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Masks for COVID: Updating the evidence

These are notes I took whilst preparing a paper on mask efficacy from Nov 2021 to Jan 2022. In the end, I gave up on the paper, because I felt like people had given up on masks, so there wasn’t much point in finishing it. I’ve decided to publish these notes in the hope some people will find them a useful starting point for their own research. My previous paper on this topic, in which I led a team of 19 experts, was written in April 2020, and published here in the Proceedings of the National Academy of Science.


The rise of better masks

In the US, 400 million N95 masks are being distributed for free, coming from the 750 million stored in the US’ Strategic National Stockpile. A similar campaign to distribute 650 millions masks in the US in 2020 was cancelled.

KN95 masks are being given to US congressional staff, and masks are required for federal workers and whilst in federal buildings.

The Los Angeles school district has required students to upgrade from cloth masks to “well-fitting, non-cloth masks with a nose wire”.

Masks work

A review paper discussed both lab evidence and empirical evidence for the importance of face masks, with eight “seminal studies” showing a reduction in transmission when masks are used, and one Danish study of surgical masks with “several design limitations” which “demonstrated only a modest benefit in limiting COVID-19 transmission”. The authors note that “laboratory studies have demonstrated the ability of surgical masks to block SARS-COV-2 and other viruses”, with the masks “60%–70% effective at protecting others and 50% effective at protecting the wearer”.

An evidence review from early in the pandemic concluded that “given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control”. It noted that “by the end of June 2020, nearly 90% of the global population lived in regions that had nearly universal mask use, or had laws requiring mask use in some public locations.” The review said that “There has been one controlled trial of mask use for influenza control in the general community. The study looked at Australian households, was not done during a pandemic, and was done without any enforcement of compliance” – and yet still found “masks as a group had protective efficacy in excess of 80% against clinical influenza-like illness.”

An observational study of Beijing households analyzed the impact of mask use in the community on COVID-19 transmission, finding that face masks were 79% effective in preventing transmission, if used by all household members prior to symptoms occurring.

One study used a multiple regression of policy interventions and country and population characteristics to infer the relationship between mask use and SARS-CoV-2 transmission. It found that transmission was around 7.5 times higher in countries that did not have a mask mandate or universal mask use, a result similar to that found in an analogous study of fewer countries. Similar results were found by numerous other papers.

A mathematical model of mask use estimates that mask wearing reduces the reproduction number R by (1−mp)^2, where m is the efficacy of trapping viral particles inside the mask, and p is the percentage of the population that wears masks.

A report in Nature explained that researchers running a randomized controlled trial (RCT) of community mask use in Bangladesh “began by developing a strategy to promote mask wearing, with measures such as reminders from health workers in public places. This ultimately tripled mask usage, from only 13% in control villages to 42% in villages where it was encouraged”, and “then compared numbers of COVID-19 cases in control villages and the treatment communities”. They found that the number of infections in mask wearing communities decreased, with a reduction of COVID symptoms using surgical masks to 0.87 times the incidence in unmasked communities, and 0.91 times when using cloth masks. The report noted that “the researchers suggest that the true risk reduction is probably much greater, in part because they did no SARS-CoV-2 testing of people without symptoms or whose symptoms did not meet the World Health Organization’s definition of the disease.” The researchers concluded that “promoting community mask-wearing can improve public health”.

The Johns Hopkins School of Public Health reviewed the work and concluded that “This study is the largest and best-designed randomized controlled trial to date of a realistic non-pharmaceutical intervention on SARS-CoV-2 transmission.”

A paper investigating an upper bound on one-to-one exposure to infectious human respiratory particles concludes that “face masks significantly reduce the risk of SARS-CoV-2 infection compared to social distancing. We find a very low risk of infection when everyone wears a face mask, even if it doesn’t fit perfectly on the face.” They calculate that “social distancing alone, even at 3.0 m between two speaking individuals, leads to an upper bound of 90% for risk of infection after a few minutes”, but that when both source and susceptible wear a well-fitting FFP2 mask, there is only 0.4% after one hour of contact. They found that to achieve good fit it is important to mold the nose piece wire to the size of the nose, rather than leaving it in a sharp folded position.

A similar study “quantifies the extent to which transmission risk is reduced in large rooms with high air exchange rates, increased for more vigorous respiratory activities, and dramatically reduced by the use of face masks.” The authors describe the six-foot rule widely used to ensure social distancing as “a guideline that offers little protection from pathogen-bearing aerosol droplets sufficiently small to be continuously mixed through an indoor space.” Instead, they develop a safety guideline based on cumulative exposure time,” the product of the number of occupants and their time in an enclosed space. In particular, they identify that the greatest risk comes in places where people are speaking (other than quietly) or singing, and that “the benefit of face masks is immediately apparent”, due to the multiplicative effect when both source and susceptible wear a mask. They further note that “Air filtration has a less dramatic effect than face mask use in increasing the CET bound. Nevertheless, it does offer a means of mitigating indoor transmission with greater comfort, albeit at greater cost.”

Another study of the combined impacts of ventilation and mask effective filtration efficiency in classroom settings found that “ventilation alone is not able to achieve probabilities <0.01 (1%)” of transmitting COVID in a classroom. However, they found that good masks reduce infection probability by >5× in some cases, and that “reductions provided by ventilation and masks are synergistic and multiplicative”. However they also noted that “most masks fit poorly”, recommending that work be done to ensure that high quality masks are used.

Similar results were found in a study of community public health interventions, which concluded that “control the pandemic, our models suggest that high adherence to social distance is necessary to curb the spread of the disease, and that wearing face masks provides optimal protection even if only a small portion of the population comply with social distance”.

Guidance from the independent scientific advisory group OzSAGE points out “that school children are able to wear masks. As an example, all children over two years of age in San Francisco are required to wear masks at school”.

Omicron changes the game

An analysis of fine aerosol emissions found that, compared to the original wild type (WT) virus:

“Delta and Omicron both also have increased transmissibility: the number of cells infected for a given number of ribonucleic acid (RNA) virus copies was found to be doubled and quadrupled respectively. Furthermore, Omicron also seems to be better at evading the immune system. This implies that the critical dose of virus copies above which a situation is potentially infectious needs to be lowered. For the WT, we had proposed a critical dose of 500 virus copies. If the above-mentioned capacity to infect cells translates into an infection risk, this would imply a critical dose of around 300 virus copies for Delta and around 100 virus copies for Omicron.”

The study finds that “surgical masks are no longer sufficient in most public settings, while correctly fitted FFP2 respirators still provide sufficient protection, except in high aerosol producing situations such as singing or shouting.”

Data from Hong Kong shows that “Omicron SARS-CoV-2 infects and multiplies 70 times faster than the Delta variant and original SARS-CoV-2 in human bronchus”.

A study of transmission in Danish households estimated the secondary attack rate (SAR) of omicron compared to delta, finding it 1.2 times higher for unvaccinated people, 2.6 times higher for double-dosed, and 3.7 times higher for boosted. The authors conclude that “the rapid spread of the Omicron VOC primarily can be ascribed to the immune evasiveness”.

According to UK statistics, the risk of hospitalization from omicron when unvaccinated is about the same as the wildtype virus, which is about half the risk of the delta variant.

The journal Infection Control Today reported that many experts are concerned that “‘Omicron the Pandemic Killer’ Idea Ignores Dangers of Long COVID”:

“Linda Spaulding, RN-BC, CIC, CHEC, CHOP, a member of Infection Control Today®’s Editorial Advisory Board (EAB), says that she’s “seen athletes in their 20s on the wait list for double lung transplants because of long COVID. That’s something that has long-term consequences. Some people talk of COVID fog. They just can’t put their thoughts together.” In addition, even the treatments for those with long COVID can put toll on a patient’s body.”

“As noted by Kevin Kavanagh, MD, another member of ICT®’s EAB, a core difficulty in society’s attempt to guide COVID-19 from pandemic to endemic is that COVID is not just a respiratory virus. Kavanagh wrote in October that SARS-CoV-2 is similar to HIV because it can “silently spread throughout the host’s body and attack almost every organ.””

Better masks work better

The US Centers for Disease Control and Prevention (CDC) explains that:

“Loosely woven cloth products provide the least protection, layered finely woven products offer more protection, well-fitting disposable surgical masks and KN95s offer even more protection, and well-fitting NIOSH-approved respirators (including N95s) offer the highest level of protection.”

Unfortunately “well-fitting disposable surgical masks” do not exist out of the box, since there are large gaps on each side of the mask. Surgical masks require modifications to achive a good fit. That’s because they are made to stop liquid splashes during surgery, rather than made to stop airborne transmission. There are two methods shown by the CDC to improve fit:

  • Knot and Tuck: Tying the sides of the mask together to remove the side gap
  • Double masking: Wearing a tight fitting cloth mask over a surgical mask

Research shows that both of these approaches dramatically reduce exposure to aerosols emitted during a period of breathing:

“…adding a cloth mask over the source headform’s medical procedure mask or knotting and tucking the medical procedure mask reduced the cumulative exposure of the unmasked receiver by 82.2% (SD = 0.16) and 62.9% (SD = 0.08), respectively (Figure 2). When the source was unmasked and the receiver was fitted with the double mask or the knotted and tucked medical procedure mask, the receiver’s cumulative exposure was reduced by 83.0% (SD = 0.15) and 64.5% (SD = 0.03), respectively. When the source and receiver were both fitted with double masks or knotted and tucked masks, the cumulative exposure of the receiver was reduced 96.4% (SD = 0.02) and 95.9% (SD = 0.02), respectively.”

An airborne transmission simulator was used to estimate the ability of various types of face masks to block COVID-19 transmission. In this experiment, “cotton mask led to an approximately 20% to 40% reduction in virus uptake compared to no mask. The N95 mask had the highest protective efficacy (approximately 80% to 90% reduction)”. All of the masks were much more effective at source control than at protecting the wearer, with the N95 stopping all detectable transmission.

The American Conference of Governmental Industrial Hygienists (ACGIH) say that “workers need respirators”, noting that a worker with an “N95 filtering facepiece respirator… has 1-10% inward leakage and outward leakage”, but with a surgical mask “has 50% inward leakage and outward leakage”, and with a cloth face covering “has 75% inward leakage and outward leakage”. They explain that “N95 FFRs have an assigned protection factor of 10 (10% inward leakage) but must receive a fit factor of 100 (1% inward leakage) on an individual worker.” ACGIH created a table showing how, if we start with an assumption that it takes on average 15 minutes to get infected if no-one is wearing a mask (based on CDC contact tracing premises), we can calculate the time it would take on average to get infected if one or both of source and receiver are wearing various types of mask. This is calculated by simply dividing the base time of 15 minutes by the leakage factor for the source’s mask (if any), and then dividing that by the leakage factor for the receiver’s mask (if any).

This approach is, however, an over-simplification. Reseach based on a a single-hit model of infection shows that the probability of infection “shows a highly nonlinear sensitivity” to inhaled virus number. Therefore, “In a virus-rich regime… wearing a mask may not suffice to prevent infection.”

Research undertaken by the National Personal Protective Technology Laboratory (NPPTL) found that respirators with an exhalation valve “reduce particle emissions to levels similar to or better than those provided by surgical masks, procedure masks, or cloth face coverings”. Furthermore, “surgical tape secured over the valve from the inside of the FFR can provide source control similar to that of an FFR with no exhalation valve”.

Pushing back against masks

Professor Alison McMillan, Commonwealth Chief Nursing and Midwifery Officer in Australia claims that “there is no evidence to suggest that we should be moving towards… N95 respirators in the community setting.” She added “I am aware that there are some publications out there suggesting a move to N95 (masks). But that’s not supported in the empirical evidence”.

According to Norman Swan, host of the ABC’s Coronacast, “If you’re wearing an N95 that hasn’t been fit tested – and it’s not an easy process to do yourself at home – there’s no guarantee that it’s an awful lot more effective than wearing a surgical mask. Professor Catherine Bennett, chair in epidemiology at Deakin University, claims that “Technically, the instructions say you shouldn’t reuse” respirators, and that “If you’re not particularly checking its fit, you’re probably wasting your time”. Occupational environment physician Malcolm Sim agrees: “If you put it [an N95 mask] off and put it on, they’re not meant for that purpose… They’re easily damaged in somebody’s handbag,” adding that the integrity of the masks can be compromised. He says that “If you’re handling them a lot, taking them on and off, there’s much more potential for you to get it [the virus] on your hands, your face, different parts of your body.”

University of New South Wales epidemiologist Mary-Louise McLaws claimed that “There’s no evidence yet that a N95 mask will protect you more than a surgical mask for Omicron.”

An opinion piece in Newsweek claims that “the effectiveness of respirators is vastly overestimated, and there is scant evidence that they stop community transmission. Moreover, NIOSH-approved respirators are tight, uncomfortable, and can impede breathing.” The article further claims that “For respirators to work, they must be well fitting, must be tested by OSHA, and must be used for only short time windows as their effectiveness diminishes as they get wet from breathing.”

Recently there has been particular pushback against the use of masks by children, with the Newsweek article alleging that “Respirators are not necessary to protect children from COVID-19 because of the astoundingly low risk COVID-19 presents to them”, and that in fact wearing masks involves “existing well-documented harms”. There hasn’t been any documented harms to children from wearing masks,

Respirators can be reused

According to mask manufacturer 3M, respirators (which they refer to as “Filtering Facepiece Respirators (FFRs)”) “can be used many times.” They say that “There is no time limit to wearing an FFR. Respirators can be worn until they are dirty, damaged or difficult to breathe through.”

In reporting from CNN, Linsey Marr, a professor of civil and environmental engineering at Virginia Tech, explained that an N95 mask’s material and filtration ability aren’t “going to degrade unless you physically rub it or poke holes in it. “You’d have to be in really polluted air … for several days before it lost its ability to filter out particles. So, you can really wear them for a long time. People have been talking about 40 hours – I think that’s fine. Really, it’s going to get gross from your face or the straps will get too loose or maybe break before you’re going to lose filtration ability… One of the first indicators of being able to change it if it looks nice and clean is that it just feels a little harder to breathe through. There appears to be more resistance with every breath.” She also noted that the contamination risk in reusing N95 masks is “lower, much lower, than the risk of you not wearing an N95 and breathing in particles”.

The CDC has prepared guidelines for optimizing the supply of respirators which recommend reusing respirators at most five times. This guidelines were created for people “implementing policies and procedures for preventing pathogen transmission in healthcare settings”. They have been widely shared, incorrectly, by reporters as being recommendations for community use.

The inventor of N95 mask material, Peter Tsai, says that “N95 masks can be rotated, 1 mask every 3–4 days”, and that in doing this “there is no change in the mask’s properties.”

According to the NIOSH Guide to the Selection and Use of Particulate Respirators N95 respirators must maintain at least 95% filtration after a total mass loading of 200mg. This is designed to ensure they continue to work in sites with high particulate matter, such as some construction environment. However in normal use, even outside in a city with high levels of population, it would take over 200 days of 24 hour per day use to get to this level. The guide says that “generally, the use and reuse of N-series Ž lters would also be subject only to considerations of hygiene, damage, and increased breathing resistance”. The NIOSH guidelines are well supported by research.

Fit tests are not required for respirators to be effective

In one study non-experts were asked to read the instructions that come with a respirator, and then to don the respirator without assistance and complete a fit test. The average fit factor achieved was 88, and the lowest fit factor of the subjects was 15, with nearly half achieving a fit factor greater than 100.

Surgical masks have been found to have a much poorer fit in practice. One study showed that for surgical masks “quantitative fit factors ranged from 2.5 to 9.6”, and another found an average fit factor of 3.0.

Guidance from the US Food and Drug Administration (FDA) explains that:

“Fit Factor is a means of expressing the difference in particle concentration inside the mask and outside the mask during use. For example, a fit factor of 2 means that the concentration of particles within the mask is ½ or 50% of the concentration outside the mask; a fit factor of 5 means the concentration of particles within the mask is 1/5 th or 20% of the concentration outside the mask.”

The guidance says that failing to achieve a fit factor of 2 “may suggest that respirator fit will not be sufficient to assure that the device will help reduce wearer exposure to pathogenic biological airborne particulates.”

An analysis of the fitted filtration efficiency (FFE) of surgical masks found that, unmodified, they only achieved an FFE of 38.5%. The “knot and tuck” technique improved that to 60.3%, and a DIY mask fitter consisting of three rubber bands increased it to 78.2%. A 3-layer cotton mask had an FFE of just 26.5%. An N95, on the other hand, achieved an FFE of 98.4%. Furthermore, the N95 FFE had a standard deviation of only 0.5% — that is, it was effective for multiple tests during “a series of repeated movements of the torso, head, and facial muscles”. Interestingly, a 2-layer nylon mask had an FFE of 79.0% (standard devatiation 4.3%), showing that some cloth masks can be quite effective. These findings were replicated in a study of numerous types of cloth mask, which found that hybrids of 600 TPI cotton with silk, chiffon, or polypropelene achieved 72-96% filtration efficiency.

Researchers have calculated that “the particle size most likely to deposit in the respiratory tract when wearing a mask is ∼2μm”. Unfortunately, this particle size is not considered in N95 or similar standards. Instead, 0.3 μm particles are used.

A 2010 study of fit testing respirators for public health medical emergencies found that 98% of non-experts wearing masks without training achieved a fit factor of over 5 (20% leakage) and 75% of them achieved a fit factor of over 10 (10% leakage).

Donning and doffing masks is not complex or risky

Analysis by the CDC concludes that the risk of infection through surfaces (fomites) “is generally considered to be low”, a view that was supported by the evidence as early as July 2020. An analysis of “418 samples from mask fronts, cell phones, paper money, card machines, sewage, air and bedding” during a COVID surge “did not detect any trace of SARS-CoV-2 in all samples analyzed”.

We should not reserve respirators for healthcare workers

According to Anne Miller, executive director of Project N95, there are many U.S. manufacturers of N95 masks and an ample supply.

The Economist reported that in Europe “at the start of the pandemic, FFP2 masks were scarce and costly. Even governments fell victim to price gouging, paying more than €4 ($4.50) per mask. Demand had previously been low, so stockpiles and production capacity could not satisfy the sudden surge. Governments wanted to reserve supplies for those most at risk of contracting the virus, such as health-care workers.” However they reported that by the end of 2021 “FFP2 masks are in healthy supply, and as the highly transmissible Omicron variant spreads across the world, updating guidance to recommend their wider use could be one way to help reduce transmission.”

In the first 6 months of 2020, over 70,000 new face mask companies were registered in China, many run by people with no previous experience and no registration or licensing. The Chinese government stepped in to make licensing more stringent, shutting down many companies, and international demand fell over quality concerns.

Due to “a dramatic reduction in demand for N95s”, US mask factories are closing. In June 2021 the American Mask Manufacturer’s Association said that “we have 28 members who are going to go out of business in the next 60 to 90 days.” By July 2021 they estimated “that 5,000 workers have been laid off across its member companies”. However following school mask mandates and demand during the omicron surge, demand in the US spiked in early 2022.

The CDC has found that 60% of KN95s are counterfeit.

In Australia it has been reported that “general practitioners have been left without highly protective N95 masks as consumers rush to stock up after a sharp rise in COVID-19 cases.”

In May 2021 the CDC stated that “The supply and availability of NIOSH-approved respirators have increased significantly over the last several months. Healthcare facilities should not be using crisis capacity strategies at this time and should promptly resume conventional practices.”

Demand distortions can increase as we proceed up the supply chain, creating inefficiencies for upstream firms. This is known as the Bullwhip Effect.

Respirators need not be uncomfortable

In an analysis of the physiological impact of the N95 filtering facepiece respirator (FFR) “in healthy healthcare workers, FFR did not impose any important physiological burden during 1 hour of use, at realistic clinical work rates”.

A study of KF80, KF94, KF99, N95, and N99 masks found that self-reported comfort levels were nearly perfectly correlated with the ease of inhalation.

Qualitative humanities research is crucial to AI

“All research is qualitative; some is also quantitative” Harvard Social Scientist and Statistician Gary King

Suppose you wanted to find out whether a machine learning system being adopted - to recruit candidates, lend money, or predict future criminality - exhibited racial bias. You might calculate model performance across groups with different races. But how was race categorised– through a census record, a police officer’s guess, or by an annotator? Each possible answer raises another set of questions. Following the thread of any seemingly quantitative issue around AI ethics quickly leads to a host of qualitative questions. Throughout AI, qualitative decisions are made about what metrics to optimise for, which categories to use, how to define their bounds, who applies the labels. Similarly, qualitative research is necessary to understand AI systems operating in society: evaluating system performance beyond what can be captured in short term metrics, understanding what is missed by large-scale studies (which can elide details and overlook outliers), and shedding light on the circumstances in which data is produced (often by crowd-sourced or poorly paid workers).

Attempting to measure racial bias leads to qualitative questions
Attempting to measure racial bias leads to qualitative questions

Unfortunately, there is often a large divide between computer scientists and social scientists, with over-simplified assumptions and fundamental misunderstandings of one another. Even when cross-disciplinary partnerships occur, they often fall into “normal disciplinary divisions of labour: social scientists observe, data scientists make; social scientists do ethics, data scientists do science; social scientists do the incalculable, data scientists do the calculable.” The solution is not for computer scientists to absorb a shallow understanding of the social sciences, but for deeper collaborations. In a paper on exclusionary practices in AI ethics, an interdisciplinary team wrote of the “indifference, devaluation, and lack of mutual support between CS and humanistic social science (HSS), [which elevates] the myth of technologists as ‘ethical unicorns’ that can do it all, though their disciplinary tools are ultimately limited.”

There are challenges when mixing computer scientsits with social scientists. (Gallery Britto image under Creative Commons Attribution-Share Alike 4.0 International)
There are challenges when mixing computer scientsits with social scientists. (Gallery Britto image under Creative Commons Attribution-Share Alike 4.0 International)

This is further reflected in an increasing number of job ads for AI ethicists that list a computer science degree as a requirement, “prioritising technical computer science infrastructure over the social science skills that can evaluate AI’s social impact. In doing so, we are building the field of AI Ethics to replicate the very flaws this field is trying to fix.” Interviews with 26 responsible AI practitioners working in industry highlighted a number of challenges, including that qualitative work was not prioritised. Not only is it impossible to fully understand ethics issues solely through quantitative metrics, inappropriate and misleading quantitative metrics are used to evaluate the responsible AI practitioners themselves. Interviewees reported that their fairness work was evaluated on metrics related to generating revenue, in a stark misalignment of goals.

Qualitative research helps us evaluate AI systems beyond short term metrics

When companies like Google and YouTube want to test whether the recommendations they are making (in the form of search engine results or YouTube videos, for example) are “good” - they will often focus quite heavily on “engagement” or “dwell time” - the time a user spent looking at or watching the item recommended to them. But it turns out, unsurprisingly, that a focus on engagement and dwell time, narrowly understood, raises all sorts of problems. Demographics can impact dwell time (e.g. older users may spend longer on websites than younger users, just as part of the way they use the internet). A system that ‘learns’ from a user’s behavioural cues (rather than their ‘stated preferences’) might lock them into a limiting feedback loop, appealing to that user’s short term interests rather than those of their ‘Better Selves.’ Scholars have called for more qualitative research to understand user experience and build this into the development of metrics.

This is the part where people will point out, rightly, that companies like Google and YouTube rely on a complex range of metrics and signals in their machine learning systems - and that where a website ranks on Google, or how a YouTube video performs in recommendation does not boil down to simple popularity metrics, like engagement. Google employs an extensive process to determine “relevance” and “usefulness” for search results. In its 172-page manual for search result ‘Quality’ evaluation, for example, the company explains how evaluators should assess a website’s ‘Expertise/ Authoritativeness/ Trustworthiness’ or ‘E-A-T’; and what types of content, by virtue of its harmful nature (e.g., to protected groups), should be given a ‘low’ ranking. YouTube has identified specific categories of content (such as news, scientific subjects, and historical information) for which ‘authoritativeness’ should be considered especially important. It has also determined that dubious-but-not-quite-rule-breaking information (what it calls ‘borderline content’) should not be recommended, regardless of the video’s engagement levels.

Irrespective of how successful we consider the existing approaches of Google Search and YouTube to be (and partly, the issue is that evaluating their implementation from the outside is frustratingly difficult), the point here is that there are constant qualitative judgments being made, about what makes a search result or recommendation “good” and of how to define and quantify expertise, authoritativeness, trustworthiness, borderline content, and other values. This is true of all machine learning evaluation, even when it isn’t explicit. In a paper guiding companies about how to carry out internal audits of their AI systems, Inioluwa Deborah Raji and colleagues emphasise the importance of interviews with management and engineering teams to “capture and pay attention to what falls outside the measurements and metrics, and to render explicit the assumptions and values the metrics apprehend.” (p.40).

The importance of thoughtful humanities research is heightened if we are serious about grappling with the potential broader social effects of machine learning systems (both good and bad), which are often delayed, distributed and cumulative.

Small-scale qualitative studies tell an important story even (and perhaps especially) when they seem to contradict large-scale ‘objective’ studies

Hypothetically, let’s say you wanted to find out whether the use of AI technologies by doctors during a medical appointment would make doctors less attentive to patients - what do you think the best way of doing it would be? You could find some criteria and method for measuring ‘attentiveness’, say tracking the amount of eye contact between the doctor and patient, and analyse this across a representative sample of medical appointments where AI technologies were being used, compared to a control group of medical appointments where AI technologies weren’t being used. Or would you interview doctors about their experiences using the technology during appointments? Or talk to patients about how they felt the technology did, or didn’t, impact their experience?

In research circles, we describe these as ‘epistemological’ choices - your judgement of what constitutes the ‘best’ approach is inextricably linked to your judgement about how we can claim to ‘know’ something. These are all valid methods for approaching the question, but you can imagine how they might result in different, even conflicting, insights. For example, you might end up with the following results:

  • The eye contact tracking experiment suggests that overall, there is no significant difference in doctors’ attentiveness to the patient when the AI tech is introduced.
  • The interviews with doctors and patients reveal that some doctors and patients feel that the AI technology reduces doctors’ attentiveness to patients, and others feel that it makes no difference or even increases doctors’ attention to the patient.

Even if people are not negatively impacted by something ‘on average’ (e.g., in our hypothetical eye contact tracking experiment above), there will remain groups of people who will experience negative impacts, perhaps acutely so. “Many of people’s most pressing questions are about effects that vary for different people,” write Matias, Pennington and Chan in a recent paper on the idea of N-of-one trials. To tell people that their experiences aren’t real or valid because they don’t meet some threshold for statistical significance across a large population doesn’t help us account for the breadth and nature of AI’s impacts on the world.

Examples of this tension between competing claims to knowledge about AI systems’ impacts abound. Influencers who believe they are being systematically downranked (‘shadowbanned’) by Instagram’s algorithmic systems are told by Instagram that this simply isn’t true. Given the inscrutability of these proprietary algorithmic systems, it is impossible for influencers to convincingly dispute Instagram’s claims. Kelley Cotter refers to this as a form of “black box gaslighting”: platforms can “leverage perceptions of their epistemic authority on their algorithms to undermine users’ confidence in what they know about algorithms and destabilise credible criticism.” Her interviews with influencers give voice to stakeholder concerns and perspectives that are elided in Instagram’s official narrative about its systems. The mismatch between different stakeholders’ accounts of ‘reality’ is instructive. For example, a widely-cited paper by Netflix employees claims that Netflix recommendation “influences choice for about 80% of hours streamed at Netflix.” But this claim stands in stark contrast to Mattias Frey’s mixed-methods research (representative survey plus small sample for interviews) run with UK and US adults, in which less than 1 in 5 adults said they primarily relied on Netflix recommendations when deciding what films to watch. Even if this is because users underestimate their reliance on recommender systems, that’s a critically important finding - particularly when we’re trying to regulate recommendation and so many are advocating providing better user-level controls as a check on platform power. Are people really going to go to the trouble of changing their settings if they don’t think they rely on algorithmic suggestions that much anyway?

Qualitative research sheds light on the context of data annotation

Machine learning systems rely on vast amounts of data. In many cases, for that data to be useful, it needs to be labelled/ annotated. For example, a hate speech classifier (an AI-enabled tool used to identify and flag potential cases of hate speech on a website) relies on huge datasets of text labelled as ‘hate speech’ or ‘not hate speech’ to ‘learn’ how to spot hate speech. But it turns out that who is doing the annotating and in what context they’re doing it, matters. AI-powered content moderation is often held up as the solution to harmful content online. What has continued to be underplayed is the extent to which those automated systems are and most likely will remain dependent on the manual work of human content moderators sifting through some of the worst and most traumatic online material to power the machine learning datasets on which automated content moderation depends. Emily Denton and her colleagues highlight the significance of annotators’ social identity (e.g., race, gender) and their expertise when it comes to annotation tasks, and they point out the risks associated with overlooking these factors and simply ‘aggregating’ results as ‘ground truth’ rather than properly exploring disagreements between annotators and the important insights that this kind of disagreement might offer.

Behind the Screen, by Sarah T. Roberts, and Netflix Reccomends, by Mattias Frey
Behind the Screen, by Sarah T. Roberts, and Netflix Reccomends, by Mattias Frey

Human commercial content moderators (such as the people that identify and remove violent and traumatic imagery on Facebook) often labour in terrible conditions, lacking psychological support or appropriate financial compensation. The interview-based research of Sarah T. Roberts has been pioneering in highlighting these conditions. Most demand for crowdsourced digital labour comes from the Global North, yet the majority of these workers are based in the Global South and receive low wages. Semi-structured interviews reveal the extent to which workers feel unable to bargain effectively for better pay in the current regulatory environment. As Mark Graham and his colleagues point out, these findings are hugely important in a context where several governments and supranational development organisations like the World Bank are holding up digital work as a promising tool to fight poverty.

The decision of how to measure ‘race’ in machine learning systems is highly consequential, especially in the context of existing efforts to evaluate these systems for their “fairness.” Alex Hanna, Emily Denton, Andrew Smart and Jamila Smith-Loud have done crucial work highlighting the limitation of machine learning systems that rely on official records of race or their proxies (e.g. census records), noting that the racial categories provided by such records are “unstable, contingent, and rooted in racial inequality.” The authors emphasise the importance of conducting research in ways that prioritise the perspectives of the marginalised racial communities that fairness metrics are supposed to protect. Qualitative research is ideally placed to contribute to a consideration of “race” in machine learning systems that is grounded in the lived experiences and needs of the racially subjugated.

What next?

Collaborations between quantitative and qualitative researchers are valuable in understanding AI ethics from all angles.

Consider reading more broadly, outside your particular area. Perhaps using the links and researchers listed here as starting points. They’re just a sliver of the wealth that’s out there. You could also check out the Social Media Collective’s Critical Algorithm Studies reading list, the reading list provided by the LSE Digital Ethnography Collective, and Catherine Yeo’s suggestions.

Strike up conversations with researchers in other fields, and consider the possibility of collaborations. Find a researcher slightly outside your field but whose work you broadly understand and like, and follow them on Twitter. With any luck, they will share more of their work and help you identify other researchers to follow. Collaboration can be an incremental process: Consider inviting the researcher to form part of a discussion panel, reach out to say what you liked and appreciated about their work and why, and share your own work with them if you think it’s aligned with their interests.

Within your university or company, is there anything you could do to better reward or facilitate interdisciplinary work? As Humanities Computing Professor Willard McCarty notes, somewhat discouragingly, “professional reward for genuinely interdisciplinary research is rare.” To be sure, individual researchers and practitioners have to be prepared to put themselves out there, compromise and challenge themselves - but carefully tailored institutional incentives and enablers matter.

AI Harms are Societal, Not Just Individual

Not just Individual, but Societal Harms

When the USA government switched to facial identification service ID.me for unemployment benefits, the software failed to recognize Bill Baine’s face. While the app said that he could have a virtual appointment to be verified instead, he was unable to get through. The screen had a wait time of 2 hours and 47 minutes that never updated, even over the course of weeks. He tried calling various offices, his daughter drove in from out of town to spend a day helping him, and yet he was never able to get a useful human answer on what he was supposed to do, as he went for months without unemployment benefits. In Baine’s case, it was eventually resolved when a journalist hypothesized that the issue was a spotty internet connection, and that Baine would be better off traveling to another town to use a public library computer and internet. Even then, it still took hours for Baine to get his approval.

Journalist Andrew Kenney of Colorado Public Radio has covered the issues with ID.me
Journalist Andrew Kenney of Colorado Public Radio has covered the issues with ID.me

Baine was not alone. The number of people receiving unemployment benefits plummeted by 40% in the 3 weeks after ID.me was introduced. Some of these were presumed to be fraudsters, but it is unclear how many genuine people in need of benefits were wrongly harmed by this. These are individual harms, but there are broader, societal harms as well: the cumulative costs of the public having to spend ever more time on hold, trying to navigate user-hostile automated bureaucracies where they can’t get the answers they need. There is the societal cost of greater inequality and greater desperation, as more people are plunged into poverty through erroneous denial of benefits. And there is the undermining of trust in public services, which can be difficult to restore.

Potential for algorithmic harm takes many forms: loss of opportunity (employment or housing discrimination), economic cost (credit discrimination, narrowed choices), social detriment (stereotype confirmation, dignitary harms), and loss of liberty (increased surveillance, disproportionate incarceration). And each of these four categories manifests in both individual and societal harms.

It should come as no surprise that algorithmic systems can give rise to societal harm. These systems are sociotechnical: they are designed by humans and teams that bring their values to the design process, and algorithmic systems continually draw information from, and inevitably bear the marks of, fundamentally unequal, unjust societies. In the context of COVID-19, for example, policy experts warned that historical healthcare inequities risked making their way into the datasets and models being used to predict and respond to the pandemic. And while it’s intuitively appealing to think of large-scale systems as creating the greatest risk of societal harm, algorithmic systems can create societal harm because of the dynamics set off by their interconnection with other systems/ players, like advertisers, or commercially-driven media, and the ways in which they touch on sectors or spaces of public importance.

Still, in the west, our ideas of harm are often anchored to an individual being harmed by a particular action at a discrete moment in time. As law scholar Natalie Smuha has powerfully argued, legislation (both proposed and passed) in Western countries to address algorithmic risks and harms often focuses on individual rights: regarding how an individual’s data is collected or stored, to not be discriminated against, or to know when AI is being used. Even metrics used to evaluate the fairness of algorithms are often aggregating across individual impacts, but unable to capture longer-term, more complex, or second- and third-order societal impacts.

Case Study: Privacy and surveillance

Consider the over-reliance on individual harms in discussing privacy: so often focused on whether individual users have the ability to opt in or out of sharing their data, notions of individual consent, or proposals that individuals be paid for their personal data. Yet widespread surveillance fundamentally changes society: people may begin to self-censor and to be less willing (or able) to advocate for justice or social change. Professor Alvaro Bedoya, director of the Center on Privacy and Technology at the Georgetown University Law Center, traces a history of how surveillance has been used by the state to try to shut down movements for progress– targeting religious minorities, poor people, people of color, immigrants, sex workers and those considered “other”. As Maciej Ceglowski writes, “Ambient privacy is not a property of people, or of their data, but of the world around us… Because our laws frame privacy as an individual right, we don’t have a mechanism for deciding whether we want to live in a surveillance society.”

Drawing on interviews with African data experts, Birhane et al write that even when data is anonymized and aggregated, it “can reveal information on the community as a whole. While notions of privacy often focus on the individual, there is growing awareness that collective identity is also important within many African communities, and that sharing aggregate information about communities can also be regarded as a privacy violation.” Recent US-based scholarship has also highlighted the importance of thinking about group level privacy (whether that group is made up of individuals who identify as members of that group, or whether it’s a ‘group’ that is algorithmically determined - like individuals with similar shopping habits on Amazon). Because even aggregated anonymised data can reveal important group-level information (e.g., the location of military personnel training via exercise tracking apps) “managing privacy”, these authors argue “is often not intrapersonal but interpersonal.” And yet legal and tech design privacy solutions are often better geared towards assuring individual-level privacy than negotiating group privacy.

Case Study: Disinformation and erosion of trust

Another example of a collective societal harm comes from how technology platforms such as Facebook have played a significant role in elections ranging from the Philippines to Brazil, yet it can be difficult (and not necessarily possible or useful) to quantify exactly how much: something as complex as a country’s political system and participation involves many interlinking factors. But when ‘deep fakes’ make it “possible to create audio and video of real people saying and doing things they never said or did” or when motivated actors successfully game search engines to amplify disinformation, the (potential) harm that is generated is societal, not just individual. Disinformation and the undermining of trust in institutions and fellow citizens have broad impacts, including on individuals who never use social media.

Reports and Events on Regulatory Approaches to Disinformation
Reports and Events on Regulatory Approaches to Disinformation

Efforts by national governments to deal with the problem through regulation have not gone down well with everyone. ‘Disinformation’ has repeatedly been highlighted as one of the tech-enabled ‘societal harms’ that the UK’s Online Safety Bill or the EU’s Digital Services Act should address, and a range of governments are taking aim at the problem by proposing or passing a slew of (in certain cases ill-advised) ‘anti-misinformation’ laws. But there’s widespread unease around handing power to governments to set standards for what counts as ‘disinformation’. Does reifying ‘disinformation’ as a societal harm become a legitimizing tool for governments looking to silence political dissent or undermine their weaker opponents? It’s a fair and important concern - and yet simply leaving that power in the hands of mostly US-based, unaccountable tech companies is hardly a solution. What are the legitimacy implications if a US company like Twitter were to ban democratically elected Brazilian President Jair Bolsonaro for spreading disinformation, for example? How do we ensure that tech companies are investing sufficiently in governance efforts across the globe, rather than responding in an ad hoc manner to proximal (i.e. mostly US-based) concerns about disinformation? Taking a hands off approach to platform regulation doesn’t make platforms’ efforts to deal with disinformation any less politically fraught.

Individual Harms, Individual Solutions

If we consider individual solutions our only option (in terms of policy, law, or behavior), we often limit the scope of the harms we can recognize or the nature of the problems we face. To take an example not related to AI: Oxford professor Trish Greenhalgh et al analyzed the slow reluctance of leaders in the West to accept that covid is airborne (e.g. it can linger and float in the air, similar to cigarette smoke, requiring masks and ventilation to address), rather than droplet dogma (e.g. washing your hands is a key precaution). One reason they highlight is the Western framing of individual responsibility as the solution to most problems. Hand-washing is a solution that fits the idea of individual responsibility, whereas collective responsibility for the quality of shared indoor air does not. The allowable set of solutions helps shape what we identify as a problem. Additionally, the fact that recent research suggests that “the level of interpersonal trust in a society” was a strong predictor of which countries managed COVID-19 most successfully should give us pause. Individualistic framings can limit our imagination about the problems we face and which solutions are likely to be most impactful.

Parallels with Environmental Harms

Before the passage of environmental laws, many existing legal frameworks were not well-suited to address environmental harms. Perhaps a chemical plant releases waste emissions into the air once per week. Many people in surrounding areas may not be aware that they are breathing polluted air, or may not be able to directly link air pollution to a new medical condition, such as asthma, (which could be related to a variety of environmental and genetic factors).

There are parallels between air polllution and algorithmic harms
There are parallels between air polllution and algorithmic harms

There are many parallels between environmental issues and AI ethics. Environmental harms include individual harms for people who develop discrete health issues from drinking contaminated water or breathing polluted air. Yet, environmental harms are also societal: the societal costs of contaminated water and polluted air can reverberate in subtle, surprising, and far-reaching ways. As law professor Nathalie Smuha writes, environmental harms are often accumulative and build over time. Perhaps each individual release of waste chemicals from a refinery has little impact on its own, but adds up to be significant. In the EU, environmental law allows for mechanisms to show societal harm, as it would be difficult to challenge many environmental harms on the basis of individual rights. Smuha argues that there are many similarities with AI ethics: for opaque AI systems, spanning over time, it can be difficult to prove a direct causal relationship to societal harm.

Directions Forward

To a large extent our message is to tech companies and policymakers. It’s not enough to focus on the potential individual harms generated by tech and AI: the broader societal costs of tech and AI matter.

But those of us outside tech policy circles have a crucial role to play. One way in which we can guard against the risks of the ‘societal harm’ discourse being co-opted by those with political power to legitimise undue interference and further entrench their power is by claiming the language of ‘societal harm’ as the democratic and democratising tool it can be. We all lose when we pretend societal harms don’t exist, or when we acknowledge they exist but throw our hands up. And those with the least power, like Bill Baine, are likely to suffer a disproportionate loss.

In his newsletter on Tech and Society, L.M. Sacasas encourages people to ask themselves 41 questions before using a particular technology. They’re all worth reading and thinking about - but we’re listing a few especially relevant ones to get you started. Next time you sit down to log onto social media, order food online, swipe right on a dating app or consider buying a VR headset, ask yourself:

  • How does this technology empower me? At whose expense? (Q16)
  • What feelings does the use of this technology generate in me toward others? (Q17)
  • What limits does my use of this technology impose upon others? (Q28)
  • What would the world be like if everyone used this technology exactly as I use it? (Q37)
  • Does my use of this technology make it easier to live as if I had no responsibilities toward my neighbor? (Q40)
  • Can I be held responsible for the actions which this technology empowers? Would I feel better if I couldn’t? (Q41)

It’s on all of us to sensitise ourselves to the societal implications of the tech we use.

There's no such thing as not a math person

On the surface, I may seem into math: I have a math PhD, taught a graduate computational linear algebra course, co-founded AI research lab fast.ai, and even go by the twitter handle @math_rachel.

Yet many of my experiences of academic math culture have been toxic, sexist, and deeply alienating. At my lowest points, I felt like there was no place for me in math academia or math-heavy tech culture.

It is not just mathematicians or math majors who are impacted by this: Western culture is awash in negative feelings and experiences regarding math, which permate from many sources and impact students of all ages. In this post, I will explore the cultural factors, misconceptions, stereotypes, and relevant studies on obstacles that turn people off to math. If you (or your child) doesn’t like math or feels anxious about your own capabilities, you’re not alone, and this isn’t just a personal challenge. The below essay is based on part of a talk I recently gave.

me, teaching sorting algorithms, at an all-women coding academy in 2015
me, teaching sorting algorithms, at an all-women coding academy in 2015

Myth of Innate Ability, Myth of the Lone Genius

One common myth is the idea that certain people’s brains aren’t “wired” the right way to do math, tech, or AI, that your brain either “works that way” or not. None of the evidence supports this viewpoint, yet when people believe this, it can become a self-fulfilling prophecy. Dr. Omoju Miller, who earned her PhD at UC Berkeley and was a senior machine learning engineer and technical advisor to the CEO at Github, shares some of the research debunking the myth of innate ability in this essay and in her TEDx talk. In reality, there is no such thing as “not a math person.”

Dr. Cathy O’Neil, a Harvard Math PhD and author of Weapons of Math Destruction, wrote about the myth of the lone genius mathematician, “You don’t have to be a genius to become a mathematician. If you find this statement at all surprising, you’re an example of what’s wrong with the way our society identifies, encourages and rewards talent… For each certified genius, there are at least a hundred great people who helped achieve such outstanding results.”

Dr. Miller debunking the myth of innate ability, and Dr. O'Neil debunking the myth of the lone genius mathematician
Dr. Miller debunking the myth of innate ability, and Dr. O'Neil debunking the myth of the lone genius mathematician

Music without singing or instruments

Imagine a world where children are not allowed to sing songs or play instruments until they reach adulthood, after spending a decade or two transcribing sheet music by hand. This scenario is absurd and nightmarish, yet it is analogous to how math is often taught, with the most creative and interesting parts saved until almost everyone has dropped out. Dr. Paul Lockhart eloquently describes this metaphor in his essay, A Mathematician’s Lament, on “how school cheats us out of our most fascinating and imaginative art form.” Dr. Lockhart left his role as a university math professor to teach K-12 math, as he felt that so much reform was needed in how math is taught.

Dr. David Perkins uses the analogy of how children can play baseball wthout knowing all the technical details, without having a full team or playing a full 9 innings, yet still gain a sense of the “whole game.” Math is usually taught with an overemphasis on dry, technical details, without giving students a concept of the “whole game.” It can take years and years before enough technical details are accumulated to build something interesting. There is an overemphasis on techniques rather than meaning.

What if math was taught more like how music or sports are taught?
What if math was taught more like how music or sports are taught?

Math curriculums are usually arranged in a vertical manner, with each year building tightly on the previous, such that one bad year can ruin everything that comes after. Many people I talk to can pinpoint the year that math went bad for them: “I used to like math until 6th grade, when I had a bad teacher/was dealing with peer pressure/my undiagnosed ADHD was out of control. After that, I was never able to succeed in future years.” This is less true in other subjects, where one bad history teacher/one bad year doesn’t mean that you can’t succeed at history the following year.

Gender, race, and stereotypes

Female teachers’ math anxiety affects girls’ math achievement: In the USA, over 90% of primary school teachers are female, and research has found “the more anxious teachers were about math, the more likely girls (but not boys) were to endorse the commonly held stereotype that ‘boys are good at math, and girls are good at reading’ and the lower these girls’ math achievement… People’s fear and anxiety about doing math—over and above actual math ability—can be an impediment to their math achievement.”

Research across a number of universities has found that more women go into engineering when courses focus on problems with positive social impact.

Structural racism also impacts what messages teachers impart to students. An Atlantic article How Does Race Affect a Student’s Math Education? covered the research paper A Framework for Understanding Whiteness in Mathematics Education, noting that “Constantly reading and hearing about underperforming Black, Latino, and Indigenous students begins to embed itself into how math teachers view these students, attributing achievement differences to their innate ability to succeed in math… teachers start to expect worse performance from certain students, start to teach lower content, and start to use lower-level math instructional practices. By contrast, white and Asian students are given the benefit of the doubt and automatically afforded the opportunity to do more sophisticated and substantive mathematics.”

The mathematics community is “an absolute mess which actively pushes out the sort of people who might make it better”

Dr. Harron's website, and some of the coverage of her number theory thesis, including on the Scientific American blog
Dr. Harron's website, and some of the coverage of her number theory thesis, including on the Scientific American blog

Dr. Piper Harron made waves with her Princeton PhD thesis, utilizing humor, analogies, sarcasm, and genuine efforts to be accessible as she described advanced concepts in a ground-breaking way, very atypical for a mathematics PhD thesis. Dr. Harron wrote openly in the prologue of her thesis on how alienating the culture of mathematics is, “As any good grad student would do, I tried to fit in, mathematically. I absorbed the atmosphere and took attitudes to heart. I was miserable, and on the verge of failure. The problem was not individuals, but a system of self-preservation that, from the outside, feels like a long string of betrayals, some big, some small, perpetrated by your only support system.” At her blog, the Liberated Mathematician, she writes, “My view of mathematics is that it is an absolute mess which actively pushes out the sort of people who might make it better.”

These descriptions resonate with my own experiences obtaining a math PhD (as well as the experiences of many friends, at a variety of universities). The toxicity of academic math departments is self-perpetuating, pushing out the people who could make them better.

The full talk

This post is based on the first part of the talk I gave in the below video, which includes more detail and a Q&A. The talk also includes recommendations about math apps and resources, as well as a framework for how to consider screentime. Stay tuned for a future fast.ai blog post covering math apps and screentime.

7 Great Lightning Talks Related to Data Science Ethics

I have been organizing and facilitating a series of Ethics Workshops for the Australian Data Science Network, featuring lightning talks by Australian experts on a range of topics related to data science ethics, including machine learning in medicine, explainability, Indigenous-led AI, and the role of policy. Check out the videos from these thought-provoking lightning talks (with longer discussions at the end):

The False Hope of Explainability in Medicine

Differences between understandings of explainability.
Differences between understandings of explainability.

Lauren Oakden-Rayner, the Director of Research for Medical Imaging at Royal Adelaide Hospital, is both a radiologist and a machine learning expert. She spoke about mismatched expectations between technical and non-technical communities on what questions explainability answers, based on her paper “The false hope of current approaches to explainable artificial intelligence in health care”. Lauren’s talk is at the start of Video #1.

Critical Gaps in ML Evaluation Practice

Often unspoken assumptions underlying machine learning evaluation practices, and the gaps left by each
Often unspoken assumptions underlying machine learning evaluation practices, and the gaps left by each

Ben Hutchinson is a senior engineer in Google Research based in Sydney. Practices for evaluating machine learning models are largely developed within academic research and rest on a number of assumptions that lead to concerning gaps when applied to real-world applications. Ben’s talk starts at 12 min mark of Video #1.

Indigenous-Led AI

On empowering, enabling, and informing Indigenous knowledge throughout the model development process.
On empowering, enabling, and informing Indigenous knowledge throughout the model development process.

Cathy Robinson is a principal research scientist at CSIRO, working on a project to center Indigenous data soveriegnty and Indigenous co-design in addressing complex ecological and conservation issues. Read more about CSIRO’s Healthy Country AI project or about CARE Indigenous Data Principles. Watch Cathy’s talk starting at 23 min mark of Video #1.

Near-Termism and AI Value Alignment

The differences between definitive and normative understandings of explainability.
The differences between definitive and normative understandings of explainability.

Aaron Snoswell is a postdoctoral research fellow at QUT, with over a decade’s experience in software development, industry research, and robotics. He spoke about the issues with focusing primarily on long-termism in AI value alignment and the need to consider short-term issues. Starts at 36 min mark Video #1.

Narrow vs Broad Understandings of Algorithmic Bias among Stakeholders in Healthcare AI

The differences between narrow vs broad undersatndings of algorithmic bias.
The differences between narrow vs broad undersatndings of algorithmic bias.

Yves Saint James Aquino is a philosopher and physician, currently working on the project “The algorithm will see you now: ethical, legal and social implications of adopting machine learning systems for diagnosis and screening” as a postdoctoral research fellow at the University in Wollongong. For his talk, he drew on interviews with 70 different stakeholders in healthcare AI, including software developers, medical doctors, and startup founders, to explore different conceptions of how algorithmic bias is understood. Watch the first talk in Video #2.

Towards Human-Centric XAI using Eye Tracking in Chest Xrays

Using a multi-modal approach for machine learning on chest x-rays
Using a multi-modal approach for machine learning on chest x-rays

Catarina Pinto Moreira is a Lecturer in Information Systems at Queensland University of Technology and a pioneer in non-classical probabilistic graphical models for decision making to empower human decision-making. Interviews with radiologists are crucial to her work; for example, interviews revealed that clinical notes are important for radiologists to use in diagnosis, even though this is not often mentioned in the literature. Her talk begins at 10 min mark of Video #2.

The Role of Policy in Data Ethics

AI policy should span the entire AI life cycle; focus on applications rather than underlying tech; and move beyond abstract principles.
AI policy should span the entire AI life cycle; focus on applications rather than underlying tech; and move beyond abstract principles.

Michael Evans crafted Australia’s National Artificial Intelligence Roadmap, contributed to the development of Australia’s national approach to governing autonomous vehicles, and represented Australia at the World Bank/IMF Annual Meetings. He gave an overview of the AI policy landscape, including policy tools, the disconnect between principles and application, and recommended ways forward. Watch Michael’s talk beginning at 20 min mark of Video #2.

Each talk is around 5 minutes long. Feel free to fast forward to those of particular interest, or watch them all!

The End

  • The False Hope of Explainability in Medicine (Lauren Oakden-Rayner, Australian Institute for Machine Learning)
  • Critical Gaps in ML Evaluation Practice (Ben Hutchinson, Google Sydney)
  • Indigenous-Led AI (Cathy Robinson, CSIRO)
  • Near-Termism and AI Value Alignment (Aaron Snoswell, Queensland Univ of Technology)
  • Narrow vs Broad Understandings of Algorithmic Bias among Stakeholders in Healthcare AI (Yves Saint James Aquino, Univ of Wollongong)
  • Towards Human-Centric XAI using Eye Tracking in Chest Xrays (Catarina Pinto Moreira, Queensland Univ of Technology)
  • The Role of Policy in Data Ethics (Michael Evans, Evans AI)