Economics and Computation 2020: virtual, early and often (from June 15 to July 22)

The EC conference will be virtual this year, and the organizers have given that some thought.  Here's an announcement (via Jason Hartline, Nicole Immorlica and Scott Kominers) of events spread out over several weeks and  time zones. A lot of market design is included (as well as incorporated:)

Economics and Computation 2020

EC 2020 will be held virtually with events from June 15 to July 22 (details of virtual format).  Participation by members of related fields is strongly encouraged.  

Since 1999 the ACM Special Interest Group on Economics and Computation (SIGecom) has sponsored the leading scientific conference on advances in theory, empirics, and applications at the interface of economics and computation. The 21st ACM Conference on Economics and Computation (Virtual EC 2020) will feature invited speakers, a highlight of papers from other conferences and journals, a technical program of submitted paper presentations and posters, workshops, and tutorials.  

Registration is mandatory (register here) but complimentary with SIGecom membership of $10 ($5 for students).  Details on joining EC events will be emailed to registered participants.

An overview of the schedule:

June 15 - 19: Mentoring Workshop and Live Tutorial Pre-recording Sessions.

June 22 - July 3: Live EC Paper Pre-recording Plenary Sessions.

July 13: Tutorial Watch Parties, Business Meeting, and Poster Session

July 14 - 16: EC Conference (Paper Watch Parties, Paper Poster Sessions, and Plenaries).

July 17 - 22: Workshops.

Areas of interest include, but are not limited to:

Design of economic mechanisms: algorithmic mechanism design; market design; matching; auctions; revenue maximization; pricing; fair division; computational social choice; privacy and ethics.

Game theory: equilibrium computation; price of anarchy; learning in games.

Information elicitation and generation: prediction markets; recommender, reputation and trust systems; social learning; data markets.

Behavioral models: behavioral game theory and bounded rationality; decision theory; computational social science; agent-based modeling.

Online systems: online advertising; electronic commerce; economics of cloud computing; social networks; crowdsourcing; ridesharing and transportation; labor markets; cryptocurrencies; industrial organization.

Methodological developments: machine learning; econometrics; data mining.
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They have some very interesting ideas about organizing what are called "watch parties" in the program, but those are in flux, and they didn't give me permission to describe their current thoughts.

Update (embargo lifted): Jason Hartline sends these links to explain:
Philosophy: http://ec20.sigecom.org/participation/covid/

Tech details: http://ec20.sigecom.org/tech/

Earlier, Nicole Immorlica had explained this way:

"In the first hour of the watch party, we will have 3-5 parallel sessions of one hour each. In each of these, we will play three 20 min pre-recorded talks with the authors present to answer questions during the talk via chat. "In the second hour, all papers from all parallel sessions will enter a virtual poster room. Participants each control an avatar and will "walk around" the virtual room. When they get close to a poster, they can view it. When they get close to the poster presenter or another participant, that person's video enters their view and they can talk. There will also be a video steam of one minute lightening talks for the posters in one corner of the virtual room in case you want a quick recap of a talk you missed. "We're also trying to innovate around coffee breaks, having hosted conversations with a limited number of participants on a first come, first serve basis, and other activities (these are not fully formed ideas yet)." 

Behavioral Economics, Computation, and Game Theory, all in Budapest in July, or online...

Here's the (appropriately cautious) announcement:

Behavioral EC '20
2nd Workshop on Behavioral Economics and Computation

The 2nd Workshop on Behavioral EC will be held in conjunction with the 21st ACM Conference on Economics and Computation (ACM EC '20) and will be co-located with the 6th World Congress of the Game Theory Society (GAMES 2020), on July 17, 2020, in Budapest, Hungary. The goal of the workshop is to bring together researchers from diverse subareas of EC who are interested in the intersection of human economic behavior and computation, to share new results and to discuss future directions for behavioral research related to economics and computation. It will be a full-day workshop, and will feature invited speakers, contributed paper presentations and a panel discussion.

...
Submission deadline: May 18, 2020, 11:59pm PDT.
Notification: June 11, 2020
The workshop: July 17, 2020
COVID-19 Updates: We are aware of the severe restrictions across the globe due to the COVID-19 pandemic. The SIGecom board will update with the final plans for the EC 2020 conference on or by May 6. In the event the in-person conference does not happen, we will hold the workshop virtually.  

Paul Milgrom's Marshall Lectures are now available on video

Auctions are ancient, but the linked auctions Paul talks about in his lectures are stunningly modern, and depend on high powered, thoughtfully deployed, state of the art computation.

"Market Design When Resource Allocation is NP-Hard," in two lectures.
Here they are:

Lecture 1

and Lecture 2:

"The Ethical Algorithm" by Michael Kearns and Aaron Roth (book talk at Google)

Here's a talk about "The Ethical Algorithm--The Science of Socially Aware Algorithm Design"
by Michael Kearns and Aaron Roth.


IMHO it would make a fine last minute holiday gift for those interested in econ and market design as well as for fans of computer science and algorithms:) 

The Shapley value and explainable machine learning

Machine learning via deep neural nets is famously a black box approach to prediction, but efforts are being made to open the black box and explain why a given prediction was made, using the Shapley value.

Here's a story from Datanami:

December 9, 2019
Real Progress Being Made in Explaining AI, by Alex Woodie

"Google made headlines several weeks ago with the launch of Google Cloud Explainable AI.  Explainable AI is a collection of frameworks and tools that explain to the user how each data factor contributed to the output of a machine learning model.

“These summaries help enterprises understand why the model made the decisions it did,” wrote Tracy Frey, Google’s director of strategy for Cloud AI, in a November 21 blog post. “You can use this information to further improve your models or share useful insights with the model’s consumers.”

"Google’s Explainable AI exposes some of the internal technology that Google created to give its developers more insight into how its large scale search engine and question-answering systems provide the answers they do. These frameworks and tools leverage complicated mathematical equations, according to a Google white paper on its Explainable AI.

"One of the key mathematical elements used is Shapley Values, which is a concept created by Nobel Prize-winning mathematician Lloyd Shapley in the field of cooperative game theory in 1953. Shapley Values are helpful in creating “counterfactuals,” or foils, where the algorithm continually assesses what result it would have given if a value for a certain data point was different.

...

“The main question is to do these things called counterfactuals, where the neural network asks itself, for example, ‘Suppose I hadn’t been able to look at the shirt colour of the person walking into the store, would that have changed my estimate of how quickly they were walking?'” Moore told the BBC last month following the launch of Explainable AI at an event in London. “By doing many counterfactuals, it gradually builds up a picture of what it is and isn’t paying attention to when it’s making a prediction.”

Academic (computer science) conferences as marketplaces

Eppstein and Vazirani propose a centralized marketplace for computer science conferences:

A Market for TCS Papers??
November 19, 2019 by Kevin Leyton-Brown

By David Eppstein & Vijay Vazirani

"No, not to make theoreticians rich! Besides, who will buy your papers anyway? (Quite the opposite, you will be lucky if you can convince someone to take them for free, just for sake of publicity!) What we are proposing is a market in which no money changes hands – a matching market – for matching papers to conferences.

"At present we are faced with massive inefficiencies in the conference process – numerous researchers are trapped in unending cycles of submit … get reject … incorporate comments … resubmit — often to the next deadline which has been conveniently arranged a couple of days down the road so the unwitting participants are conditioned into mindlessly keep coming back for more, much like Pavlov’s dog.

"We are proposing a matching market approach to finally obliterate this madness. We believe such a market is feasible using the following ideas. No doubt our scheme will have some drawbacks; however, as should be obvious, the advantages far outweigh them.

"First, for co-located symposia within a larger umbrella conference, such as the
conferences within ALGO or FCRC, the following process should be a no-brainer:

1). Ensure a common deadline for all symposia; denote the latter by S.

2). Let R denote the set of researchers who wish to submit one paper to a symposium in this umbrella conference – assume that researchers submitting more than one paper will have multiple names, one for each submission. Each researcher will provide a strict preference order over the subset of symposia to which they wish to submit their paper. Let G denote the bipartite graph with vertex sets (R, S) and an edge (r, s) only if researcher r chose symposium s.

3). The umbrella conference will have a large common PC with experts representing all of its symposia. The process of assigning papers to PC members will of course use G in a critical way.

"Once papers are reviewed by PC members and external reviewers, each symposium will rank its submissions using its own criteria of acceptance. We believe the overhead of ranking each paper multiple times is minimal since that is just an issue of deciding how “on-topic” a paper is – an easy task once the reviews of the paper are available.

4). Finally, using all these preference lists, a researcher-proposing stable matching is computed using the Gale-Shapley algorithm. As is well-known, this mechanism will be dominant strategy incentive compatible for researchers." 

"With a little extra effort, a similar scheme can also be used for a group of conferences at diverse locations but similar times, such as some of the annual summer theory conferences, STOC, ICALP, ESA, STAC, WADS/SWAT, etc.

Harvey Prize to Christos Papadimitriou

The Technion's 2018 Harvey Prize prize, to Christos Papadimitriou, has been only recently announced: it will be awarded this month.

From Columbia University:
Professor Christos Papadimitriou Awarded the 2018 Harvey Prize
OCT 08 2019

"The Harvey Prize for Science and Technology for 2018 is awarded to professor Christos Papadimitriou for his work on the theory of algorithms and computational complexity and its application to the sciences. Papadimitriou will receive the award at a ceremony at the Technion-Israel Institute of Technology. Technion first presented the award in 1972 and two awards are given yearly. The scientists behind the genome editing technology CRISPR/Cas9 are also awardees this year.

“Professor Papadimitriou is considered the founding father of algorithmic game theory, defining key concepts, formulating key questions and proving basic results,” said Peretz Lavie, professor and president of the Technion. “He is a pioneer in the application of algorithms and complexity to other fields, including economics, biology and more.”

********
And here's the entry at the Harvey Prize link:

The Harvey Prize, established in 1971 by Leo M. Harvey of Los Angeles, is awarded annually at Technion for exceptional achievements in science, technology, and human health, and for outstanding contributions to peace in the Middle East, to society and to the economy.

PROF. CHRISTOS H. PAPADIMITRIOU

Leo M. Harvey (1887-1973) was an industrialist and inventor. He was an ardent friend and supporter of Technion and the State of Israel.

Over the years, more than a quarter of Harvey laureates have subsequently won the Nobel Prize.

The award ceremony will take place in November 2019 at Technion.

Milgrom Marshall Lectures at University of Cambridge

Paul Milgrom will be giving the 2019-2020 Marshall Lectures at Cambridge today and tomorrow.  Here's a video abstract by Paul:

2019-20 Marshall Lecture by Professor Paul Milgrom

Paul Milgrom is best known for his contributions to the microeconomic theory, his pioneering innovations in the practical design of multi-item auctions, and the extraordinary successes of his students and academic advisees. According to his BBVA Award citation: “Paul Milgrom has made seminal contributions to an unusually wide range of fields of economics including auctions, market design, contracts and incentives, industrial economics, economics of organizations, finance, and game theory.” According to a count by Google Scholar, Milgrom’s books and articles have received more than 90,000 citations. - Professor Milgrom's Personal Site >>

 Professor Paul Milgrom
(Stanford Department of Economics)
will give two lectures on,
"Market Design When Resource Allocation is NP-Hard"

Venue: Lady Mitchell Hall

Tuesday 19th November 2019
5.00pm to 6.00pm
and
Wednesday 20th November 2019
5.00pm to 6.30pm
*********
I'll update when Paul's lectures are available.
(In the meantime, here are my 2013-2014 Marshall Lectures on "Matching Markets and Market Design"  )
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Update: Both lectures are now available at the Marshall Lectures site.

Matching markets @ Simons Institute are multi-disciplinary

Two recent blog posts at the algorithmic game theory blog Turing's Invisible Hand remark on the multi-disciplinary nature of modern matching theory and market design, which involves economics, computer science, operations research and mathematics...


Matching Markets @ Simons: Driven by Theory, Driving the Economy by robertkleinberg

"A more notable aspect of matching theory in recent years has been its impact on the design of real-world marketplaces. Over the two workshops, a mix of speakers from academia and industry covered a host of markets, including payment routing, online advertising, kidney exchange, real-estate, public housing, ride-sharing, long-haul trucking, restaurant reviews, school choice, food-banks and many many others. A common theme that emerged was that online marketplaces, with the support of good algorithm and mechanism designers, are slowly taking over the economy."

and

Blind Folks and the Evolving Elephant – by Vijay Vazirani

"The “blind men’’ in this case are entire disciplines which can lay claim to the field of matching markets. Of course, the obvious one is economics – the founders of this field, namely Gale and Shapley, were mathematical economists and the 2012 Nobel Prize in Economics was awarded to Alvin Roth and Lloyd Shapley for work on these markets.

A key enabler was researchers in systems and networking. Their scientific revolutions of the Internet and mobile computing put matching markets on an exciting, new journey and their systems run these centralized markets on powerful computers.

The discipline of algorithm design has had an umbilical connection to matching markets: At the birth of this field lies the highly sophisticated Gale-Shapley stable matching algorithm (1962), whose pivotal game-theoretic property of incentive compatibility follows as a free gift from polynomial time solvability — it was established two decades after the discovery of the algorithm! Yet most researchers, including those in theoretical computer science, are not aware that algorithm design is also a legitimate claimant to this field. Indeed, the very “engine’’ that runs almost each one of these markets is a sophisticated algorithm chosen from the “gold mine’’ of matching theory! Besides stable matching, this includes maximum matching and online matching and their numerous variants."

Automatic algorithmic affirmative action, by Ashesh Rambachan and Jonathan Roth

There's been some justified concern that algorithms that make predictions and choices based on previous choices made by humans might replicate the human biases embedded in the historic data.  Below is a paper that points out that the opposite effect could happen as well.

As explained here: "Imagine a college that has historically admitted students using (biased) admissions officers, but switches to an algorithm trained on data for their past students. If the admissions officers unfairly set a higher bar for people from group A, then assuming student performance is fairly measured once students arrive on campus, students from group A will appear to be stronger than students from group B. The learned model will therefore tend to favor students from group A, in effect raising the bar for students from group B."*

Here's the paper itself, and its abstract:

Bias In, Bias Out? Evaluating the Folk Wisdom
Ashesh Rambachan, Jonathan Roth

Abstract: We evaluate the folk wisdom that algorithms trained on data produced by biased human decision-makers necessarily reflect this bias. We consider a setting where training labels are only generated if a biased decision-maker takes a particular action, and so bias arises due to selection into the training data. In our baseline model, the more biased the decision-maker is toward a group, the more the algorithm favors that group. We refer to this phenomenon as "algorithmic affirmative action." We then clarify the conditions that give rise to algorithmic affirmative action. Whether a prediction algorithm reverses or inherits bias depends critically on how the decision-maker affects the training data as well as the label used in training. We illustrate our main theoretical results in a simulation study applied to the New York City Stop, Question and Frisk dataset.
**********

* I'm reminded of the saying "To get the same reward as a man, a woman has to be twice as good.  Fortunately that's not hard..."

Algorithms and intelligence at Penn

From Penn Today:
The human driver
As the ability to harness the power of artificial intelligence grows, so does the need to consider the difficult decisions and trade-offs humans make about privacy, bias, ethics, and safety.

"Already, some AI-enabled practices have raised serious concerns, like the ability to create deepfake videos to put words in someone’s mouth, or the growing use of facial recognition technology in public places. Automated results that turned out to reflect racial or gender bias has prompted some to say the programs themselves are racist.

"But the problem is more accidental than malicious, says Penn computer scientist Aaron Roth. An algorithm is a tool, like a hammer—but while it would make no sense to talk about an “ethical” hammer, it’s possible to make an algorithm better through more thoughtful design.

“It wouldn’t be a moral failure of the hammer if I used it to hit someone. The ethical lapse would be my own,” he says. “But the harms that algorithms ultimately do are several degrees removed from the human beings, the engineers, who are designing them.”

"Roth and other experts acknowledge it’s a huge challenge to push humans to train the machines to emphasize fairness, privacy, and safety. Already, experts across disciplines, from engineering and computer science to philosophy and sociology, are working to translate vague social norms about fairness, privacy, and more into practical instructions for the computer programs. That means asking some hard questions, Roth says.

“Of course, regulation and legal approaches have an important role to play, but I think that by themselves they are woefully insufficient,” says Roth, whose book, “The Ethical Algorithm,” with Penn colleague Michael Kearns will be published in November.

The sheer size of the data sets can make transparency difficult, he adds, while at the same time revealing errors more easily."
*************

Listen also to
The programming ethos
In a podcast conversation, Penn professors Michael Kearns, Aaron Roth, and Lisa Miracchi discuss the ethics of artificial intelligence.

Celebrating Christos Papadimitriou at 70 at Columbia: September 6-8, 2019.

Christos Papadimitriou, the computer scientist who intersects with market design trhough his big contributions to algorithmic game theory, is being celebrated at Columbia in September:
70 Years Papadimitriou. 
A beautiful journey to the Theory of Computation.

Congratulations to Yannai Gonczarowski

Yannai Gonczarowski, who will be  a post-doc at Microsoft Research New-England starting this summer, has collected some awards... Here are two announcements:

Dr. Yannai A. Gonczarowski wins the 2019 SIGecom Disseration Award

The 2019 SIGecom Disseration Award is given for an outstanding dissertation in the field of economics and computation

and Yannai Gonczarowski wins Best Paper Award at MATCH-UP 2019

You can find his award talk from EC'19 here:

"Aspects of Complexity and Simplicity in Economic Mechanisms"

https://www.facebook.com/1775878152557803/videos/2808456079229122/

And his talk about the gap-year academies redesign here:

"Matching for the Israeli "Mechinot" Gap-Year Programs: Handling Rich Diversity Requirements" with Lior Kovalio, Noam Nisan, and Assaf Romm

https://www.facebook.com/1775878152557803/videos/2825212147551296/

HT: Assaf Romm, who writes

"Yannai is possibly one of the best examples of the increasingly large group of CS people who are also "real" market designers. Not only does he have several innovative works on computational and economic aspects of stable matchings (such as their communication complexity, manipulability by coalitions, and strategic simplicity) and auctions, but he is also one of the main forces behind the new redesign of the market for the gap-year academies in Israel. "

Economics and CS at the Association for Computing Machinery

The special interest group within the association for computing machinery that runs activities related to economics and computation (e.g. the EC conference) has elected some market designers to run the show:

2019 ACM SIGecom Election Results
(For the term of 1 July 2019 – 30 June 2021)
Chair
Nicole Immorlica
Microsoft Research
Email: nicimm@gmail.com

Vice-Chair
Scott Duke Kominers
Harvard University
Email: kominers@fas.harvard.edu

Secretary-Treasurer
Katrina Ligett
The Hebrew University of Jerusalem
Email: katrina@cs.huji.ac.il

Ethical algoritms: a recent talk and a forthcoming book

Increasingly, algorithms are decision makers. Here's a recent talk, and a book forthcoming in October, about what we might mean by ethical decision making by algorithms.

And here's the forthcoming book:

 The Ethical Algorithm: The Science of Socially Aware Algorithm Design Hardcover – November 1, 2019
by Michael Kearns (Author), Aaron Roth  (Author)

Cornell celebrates Eva Tardos

In the Cornell Sun:

A Tribute to the Women in Lab Coats, Behind the Microscopes and Computer Screens
By Sophie Reynolds, Catherine Cai, Caroline Chang

"Professor Eva Tardos, Computer Science

Cornell Professor Eva Tardos, computer science, focuses her research on the effects of “selfish users” in networks. A “selfish user” optimizes resource usage for their own benefit like in packet routing, crowdsourcing and bitcoin mining. Selfish optimization by one user can have a negative effect on other users because it could limit access to resources and subsequently slow down processes in their respective areas of a network.

“Understanding the tradeoff between more complicated designs that can mediate effects of selfish users versus a simpler design […] is an area that I have been working on for 20 or so years, and I still find it fascinating,” Tardos said.

Tardos’ research also overlaps heavily with algorithmic game theory. Tardos notes that her research is extremely interdisciplinary and that she actively communicates with Cornell faculty such as Professor David Easley, economics, as well as economics graduate students.

“The graduate course that I last taught, CS 6840: Algorithmic Game Theory, had econ grad students in it and those are the people I often talk to, even after the course.”

Tardos not only collaborates with economists at Cornell, but also with larger scientific communities such as in the Association for Computing Machinery (ACM) Conference on Economics and Computation, a forum to exchange ideas and converse over technical papers.

In addition to her research, Tardos teaches CS 4820: Analysis of Algorithms, a core class in Cornell’s computer science curriculum that focuses on the design and analysis of computer science algorithms.

“The best part of teaching undergraduate students is to teach students a principled way of thinking about algorithms,” Tardos said.

Tardos, a strong advocate for women in computing fields and an advisor for Women In Computing At Cornell, acknowledges that the number of women getting involved in computer science has not steadily risen like in many other STEM fields.

According to Tardos, the 1980’s were a high point for women in computing but after the dot com boom and bust, the number of women in computer science started to drop.

“Fortunately, in recent years that trend has reversed, and we are doing much better in attracting women to the field,” Tardos said.

Cornell is already ahead of the curve in achieving a 1:1 ratio of women to men in engineering disciplines, and Tardos remains very optimistic about the prospect of more women in computing fields entering academia.

Tardos hopes that this generation of women does not underestimate the excitement of being a computer scientist at a research university."

Impossibility Results in Fairness (from Adventures in Computation)

Here are excerpts from a post at Adventures in Computation that will be of interest to market designers:

Impossibility Results in Fairness as Bayesian Inference

"One of the most striking results about fairness in machine learning is the impossibility result that Alexandra Chouldechova, and separately Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan discovered a few years ago. These papers say something very crisp. I'll focus here on the binary classification setting that Alex studies because it is much simpler. There are (at least) three reasonable properties you would want your "fair" classifiers to have. They are:

1. False-Positive Rate Balance: The rate at which your classifier makes errors in the positive direction (i.e. labels negative examples positive) should be the same across groups.
2. False-Negative Rate Balance:  The rate at which your classifier makes errors in the negative direction (i.e. labels positive examples negative) should be the same across groups.
3. Predictive Parity: The statistical "meaning" of a positive classification should be the same across groups (we'll be more specific about what this means in a moment)

What Chouldechova and KMR show is that if you want all three, you are out of luck --- unless you are in one of two very unlikely situations: Either you have a perfect classifier that never errs, or the base rate is exactly the same for both populations --- i.e. both populations have exactly the same frequency of positive examples. If you don't find yourself in one of these two unusual situations, then you have to give up on properties 1, 2, or 3.
...
"So why is this result true? The proof in Alex's paper can't be made simpler --- its already a one liner, following from an algebraic identity. But the first time I read it I didn't have a great intuition for why it held. Viewing the statement through the lens of Bayesian inference made the result very intuitive (at least for me). With this viewpoint, all the impossibility result is saying is: "If you have different priors about some event (say that a released inmate will go on to commit a crime) for two different populations, and you receive evidence of the same strength for both populations, then you will have different posteriors as well". This is now bordering on obvious --- because your posterior belief about an event is a combination of your prior belief and the new evidence you have received, weighted by the strength of that evidence.  "
...
"So the mathematical fact is simple --- but its implications remain deep. It means we have to choose between equalizing a decision maker's posterior about the label of an individual, or providing an equally accurate signal about each individual, and that we cannot have both. Unfortunately, living without either one of these conditions can lead to real harm."

Market design through machine learning: David Parkes

If I were in Boston I'd go to hear David Parkes speak today about

Optimal Economic Design through Deep Learning

Abstract: Designing an auction that maximizes expected revenue is a major open problem in economics. Despite significant effort, only the single-item case is fully understood. We ask whether the tools of deep learning can be used to make progress. We show that multi-layer neural networks can learn essentially optimal auction designs for the few problems that have been solved analytically, and can be used to design auctions for poorly understood problems, including settings with multiple items and budget constraints. I will also overview applications to other problems of optimal economic design, and discuss the broader implications of this work. Joint work with Paul Duetting (London School of Economics), Zhe Feng (Harvard University), Noah Golowich (Harvard University), Harikrishna Narasimhan (Harvard -> Google), and Sai Srivatsa (Harvard University). Working paper: https://arxiv.org/abs/1706.03459

Kidney exchange in Israel using Itai Ashlagi's software

My colleague Itai Ashlagi has been inventing, building, distributing and updating state of the art kidney exchange software ever since he came to Harvard, some years ago. Since then he's been at MIT, and now Stanford, but this recent article from the Jewish Telegraphic Agency about how his software is propagating in Israel still thinks he's at Harvard:

New program finds donors for complicated kidney transplant patients

"JERUSALEM (JTA) — Kidney transplant patients who have had a hard time finding a match will have another opportunity through a new unit at an Israeli hospital.

"Kidney transplant patients who suffer from high levels of antibodies due to previous transplants or blood donations can go for many years without finding a suitable donor. A new and advanced software program can be used to cross-check through advanced information systems from hospitals in Israel and around the world.

"The program, developed by Professor Itai Ashlagi of Harvard University, was donated to the Matnat Chaim organization and will be operated out of Beilinson Hospital’s Department of Transplantation in Petach Tikvah, in central Israel."

Understanding and misunderstanding algorithmic bias

Adventures in Computation explains a recent political discussion:

Algorithmic Unfairness Without Any Bias Baked In
"Discussion of (un)fairness in machine learning hit mainstream political discourse this week, when Representative Alexandria Ocasio-Cortez discussed the possibility of algorithmic bias, and was clumsily "called out" by Ryan Saavedra on twitter.
...
"Bias in the data is certainly a problem, especially when labels are gathered by human beings. But its far from being the only problem. In this post, I want to walk through a very simple example in which the algorithm designer is being entirely reasonable, there are no human beings injecting bias into the labels, and yet the resulting outcome is "unfair".