barriers to effective implementation of existing policy.
From the initial set of eight CILab policy debates, we have learnt that it is sometimes useful to design a ‘crossover point’ into the process. This is where part way through a debate, statements submitted by policymakers, subject matter experts and members of the public can be shown to each other, in a bid to break down groupthink across those groups. We used this approach in a Pol.is debate on a topic relating to UK foreign policy, and think it could help test how existing policies on complex areas such as climate change or social care are perceived within and outside government…(More)”
Hurricane Ian Destroyed Their Homes. Algorithms Sent Them Money
Article by Chris Stokel-Walker: “The algorithms that power Skai’s damage assessments are trained by manually labeling satellite images of a couple of hundred buildings in a disaster-struck area that are known to have been damaged. The software can then, at speed, detect damaged buildings across the whole affected area. A research paper on the underlying technology presented at a 2020 academic workshop on AI for disaster response claimed the auto-generated damage assessments match those of human experts with between 85 and 98 percent accuracy.
In Florida this month, GiveDirectly sent its push notification offering $700 to any user of the Providers app with a registered address in neighborhoods of Collier, Charlotte, and Lee Counties where Google’s AI system deemed more than 50 percent of buildings had been damaged. So far, 900 people have taken up the offer, and half of those have been paid. If every recipient takes up GiveDirectly’s offer, the organization will pay out $2.4 million in direct financial aid.
Some may be skeptical of automated disaster response. But in the chaos after an event like a hurricane making landfall, the conventional, human response can be far from perfect. Diaz points to an analysis GiveDirectly conducted looking at their work after Hurricane Harvey, which hit Texas and Louisiana in 2017, before the project with Google. Two out of the three areas that were most damaged and economically depressed were initially overlooked. A data-driven approach is “much better than what we’ll have from boots on the ground and word of mouth,” Diaz says.
GiveDirectly and Google’s hands-off, algorithm-led approach to aid distribution has been welcomed by some disaster assistance experts—with caveats. Reem Talhouk, a research fellow at Northumbria University’s School of Design and Centre for International Development in the UK, says that the system appears to offer a more efficient way of delivering aid. And it protects the dignity of recipients, who don’t have to queue up for handouts in public…(More)”.
Is This the Beginning of the End of the Internet?
Article by Charlie Warzel: “…occasionally, something happens that is so blatantly and obviously misguided that trying to explain it rationally makes you sound ridiculous. Such is the case with the Fifth Circuit Court of Appeals’s recent ruling in NetChoice v. Paxton. Earlier this month, the court upheld a preposterous Texas law stating that online platforms with more than 50 million monthly active users in the United States no longer have First Amendment rights regarding their editorial decisions. Put another way, the law tells big social-media companies that they can’t moderate the content on their platforms. YouTube purging terrorist-recruitment videos? Illegal. Twitter removing a violent cell of neo-Nazis harassing people with death threats? Sorry, that’s censorship, according to Andy Oldham, a judge of the United States Court of Appeals and the former general counsel to Texas Governor Greg Abbott.
A state compelling social-media companies to host all user content without restrictions isn’t merely, as the First Amendment litigation lawyer Ken White put it on Twitter, “the most angrily incoherent First Amendment decision I think I’ve ever read.” It’s also the type of ruling that threatens to blow up the architecture of the internet. To understand why requires some expertise in First Amendment law and content-moderation policy, and a grounding in what makes the internet a truly transformational technology. So I called up some legal and tech-policy experts and asked them to explain the Fifth Circuit ruling—and its consequences—to me as if I were a precocious 5-year-old with a strange interest in jurisprudence…(More)”
The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future
Book by Orly Lobel: “Much has been written about the challenges tech presents to equality and democracy. But we can either criticize big data and automation or steer it to do better. Lobel makes a compelling argument that while we cannot stop technological development, we can direct its course according to our most fundamental values.
With provocative insights in every chapter, Lobel masterfully shows that digital technology frequently has a comparative advantage over humans in detecting discrimination, correcting historical exclusions, subverting long-standing stereotypes, and addressing the world’s thorniest problems: climate, poverty, injustice, literacy, accessibility, speech, health, and safety.
Lobel’s vivid examples—from labor markets to dating markets—provide powerful evidence for how we can harness technology for good. The book’s incisive analysis and elegant storytelling will change the debate about technology and restore human agency over our values…(More)”.
Can critical policy studies outsmart AI? Research agenda on artificial intelligence technologies and public policy
Paper by Regine Paul: “The insertion of artificial intelligence technologies (AITs) and data-driven automation in public policymaking should be a metaphorical wake-up call for critical policy analysts. Both its wide representation as techno-solutionist remedy in otherwise slow, inefficient, and biased public decision-making and its regulation as a matter of rational risk analysis are conceptually flawed and democratically problematic. To ‘outsmart’ AI, this article stimulates the articulation of a critical research agenda on AITs and public policy, outlining three interconnected lines of inquiry for future research: (1) interpretivist disclosure of the norms and values that shape perceptions and uses of AITs in public policy, (2) exploration of AITs in public policy as a contingent practice of complex human-machine interactions, and (3) emancipatory critique of how ‘smart’ governance projects and AIT regulation interact with (global) inequalities and power relations…(More)”.
Call it data liberation day: Patients can now access all their health records digitally
Article by Casey Ross: “The American Revolution had July 4. The allies had D-Day. And now U.S. patients, held down for decades by information hoarders, can rally around a new turning point, October 6, 2022 — the day they got their health data back.
Under federal rules taking effect Thursday, health care organizations must give patients unfettered access to their full health records in digital format. No more long delays. No more fax machines. No more exorbitant charges for printed pages.
Just the data, please — now…The new federal rules — passed under the 21st Century Cures Act — are designed to shift the balance of power to ensure that patients can not only get their data, but also choose who else to share it with. It is the jumping-off point for a patient-mediated data economy that lets consumers in health care benefit from the fluidity they’ve had for decades in banking: they can move their information easily and electronically, and link their accounts to new services and software applications.
“To think that we actually have greater transparency about our personal finances than about our own health is quite an indictment,” said Isaac Kohane, a professor of biomedical informatics at Harvard Medical School. “This will go some distance toward reversing that.”
Even with the rules now in place, health data experts said change will not be fast or easy. Providers and other data holders — who have dug in their heels at every step — can still withhold information under certain exceptions. And many questions remain about protocols for sharing digital records, how to verify access rights, and even what it means to give patients all their data. Does that extend to every measurement in the ICU? Every log entry? Every email? And how will it all get standardized?…(More)”