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Applied Data Ethics, a new free course, is essential for all working in tech

Today we are releasing a free, online course on Applied Data Ethics, which contains essential knowledge for anyone working in data science or impacted by technology. The course focus is on topics that are both urgent and practical, causing real harm right now. In keeping with the fast.ai teaching philosophy, we will begin with two active, real-world areas (disinformation and bias) to provide context and motivation, before stepping back in Lesson 3 to dig into foundations of data ethics and practical tools. From there we will move on to additional subject areas: privacy & surveillance, the role of the Silicon Valley ecosystem (including metrics, venture growth, & hypergrowth), and algorithmic colonialism.

If you are ready to get started now, check out the syllabus and reading list or watch the videos here. Otherwise, read on for more details!

Issues related to data ethics make headlines daily, as real people are harmed by misuse
Issues related to data ethics make headlines daily, as real people are harmed by misuse

There are no prerequisites for the course. It is not intended to be exhaustive, but hopefully will provide useful context about how data misuse is impacting society, as well as practice in critical thinking skills and questions to ask. This class was originally taught in-person at the University of San Francisco Data Institute in January-February 2020, for a diverse mix of working professionals from a range of backgrounds (as an evening certificate courses).

About Data Ethics Syllabi

Data ethics covers an incredibly broad range of topics, many of which are urgent, making headlines daily, and causing harm to real people right now. A meta-analysis of over 100 syllabi on tech ethics, titled “What do we teach when we teach tech ethics?” found that there was huge variation in which topics are covered across tech ethics courses (law & policy, privacy & surveillance, philosophy, justice & human rights, environmental impact, civic responsibility, robots, disinformation, work & labor, design, cybersecurity, research ethics, and more– far more than any one course could cover). These courses were taught by professors from a variety of fields. The area where there was more unity was in outcomes, with abilities to critique, spot issues, and make arguments being some of the most common desired outcomes for tech ethics course.

There is a ton of great research and writing on the topics covered in the course, and it was very tough for me to cut the reading list down to a “reasonable” length. There are many more fantastic articles, papers, essays, and books on these topics that are not included here. Check out my syllabus and reading list here.

A note about the fastai video browser

There is an icon near the top left of the video browser that opens up a menu of all the lesson. An icon near the top right opens up the course notes and a transcript search feature.

Use the icons on the top left and right of the video browser to collapse/expand a menu and course notes/transcript search
Use the icons on the top left and right of the video browser to collapse/expand a menu and course notes/transcript search

Topics covered

Lesson 1: Disinformation

From deepfakes being used to harass women, widespread misinformation about coronavirus (labeled an “infodemic” by the WHO), fears about the role disinformation could play in the 2020 election, and news of extensive foreign influence operations, disinformation is in the news frequently and is an urgent issue. It is also indicative of the complexity and interdisciplinary nature of so many data ethics issues: disinformation involves tech design choices, bad actors, human psychology, misaligned financial incentives, and more.

Watch the Lesson 1 video here.

Lesson 2: Bias & Fairness

Unjust bias is an increasingly discussed issue in machine learning and has even spawned its own field as the primary focus of Fairness, Accountability, and Transparency (FAccT). We will go beyond a surface-level discussion and cover questions of how fairness is defined, different types of bias, steps towards mitigating it, and complicating factors.

Watch the Lesson 2 video here.

Lesson 3: Ethical Foundations & Practical Tools

Now that we’ve seen a number of concrete, real world examples of ethical issues that arise with data, we will step back and learn about some ethical philosophies and lenses to evaluate ethics through, as well as considering how ethical questions are chosen. We will also cover the Markkula Center’s Tech Ethics Toolkit, a set of concrete practices to be implemented in the workplace.

Watch the Lesson 3 video here.

Lesson 4: Privacy and surveillance

Huge amounts of data are being collected about us: apps on our phones track our location, dating sites sell intimate details, facial recognition in schools records students, and police use large, unregulated databases of faces. Here, we discuss real-world examples of how our data is collected, sold, and used. There are also concerning patterns of how surveillance is used to suppress dissent and to further harm those who are already marginalized.

Watch the Lesson 4 video here.

Lesson 5: How did we get here? Our Ecosystem

News stories understandably often focus on one instance of a particular ethics issue at a particular company. Here, I want us to step back and consider some of the broader trends and factors that have resulted in the types of issues we are seeing. These include our over-emphasis on metrics, the inherent design of many of the platforms, venture capital’s focus on hypergrowth, and more.

Watch the Lesson 5 video here.

Lesson 6: Algorithmic Colonialism, and Next Steps

When corporations from one country develop and deploy technology in many other countries, extracting data and profits, often with little awareness of local cultural issues, a number of ethical issues can arise. Here we will explore algorithmic colonialism. We will also consider next steps for how students can continue to engage around data ethics and take what they’ve learned back to their workplaces.

Watch the Lesson 6 video here.

For the applied data ethics course, you can find the homepage here, the syllabus and reading list and watch the videos here.

Essential Work-From-Home Advice: Cheap and Easy Ergonomic Setups

You weren’t expecting to spend 2020 working from home. You can’t afford a fancy standing desk. You don’t have a home office, or even much spare space, in your apartment. Your neck is getting a permanent crick from hunching over your laptop on the couch. While those of us who are able to work from home are privileged to have this option, we still don’t want to permanently damage our backs, necks, or arms from a bad ergonomic setup.

This is not a post for ergonomic aficionados (the setups I share could all be further optimized). This is a post for folks who don’t know where to get started, have a limited budget, and are willing to try simple, scrappy approaches. Key takeway: for 34 dollars (21 for a good mouse, and 13 for a cheap keyboard), as well as some household items, you can create an ergonomic setup like the one below. I will show many other options throughout the post, for both sitting and standing, as well as approaches you can easily assemble/disassemble (if you are using the family dinner table and need to clear it off each evening).

While visiting family, I created an ergonomic setup on a counter
While visiting family, I created an ergonomic setup on a counter

You can permanently damage your body with bad ergonomics

You can permanently damage your back, neck, and wrists from working without an ergonomic setup. Almost two decades ago, my partner Jeremy suffered from repetitive stress injury due to working without an ergonomic setup. At the time, his arms were paralyzed and he had to take months off from work. Even now and after years filled with good ergonomics and yoga, this still impacts his life, severely limiting how much time he can spend in cars or on planes, and creating painful flare-ups. Please take this issue seriously.

Key advice: Have a separate keyboard and mouse

The most important thing to know is that you want your screen approximately at eye height, and your elbows at approximately right angles to your torso as they type and use the mouse. This is the case whether you are sitting or standing. If you are using a laptop, this will be impossible with the built-in keyboard and trackpad (no matter how nice they are). It is essential to have a separate keyboard and mouse. If you only do one thing to address ergonomics, obtain a separate keyboard and mouse.

If you can’t afford an external monitor, no worries, you can just elevate your laptop. Over the years, I have used cardboard boxes, drinking glasses, bottles of soda, board games, and stacks of books to elevate my laptop. I will recommend some keyboards and mice that I like below, but anything is better than using the ones built into your laptop (since that forces you to keep your screen at the wrong height). For example, the picture in the intro is of a set-up I created while visiting a family member’s apartment in 2014, using books and a cardboard box to elevate my keyboard, mouse, and laptop to the appropriate heights.

For the deep learning study group, I routinely used a brown cardboard box. Bonus: I could store everything in the box when we had the clear out of that room each night.
For the deep learning study group, I routinely used a brown cardboard box. Bonus: I could store everything in the box when we had the clear out of that room each night.

Above is a picture from the deep learning study group, which meets 5 days a week, for 7 weeks, every time we run the deep learning course. I use a brown cardboard box to elevate my keyboard. We have to clear out of that conference room each evening, and it is simple for me to put my items in the box. This sort of solution could work if you don’t have a dedicated office space in your home, and need to be able to set up/take down your workstation regularly.

I rarely worked in coffee shops pre-pandemic (and never do now), but when I had to I would still try to create an ergonomic setup (and go to a coffeeshop where there was enough space!). Here, I’ve stacked my laptop on top of my rolled-up backpack. Ideally, my screen would be higher, but this is still better than having it at table level. Don’t let the perfect be the enemy of the good. Every step you take towards a more ergonomic setup is helpful.

When working at a coffee shop (pre-pandemic), I brought an external keyboard and mouse, and used my rolled-up backpack to raise the height of my laptop screen
When working at a coffee shop (pre-pandemic), I brought an external keyboard and mouse, and used my rolled-up backpack to raise the height of my laptop screen

About standing desks

If you have a regular desk (or even just a table) at home and want a standing desk, one option is to convert it using the $22 standing desk approach, which involves an Ikea side table and shelf. I had a previous job in which this was quite popular. Here is a photo of my work desk from that time.

In a previous job, many of us set up $22 standing desks using Ikea side tables
In a previous job, many of us set up $22 standing desks using Ikea side tables

Standing on a hard floor can be difficult for your back. I have a GelPro mat, which I love. If you can’t afford a GelPro mat, standing on a folded-up yoga mat works great too.

Note that standing desks are not a cure-all. I’ve often seen people with expensive standing-desk converters (also known as desktop risers) that still have their monitor way too low. Even if you have an external monitor and desktop riser, makes sure your monitor is at an appropriate height. It is likely you will still need to stack it on top of something. If you don’t like the aesthetics of using books or other household items, you can buy a monitor stand, such as this one.

Using a standing desk with poor posture is not very ergonomic, so be cognizant of when you start feeling fatigued. I prefer to switch between standing and sitting throughout the day, as my energy fluctuates.

Budget Recommendations

My “budget recommendation” would be to get an Anker vertical mouse for $21 and literally any keyboard. If you have to choose, I’ve found that having a good mouse is way more important than a good keyboard. It is important that you get some keyboard though, so that you can elevate your laptop screen. In the setup below, I’m using a lightweight travel keyboard that isn’t particularly ergonomic, but it works fine.

The barista at this coffee shop kindly let me use 2 plastic tubs to prop up my laptop.
The barista at this coffee shop kindly let me use 2 plastic tubs to prop up my laptop.

I realize that at a time when many Americans do not have enough to eat, that you may not have 34 dollars to spare (21 dollars for a mouse and 13 dollars for a cheap keyboard). However, if this is an option for you, it is well worth the cost. If you permanently damage your back, neck, or arms, no amount of money may be enough to heal them later.

Other products I like

My favorite mouse is the Logitech wireless trackball mouse. I have also used and liked the Anker vertical mouse. For keyboards, I like Goldtouch (I use an older version of this one) or the Microsoft Ergonomic Keyboard. And if you are looking for a compact, lightweight travel keyboard, I like the iClever foldup keyboard.

As mentioned above, GelPro mats are great if you are going to be standing, and a folded-up yoga mat is a cheaper alternative.

I have a Roost portable, lightweight laptop stand, which is great, although I can’t use it since I switched from a Macbook Air to a Microsoft Surface Pro. None of the links in this post are affiliate links; I’m just recommending what I’ve personally used and like.

For more about home office set-ups, Jeremy recently posted a twitter thread about his preferred computer set-up (which includes some pricier options). It’s also worth noting that his desk has a small footprint, and fits in the corner of our living room.

Cloth masks can protect the wearer

As we all know now, the science shows that DIY masks are particularly good at protecting those around you, in case you’re infected with COVID-19. But that doesn’t mean that you can’t do a lot to protect yourself too.

Unfortunately, many public health bodies still incorrectly claim that there is no evidence that DIY masks are useful at protecting the wearer. There’s actually plenty of evidence they can. Effective protection for the wearer of a mask depends on three critical things:

  • Material: does the mask filter particles of the appropriate sizes?
  • Fit: do particles squeeze in through the gaps of your mask?
  • Sanitation: can you clean and re-use the mask?

Let’s look at each in turn.

Material

The droplets that you need to filter out to protect yourself when wearing a mask are smaller than those that you have to filter out to protect those around you. That’s because they evaporate rapidly to become around 5x smaller after they’re ejected from your mouth. It’s unlikely that particles smaller than 1 micron can contain the virus, and particles can be up to 100 microns, so that’s the size range that ideally we want to filter. However, I haven’t seen any studies yet that look at that size range. Nearly all studies mainly look at much smaller particles, since that’s what the official NIOSH standard requires. The good news is that anything that does well on those tests will almost certainly do even better for larger particles, so here we’ll focus on NIOSH standard tests.

After looking at dozens of academic papers and websites, by far the best information I’ve found is in a table from maskfaq.com based on testing from TSI. I’ve extracted the best performing materials into the table below, sorted by quality, and color-coded by efficiency.

Table of filtration for highest quality DIY materials

A higher efficiency is better—it shows the percentage of particles that were filtered (remember, this is with much smaller particles at a much higher flow rate than we see in practice). A lower resistance is better—it is a measure of how hard it is to breath through. The “Q” column shows the filter quality factor, which combines efficiency and resistance. For materials with high Q but low efficiency, you can use more layers to increase the efficiency (although doubling the number of layers won’t necessarily mean doubling the efficiency).

Based on this table, the clear winner appears to be Filtrete 1900. It’s over 85% effective, and has an astonishingly low resistance. There are instructions available for creating masks with this material. One piece of filter material makes hundreds of mask filters, so you can get together with your community to make lots from a single order. However, be aware of three key issues:

  • It is not approved for use in masks by 3M. My guess is they just haven’t tested it and want to avoid liability; there isn’t any fiberglass or similar substances in it that might be problematic.
  • It can’t be washed, and I don’t know if there are other ways to sanitize it. However, it lasts three months as an air conditioner filter.
  • It might only filter small droplets, since it relies on electrostatic attraction for filtration. So it’s probably best to combine it with cotton, such as in a cloth mask with a filter pocket
Choose from hundreds of thousands of mask designs with a filter pocket

Personally, I prefer to use Filti, which is a nanofiber material specifically designed for face masks. It has the highest efficiency of any DIY material I’ve seen tested. You can buy masks with Filti pockets at Amazon or Etsy. Filti can be sanitized with heat and re-used. You can buy pre-cut material for 20 masks for around $20, which makes it better value if you’re not making many masks.

An even more economical option are shop towels. They’re not anywhere near as effective as Filti or Filtrete for wearer protection, but at around $20 for 200 towels, which you can fold to create two layers, you can stape rubber bands to them and give them away to anyone that needs them.

Fit

For wearer protection, fit is particularly important because, as you inhale, you will be sucking air (and floating particles in the air) straight through any gaps. The main places you are likely to have gaps are:

  • Around your nose
  • The sides of your mouth
  • At the bottom of your mask.

The bottom of your mouth is easy to handle: just make sure your mask is large enough to cover well past your chin, and is nice and wide at the bottom, and you should find that that creates a good seal where your chin is.

To fit around your nose properly, use a moldable nose piece. This is the thing that sits over the bridge of your nose and you mold to follow you face. The cheapest way to make one is to cut out a piece of aluminum foil, and fold it five times, to create a strip. You can see how in the video below.

Alternatively, you can use pipe cleaner, soft wire tie, or just buy adhesive nose strips.

To close the gaps at the sides of your mouth (and also helpful for your nose) you can use a mask brace. There are two great approaches explained at fixthemask.com. The first approach just uses three rubber bands, and is shown in the video above. This works well, and has been tested and shown to be capable of passing the NIOSH N95 fit test. However, it can be a bit awkward and uncomfortable, so I prefer the rubber sheet brace shown here:

A rubber mask brace

The only tricky bit is finding the material. I managed to find the correct type of rubber for around $20 from Amazon. One piece will make ten braces. I found that I could easily print the design on my printer and then use it as a stencil for cutting the rubber sheet with scissors. I’m not very crafty, so if I can do it, anyone can…

Another alternative to improving fit is adding a nylon stocking layer. I haven’t tried this myself, but researchers at Northeastern University have tested it and found it works well.

One tip that helps: get a larger mask with straps that tie all the way around the back, rather than just going over your ears. These can often have a much better fit. A thoroughly documented design with extensive tests is available at diymask.site.

If you have a 3D printer, there are some very thoughtful rigid designs in section IV of this paper, as well as some great fabric designs. If you have a heat sealer, there’s an excellent series of videos showing how to quickly create a mask that passes the N95 fit test. Many of these designs are available to purchase from hobbyists, crafters, and non-profits, often for no more than the cost of the materials. For instance, here is a rigid mask for just US$2.

Sanitation

For basic cloth masks, you can simply throw them in the wash. Anything involving soap will destroy the virus’s protective lipid layer. I believe that shop towels should be fine to wash too.

Most specialized filter material, including Filti, can’t be washed. Instead, put the filter material in a ziploc bag, and put it in a 160F oven for 30 minutes. (I asked one of the Stanford researchers that wrote these guidelines for tips on how to do it at home, and they suggested the ziploc bag trick.) I don’t know if Filtrete can handle these temperatures however, so you are probably best off simply disposing of Filtrete inserts when they’re dirty.

Generally you’ll probably be using specialized filter material as inserts in a cloth mask’s pocket. In these cases, take the insert out before you wash the cloth mask. If you forget, throw the insert away and get a new one — seriously, I mean it; Filti, for instance, loses about half its filtration after washing!

Recommendation

I suggest buying a large cloth mask with a Filti insert, moldable nose piece, and straps that go around the head, from Amazon or Etsy. When you need to sanitize it, put the cloth mask in the wash and sanitize the insert in the oven as described above.

If you use that kind of mask, or follow the other approaches described on this page, you should be able to achieve good protection when you go out. As well as wearing a mask, wear goggles or glasses (including sun glasses) too, since the virus can also enter through the eyes.

Particle sizes for mask filtration

Summary: SARS-CoV-2 particles do not float freely in the air. They are expelled as relatively large droplets, which research shows are easily caught by a simple cloth or paper mask. If an infected person doesn’t wear a mask, their droplets quickly evaporate into smaller droplet nuclei, which are harder to filter with a cloth mask. However there are some cloth mask designs which can do a very good job of this too.

I’ve seen a lot of confusion about the efficacy of mask filtration, and the impact of masks on re-breathing CO2. In each case, part of the problem is based on a failure to understand the relevant particles, and particle sizes. So let’s see if we can resolve some of the confusion!

Here are some basic parameters (all approximate measurements):

  • The SARS-CoV-2 virus particle is 100nm (nanometers) in diameter.
  • A CO2 molecule is 0.33nm diameter.
  • When we speak we produce droplets between 20 and 2000µm (micrometers) in diameter. Note that a micrometer is a thousand times larger than a nanometer!
  • Larger droplets fall to the ground fairly quickly. Smaller droplets evaporate in (at most) a few seconds to a droplet nuclei of around 1µm.
  • A 27µm droplet would carry 1 virion on average, and would evaporate to 5µm in a few seconds.
  • Small particles do not fly straight through materials, but instead follow brownian motion, resulting in them coming in contact with a material even when the material weave is larger than the particle.
  • Many materials, such as paper towel, have a complex weave which make it very difficult for particles to fully penetrate.
  • Materials like chiffon and silk also have electrostatic effects that result in charge transfer with nanoscale aerosol particles, making them particularly effective (considering their sheerness) at excluding particles in the nanoscale regime (<∼100 nm).

So, the first thing to note is that CO2 is going to flow through any mask without any trouble. There is no known mask material that will filter 0.33nm molecules. If it did, you wouldn’t be able to breathe at all!

The size of the virus particle itself is not relevant to any discussion of mask filtration. This is because virus particles never float freely in the air, but are always at least suspended in a droplet nuclei ten times larger than the virus itself. A droplet containing a single particle will on average start out 270 times larger than the virion, and will evaporate to nuclei of 50 times larger than the virion.

The size of the weave of the fabric is also not directly comparable to the size of the droplets or droplet nuclei, due to the three dimensional nature of many types of material, the indirect route taken by small particles in brownian motion, and the electrostatic effects in many materials. So if you’ve seen those claims that masks can’t possibly stop COVID-19 because the virus is too small, now you know why they’re totally wrong.

A popular meme created by someone that doesn't understand aerosol science
A popular meme created by someone that doesn't understand aerosol science

Therefore, the only way to really understand the efficacy of a mask is to actually test it in practice. Because the size of droplets that are ejected are much larger than those that remain in the air (due to evaporation), testing must be done separately for source control (protecting others from the wearer) vs PPE (protecting the wearer from others).

Source control efficacy

There are two main ways to physically test a mask:

  1. Have someone wearing it breathe, talk, cough, and so forth, or
  2. Synthetically simulate these actions using a spray mechanism, such as a nebulizer.

Because actual human actions are complex and hard to simulate correctly, the first approach is preferred where possible. Generally, we are most interested in speech droplets, because people that are coughing and sneezing should stay home, so those are of less importance to community transmission, and breathing is not believed to contain significant concentrations of SARS-CoV-2 particles.

There is a study that looked at the protective effect of a simple cloth mask for speech droplet source control, that found that approximately 99% of the forward-facing droplets visible in a laser chamber were blocked. The cloth mask in the study was moist, in order to avoid dust contamination of the equipment; a followup experiment from the same group, pictured here (but not published yet), found that a dry paper towel had the same results.

There are no studies that have directly measured the filtration of smaller or lateral particles in this setting, although using Schlieren imaging it has been shown that all kinds of masks greatly limit the spread of the droplet cloud, consistent with a fluid dynamic simulation that estimated this filtration level at 90%.

Fluid dynamics simulation of droplet cloud with vs without mask
Fluid dynamics simulation of droplet cloud with vs without mask

Another approach to studying source control efficacy tested viral shedding in respiratory droplet samples and aerosol samples. In this study, seasonal coronavirus was tested, which is in the same genus as SARS-CoV-2. Cloth masks were not tested; neither were speech droplets – only breathing and coughing were studied. An unfitted surgical mask was 100% effective at blocking coronavirus particles.

In a pair of studies from 50 years ago, a portable isolation box, provided with a filtered air supply and a means of access for a test subject’s head, was attached to an Andersen Sampler and used to measure orally expelled bacterial contaminants before and after masking. In one of the studies, during talking, unmasked subjects expelled more than 5,000 bacterial contaminants per 5 cubic feet; 7.2% of the contaminants were associated with particles less than 4μm in diameter. Masked subjects (using a cotton muslin and flannel blend) expelled an average of 19 contaminants per 5 cubic feet; 63% were less than 4μm in diameter. So overall, over 99% of contaminants were filtered. The second study used the same experimental setup, but studied a wider range of mask designs, including a 4-ply cotton mask. For each mask design, over 97% contaminant filtration was observed.

PPE efficacy

Protection of the wearer (PPE) is much more challenging that source control, since, as discussed, the particles are much smaller (although not as small as a free virion). It’s also much harder to directly test mask efficacy for PPE using a human subject, so instead simulations must be used. There are two considerations when looking at efficacy:

  1. The filtration of the material
  2. The fit of the design.

There are many standards around the world for both of these issues, such as the U.S. National Institute for Occupational Safety and Health (NIOSH) N95 classification. The ‘N95’ designation means that when subjected to careful testing, the respirator blocks at least 95 percent of very small (0.3 micron) test particles. These are much smaller than virus-carrying droplets or droplet nuclei, which means that masks that do not meet this standard may be effective as PPE in the community.

One recent study looked at the aerosol filtration efficiency of common fabrics used in respiratory cloth masks, finding that efficacy varied widely, from 12% to 99.9%. Underlining the importance of fit for specialized medical masks, an unfitted N95 respirator had the worst efficacy. Many materials had >=96% filtration efficacy for particles >0.3 microns, including 600 TPI cotton, cotton quilt, and cotton layered with chiffon, silk, or flannel. These findings support studies reported in 1924 by Wu Lien Teh, which described that a silk face covering with flannel added over the mouth and nose was highly effective against pneumonic plague.

Many studies use very small aerosol particles at very high flow rates, such as a study that was used as the basis for a table in WHO’s Advice on the use of masks in the context of COVID-19. In this study, tiny 78nm aerosol particles were blasted through cloth at a rate of 95 liters per minute. Only N95 and equivalent masks were able to stand up to this torrent of aerosol, which would never be seen in practice in any normal community setting. The machines used for these studies are specifically designed for looking at masks that hold their shape (respirators) which are glued or attached with beeswax firmly to the testing plate. Flexible masks such as cloth and surgical masks can get pulled into the hole in the testing plate.

There are many designs of cloth masks, with widely varying levels of fit. There have been few tests of different designs. One study looked at unfitted surgical masks, and used three rubber bands and a paper clip to improve their fit. All eleven subjects in the test passed the N95 fit test using this approach. A simple mask cut from a t shirt achieved a fit score of 67, not up to the 100 level required for N95, but this mask offered substantial protection from the challenge aerosol and showed good fit with minimal leakage. Wu Lien Teh noted that a rubber support could provide good fit, although he recommended that a silk covering for the whole head (and flannel sewed over nose and mouth areas), with holes for the eyes, tucked into the shirt, is a more comfortable approach that can provide good protection for a whole day.

Overall, it appears that cloth face covers can provide good fit and filtration for PPE, but results will vary a lot depending on material and design.

Conclusion

Overall, there is evidence that simple cloth face masks will generally provide good protection to those around the wearer (source control). This is possible because droplets expelled during speech are much larger than the droplet nuclei they later turn into through evaporation.

There are also some combinations of material and design which can provide good protection to the wearer as well (PPE). However, many cloth masks do not have the materials or design necessary to achieve the highest level of protection.

Introducing the first cohort of USF CADE Data Ethics Research Fellows

The University of San Francisco is welcoming three Data Ethics research fellows (one started in January, and the other two are beginning this month) for year-long, full-time fellowships. We are so excited to have them join our community. They bring expertise in an interdisciplinary range of fields, inlcuding bioethics, public policy, anthropology, computer science, data privacy, and political philosophy. We had many fantastic applicants for the program, and we wish we had been able to offer a larger number of fellowships. We hope to be able to expand this program in the future. Without further ado, here is our first cohort of data ethics research fellows: Ali Alkhatib, Razvan Amironesei, and Nana Young.

from left to right: Ali Alkhatib, Razvan Amironesei, Nana Young
from left to right: Ali Alkhatib, Razvan Amironesei, Nana Young

Ali Alkhatib

Ali Alkhatib is a social computing researcher trained in Computer Science and Anthropology. His research explores how people relate to artificial intelligence and technology broadly, and attempts to situate those relationships in historical backdrops and ontological foundations using scholarship from the social sciences.

His paper Street-Level Algorithms: A Theory at the Gaps Between Policy and Decisions won the best paper award in 2019 at CHI Conference on Human Factors in Computing Systems, the premier international conference of Human-Computer Interaction, and his paper Examining Crowd Work and Gig Work Through The Historical Lens of Piecework won honorable mention at CHI 2017. Ali wrote the powerful essay Anthropological/Artificial Intelligence & the HAI.

Ali studied Computer Science at Stanford for a few years while pursuing a PhD with Michael Bernstein as his advisor. He earned my B.A. in Anthropology & B.S. in Informatics, specializing in human-computer interaction, both from UC Irvine in 2014, with an honors thesis on the Culture of Quantified Self, working under Tom Boellstorff.

Razvan Amironesei

Razvan Amironesei, PhD, was most recently a visiting scholar in the Department of Philosophy at the University of California, San Diego, where he chairs a multicampus faculty research group on algorithms and politics. He conducts interdisciplinary research (1) on the genealogy of datasets in collaboration with Google researchers by showing the constitution of algorithmic bias and its relation to harm as a historical, ethical, and technical problem and (2) on specific issues related to privacy practices of data related to human rights and questions regarding cybersecurity in the tech sector.

Over his past 8 years with UCSD, Razvan has written and received three grants that he used to organize events on the political and ethical dimensions of algorithms at UC San Diego, UC Berkeley, and UCLA. His Ph.D. dissertation in philosophy was devoted to the relationship between biopower and the concept of life, where he engaged with a sociological and theoretical analysis of Human-computer interaction technologies, in particular the question of brain surveillance. In his MA, he worked on questions around surveillance and privacy via a historical analysis of disciplinary technologies. Razvan has previously taught many undergraduate and graduate level courses in political philosophy and ethics including: “Ethics and Healthcare,” “The Ethics of Human Cloning,” and “Politics, Power, Violence.”

Nana Young

Nana Young is a global health bioethicist with domestic and international experience conducting independent health disparities research in low- and middle-income settings. Her research interests include ethical implications of disruptive technology, cyberharms and vulnerable populations, algorithmic justice, and harnessing the power of artificial intelligence to drive ethical, sustainable development in low and middle-income countries.

Nana Young earned her MA in Bioethics & Science Policy at Duke University, with a thesis on “When Private Bodies Deliver Public Goods: Why the Expectation of Private Altruism to Substitute for State Public Good Delivery is a Desertion of Government Responsibility that Places the Poor at Heightened Risk.” While at Duke, she helped design a course on race, genomics, emerging technologies, and society. She earned a BA in sociology at Princeton, with a thesis on a “Qualitative Study of the Socio-Cultural Sources of Mental Illness Stigma in Ghana, West Africa.”

Previously, Nana worked on strategic initiatives at a non-profit to shape strategic engagement with industry, civil society, academic and government actors towards the promotion and implementation of policy and health systems solutions of pressing global health issues including NCDs, disease epidemics, climate change, tobacco control, mental health, maternal mortality, and more.

Please join me in welcoming these data ethics research fellows to the University of San Francisco Center for Applied Data Ethics!