Madison Karas

Givers Gain is the only event I will go to that has the word "networking" in the description.

The aim of the session is to make helpful introductions for people attending the session, by combining the networks of each individual that attends. The basic principle being "I may not be able to help you, but I probably know someone who can".

Anyone can run one of these sessions. It works best with groups between 10-20 people in size. Too few and the network isn't big enough, too big and the session becomes unwieldy. Closer to 20 is probably the best size if you can get it.

The session works in 2 rounds. In the first round, people take turns describing themselves and the kind of person they'd like to be introduced to. Then there's a break, and people think if they have any introductions to make. Then in the second round, people take turns sharing the introductions that they can make.

How To Describe Yourself

You have 90 seconds in total, so you need to be succinct. The template to follow is:

• Your name (so people can make the introduction. Contact details are shared at the end)
• Your organisation or practice (the domain of life your introduction is for)
• Why you're good at what you do (why people should want to be introduced to you)

How to Describe Your Desired Introductions

You need to describe the kind of person you want to meet, the more specific the better. It might feel like being specific is ruling people out, but it actually isn't. People find it easier to create a partial match with more information than a perfect match with less information. Being specific helps people make the partial matches.

The template that I'm familiar with uses 3 levels of introductions, called :

• Bread and butter. This person is your day-to-day business, you meet and work with this kind of person all the time, but it's always good to know more of them.
• The Cream. This is the kind of person that if you met them, it would give you a great quarter, or a great year. It's your normal business, but it's a big substantial chunk.
• The Dream. This is the kind of person that could take your organisation or practice to a whole new level.

Ideally you'd have your bread and butter, cream and dream ready before your turn in the round. It takes some practice to be able to think of them and present them within the time limit.

Giving Referrals

While people are introducing themselves and describing their ideal introductions, take notes. You'll have time to think of people during the break.

The referrals should be to people, not business ideas. "Have you tried calling local shops?" is not a referral, for example.

If you give someone a referral, it's your responsibility to act on them. It can be as simple as sending an email that says "I met someone at a networking event who wanted to meet people like you, can I introduce them to you?". All you need to do is make the connection.

After the Session

After the session, keep your notes in your back-pocket, because you may come across or think of connections later and ideally this can be a regular group you attend and you make it better by creating more connections.

I would be happy to run a Givers Gain session for the cohort and faculty to demonstrate how it works.

So it turns out, in government, civic society, and life more generally, finding out of if something actually works (eg, achieves the change you wanted it to) is actually rather hard. People have done excellent talks about this. The UK Government has got a book about this. And yet it's still all pretty hard, and people tend to be very confidently wrong.

I've spent a lot of my time trying to run evaluations in government, but it's not exactly a fun thing to teach... so instead, I'm proposing a bit of a lab experiment: let's run an experiment on ration club (or something else lab related you control!)

If people are interested, we'll spend a few hours talking through what sort of changes we might want to make to RC, then hopefully actually test them, robustly, record what changes, and run a full experiment from cradle to grave. We'll start from first principles, so a vague understanding of causal inference and statistics is very welcome, but absolutely not necessary.

PS: it will defo go wrong.

Relevant reading:

• all the evaluations linked above
• Some of the other topics of data + evidence
• Freakonomics!
• If you're feeling hardcore, Causal Inference for the Brave and True, my bible.

Non-violent communication is a book written by Marshall Rosenberg. I've never read it, but from my skimming, it is similar to the communication practice taught at Community Mediation Maryland. However, there are some key philsophical differences, and I think it's aimed at different goals. I don't know for sure because I haven't read it yet! Generally it is quite popular among mediators.

Possible method of engagement: a one-time book club meeting to discuss this book, with the expectation that everyone in attendance has read it before attending. If desired, I could bring exercises to practice the skills. Noting that I have not read it before, but I do come at it with an expert eye and some familiarity with the broad topic, this would be like a self-guided skills workshop.

Exercises would be like: simulated role play, where you're given some character background and have to act out a conflict. A chance to practice the language. I don't really know for sure yet what it would look like, having not yet read the book.

Asymmetric information arises when one party knows more than another. Understanding the implications of this will help you understand everything from why private health insurance is difficult to why socialising with new people is hard.

Asymmetric information occurs in a range of contexts:

• the buyer doesn't know if your car has a hidden fault
• the health insurer doesn't know if you have a hidden condition
• your car insurer doesn't know if you bother locking your car
• your boss doesn't know if you're working or scrolling Tiktok
• you don't know whether or not your new acquaintance is a weirdo

The first three are hidden facts. The second two are hidden choices. Economists study these situations because they can lead to markets unraveling and collapsing. There are also a range of ways to mitigate them including: warranties, references, qualifications, reviews, medical questionnaires, insurance excesses.

For a seminal paper on this issue, see: The Market for "Lemons": Quality Uncertainty and the Market Mechanism (1970)

I define a decision as the process of selecting one out of two or more alternatives.

In many cases, it can be valuable to put some work into generating as many ideas as possible. This is especially important for making decisions amidst disagreement — it takes some real creative work to find an idea that everyone will agree to.

In addition to finding creative ideas to reach agreement, you also simply want ideas that are good! If you are designing a technology, or planning a campaign, you don't just want to settle on the first viable idea that comes along. You want to push yourself and your group to come up with the best possible idea.

Colloquially, the process of coming up with ideas is known as "brainstorming." For design purposes, you must address two key tensions in brainstorming.

On the one hand, you want to maintain as much diversity as possible in your process — the more diverse your ideas are, the more likely you are to find a good one. If all of your ideas are very similar, you're not really exploring the possibility space. Thus, a good design practice might be to keep all of your group members isolated, so they don't start to herd together around the same set of ideas.

On the other hand, people inspire each other — we "riff" on each others ideas — once I hear an idea from you, it sparks an idea from me that I would not have thought about on my own.   So we don't actually want to keep people separate, do we? We want to keep them together, so they can bounce ideas off each other.

This is the tension you must consider in your design process — balancing diversity and creative synergy.

Another technique for generating ideas is crowdsourcing. The idea of crowdsourcing is that we can recruit members outside of a group to contribute ideas, often through a competition where the best idea gets a prize.

The advantage of crowdsourcing is that it puts a lot more people on the problem—and more people means more chance of finding a good idea. A very popular website for crowdsourcing is Kaggle.

One interesting idea I recently read (no link available) about crowdsourcing is that the type of feedback matters — sometimes, you might want to give feedback on the quality of the solutions, and sometimes you might want to give feedback on the way that people are designing the problem.   Each approach has different advantages.

These are a few examples of design principles you might consider, and we can discuss others.

With respect to developing your facilitation practice, I teach a very specific set of techniques for managing idea generation.

Causality is a branch of statistics which economists specialise in. It lets us study all kinds of interesting questions, like the impact of minimum wage on jobs, an extra year of education on income, whether a drug works, smoking bans on smoking rates, pornography on sexual assault.

Let's talk through causal analysis intuitively as well as nerding out on causality theory, e.g. the work of Judea Pearl.

See A Crash Course in Good and Bad Controls (2022), The Book of Why (2019) and Causality (2009).

Regression analysis is one of the most common tools in statistical analysis. It can be beautifully simple or very complex.

I can explain how it works in simple terms for people with zero background in statistics.

My favourite book on this, a hidden gem: Regression Analysis: A Practical Introduction by Jeremy Arkes

Video calls are a powerful tool for connecting people remotely.

During my time at the Centre for Collective Intelligence, I lead the development of Zeitgeist - a tool to run deliberative workshops, both remotely and in-person. We designed an interface that allowed participants to deliberate together and interact with workshop content.

In this topic, we’ll explore how small group video call experiences can offer a comfortable, natural, experience - and guide the development of an open source library that lowers the risk and effort to integrate video calls into applications.

Vendor lock-in

Building Zeitgeist, we faced a difficult architectural choice: Which video call provider to integrate? We did not have the resources to develop, host, or maintain our own.

There are plenty of video call service vendors to choose between. From those that offered the features we needed, we selected Amazon Chime SDK, primarily because it offered easy integration with our product. In Feburary 2026, Amazon sunset Chime. The SDK, whilst still available, is in maintenance mode: No new features or major changes are planned. Sooner or later, support will end, and Zeitgeist will need to migrate.

When you build an application that uses a video call service, you must use a video calling client library that can interact with the provider. These client libraries are created by the service providers, and they are not incentivised to support other providers. Most use their own terminology, and none have a common interface. This is vendor lock-in, and it represents a bit of risk for you:

• What if they increase their prices?

• What if they remove support for your region?

• What if they adopt unethical practices?

• What if another provider offers a feature you need?

It’s often a significant effort to replace a library like this, as you are likely to have heavily integrated it into your user interface.

An opportunity for OSS

At time of writing, there’s no open source, vendor neutral, video calling client library. (There are a few open source video calling services, but they require that you also host or work with a basic WebRTC provider.) There’s a niche for a library that can bridge the divide:

• Unified terminology for common video calling concepts

• Supports any number of vendors with a plugin architecture

• Supports any number of application/UI frameworks by publishing multiple libraries

• Provides an abstraction layer: import the library, build your UI, and connect to any vendor

• Easily switch between vendors with small configuration changes

• Easily extensible

I’ll be working on the beginnings of this, as an open source React library, over the coming year. Work we do on this topic will directly feed into the development of this library. Collaborators are welcome.

With the reach of generative AI, the term “slop” has risen in prominence. It represents a rejection of thoughtless content, often perceived to be low in quality or creativity.

Developers have always taken a view about the amount of effort they put in to a task - with a direct effect on the quality of code they deliver. This is a calculation based on deadlines, availability, resources, reputation, and plenty of other factors.

Opinion is strongly divided about AI-assisted coding. Some argue it supports increased productivity. Others cite poor quality outputs and unexpectedly high cost.

What does poor quality code look like?

I have been exploring the outputs from some generative AI coding tools. Some traits are pretty clear:

• Use of obscure and made-up terms

• Code mingled with narrative comments

• Spaghetti code

• Long or difficult to read functions

• Inflexible, hard-coded assumptions

• Dependencies on old libraries, difficult to upgrade

These issues are also referred to as technical debt.

Does it even matter?

Arguably, if code is only ever to be written by one person (or agent), then idiosyncrasies don't matter. Provided the developer knows where everything is, who cares if nobody else can figure it out? This may well be a naive position...

• Do you trust your coding agent to get everything right?
• What happens when you run out of credit?
• Do you want to be able to maintain it yourself?
• Are you sure nobody else will look at the code?

What can we do about it?

Over time, technical debt builds up in every project and the solution is to refactor the code - a sense-making process in which developers rewrite the offending parts. Solo developers and small teams working in isolation are particularly vulnerable to technical debt, but all teams experience it.

Running code reviews and writing code that any person can quickly understand is a good preventative measure. Some examples of established practices:

• Clear and meaningful variable names

• Use of well established design patterns

• Use of existing frameworks, rather than re-inventing the wheel

• DRY (Don’t Repeat Yourself)

• KISS (Keep It Simple Stupid)

• YAGNI (You Ain’t Gonna Need It)

• SOLID (principles for object oriented development)

• Separation of concerns (a precursor to the Single Responsibility Principle)

• Clean Code (and opinionated superset of principles and approaches)

How are coding agents any different?

Coding assistants work alone. They aren't trained to expect code review nor do they have code quality metrics. As they work, they develop personal shorthand and assumptions, which then shapes and styles the code they produce.

I have been collating a set of guidance, skills, and scripts for coding assistants, to raise the quality of their outputs. It's available here: instantiator/dev-qual

For this topic, we’ll discuss factors that contribute to code quality, and how they can be measured and applied - both to AI-assisted and human developed coding. With some metrics in hand, I’ll support you to compose rules for your language or framework of choice, or enhance those already found in dev-qual.

I can't say much about the place before I got the keys in 2011, but I'm happy to give my account of what happened after that. When the college started, the house was practically a ruin; no heating or hot water, a chunk of the roof was missing, the front door was broken and didn't lock!

Reid Hoffman famously said "If you're not embarrassed by the first version of your product, you've launched too late", and Newspeak House was no exception. Much like it feels with AI today, it was obvious that there was something important happening with technology and politics, people needed a place to meet to talk about it, and this was the best we had.

The history that is written is a combination of the events held here (newspeak.house/events) and the people who inhabited it (newspeak.house/fellowship); maybe you can ask your favourite model to give you an overview. Otherwise, feel free to ask some questions below and I'll do my best to tell you a story!

Much of my professional work has involved working between different professions - in particular I have interfaced between officials (in governments and regulators), academics, and civil society. But also even within government I found it useful to understand differing contexts between departments and (e.g.) analysts vs. policymakers - and develop skills to understand and navigate them.

I can talk to Fellows about practical skills and approaches that can be useful in collaborating across different mindsets and professions. This will include considerations of different incentives and pressures, and methods for learning from engagements, building in particular on thoughts I began in this post:

• www.oliver-marsh.com/academic-non-academic-collaborations-also-charity-donations

There are numerous opinions and camps around the ethics of various technologies (most strongly and recently, AI), which can create frictions even amongst political technologists who are aligned on other values - let alone people who do not consider themselves political technologists. I can provide advice for Fellows who wish to discuss or navigate polarised views around tech. I will not attempt to convert Fellows to any particular perspective, nor help them to do so with others. I will instead help them consider how they can locate their own work, projects, and views within the existing space; and engage with & prepare for potential criticisms and opposition.

My experience in this topic: Besides my broader work in tech accountability and safety, also at the NGO AlgorithmWatch I was responsible for a lot of work at the intersection of differering views on tech, and a spectrum of organisations who took different approaches and stances. I developed AlgorithmWatch's principles for use of Generative AI, building on surveys which showed diverging views of AI in our organisation. I was also responsible for our research strategy, much of which challenged technologies while also often using (and sometimes encouraged use of) the same technologies.

There is growing appetite for alternative ways of designing social media to give users ways to escape X, Facebook, TikTok and the like - and not just in the political technology community. In some quarters, particularly in Europe, there is growing political attention to this topic with high profile initiatives like rebuild.net (championed by former EU Commissioner for Competition Margrethe Vestager) and #SaveOurSocials. Bluesky in particular is being used and discussed ever more widely.

There are various theories of change for how these platforms could displace existing Big Tech and/or protect democracy - many of which are perhaps over-optimistic and need to be challenged and refined.

I can discuss with Fellows about some of the core innovations and methods underlying alternative social media design - such as interoperability and federation, "public value" AI systems, pro-social design, and middleware.

I can also discuss framing questions around the hoped-for benefits of the design. Who do you hope uses it, and how, in order to achieve your desired change (and is that realistic)? What is your conception of your user groups and their needs, and does that need refining or researching? What existing tech, if any, are you hoping to displace - and if so, how?

Some material for starters:

https://www.betterconflictbulletin.org/

https://www.journalofdemocracy.org/articles/the-future-of-platform-power-making-middleware-work/

https://www.prosocialdesign.org/

https://www.oliver-marsh.com/sideways-looks-35-a-better-internet-part-1-also-hayfever/

https://www.oliver-marsh.com/a-better-internet-part-2-also-mermaids/

The problem of addressing false and misleading content online has a long history, but it became supercharged particularly after 2016. It has spawned much terminology - mis/disinformation, malinformation, FIMI, influence operations - and a range of countermeasures, from fact-checking to media literacy to regulations. New techniques - from fake accounts to deepfakes LLM poisoning - keep emerging, generating new countermeasures but also causing new panics.

The topic has become a major one in political technology discussions, and one which constantly generates new efforts and ideas - many of which do not engage well with existing discussions. There have also been changing views on the nature of the problem itself. Is the risk that minds will be changed in ways that swing elections, or cause mass violence, or even just sow distrust? Or targeted operations to deceive elites or radicalise vunerable individuals? Is the veracity or reliability of information really the best way to address online discourse? Should we be focused more on polarisation and radicalisation than deception - or are these all inextricably related anyway? Or is this problem all overblown anyway, with risks of bad solutions - some of which may threaten freedom of speech?

I can help fellows who wish to engage with "disinformation" navigate this history, and work on an informed basis through core questions: What do I think the problem is? Is my proposed solution meeting the right audiences? Has anyone else tried what I want to do? What might critique or resistance to my solution be?

Some material for starters:

https://www.oliver-marsh.com/sideways-looks--29--ai-pril-fools-also-dating-data/

"Why misinformation must not be ignored": https://pubmed.ncbi.nlm.nih.gov/39666498/

"In the age of AI, keep calm and vote on": https://www.politico.eu/article/artificial-intelligence-elections-ballots-vote-chinsinau-seattle-policymakers-technology/

Economic models are often criticised for relying on absurd assumptions, such as perfect rationality. Despite this criticism, these models persist throughout our politics, policy, and in business.

If modelling is the art of simplifying a complex world through assumptions and abstractions, the question is not if these assumptions are true, but whether they are useful. What makes a good assumption? When does a simplification improve understanding vs worsen it? Let's discuss how economists approach these questions, from traditional modelling methods, to more recent developments in behavioural economics that adapt the assumptions with psychological research.

For further reading, Milton Friedman’s The Methodology of Positive Economics (1953)

Machine learning and AI algorithms are used for decisions that impact people's lives, such as assessing insurance risk, credit approval, and screening job applications. And these algorithms can sometimes discriminate against protected groups.

I am particularly concerned about proxy discrimination, where seemingly innocuous characteristics (e.g. postcode, size of car engine, etc) act as stand-ins for protected characteristics (e.g. race, sex). Even when protected characteristics are excluded from models, they can still discriminate via the proxies.

As a professional in economics and causal inference, I break this problem down through the lens of causal analysis. By understanding how inputs causally impact a model's outputs, we can assess if and when discrimination arises.

Further reading:

• Implementing Anti-Discrimination Policies in Statistical Profiling Models (2011)
• Reuters: Insight - Amazon scraps secret AI recruiting tool that showed bias against women (2018)
• Discrimination-Free Insurance Pricing (2021)

Economic theory often seeks to identify a model's "equilibrium" - a stable state towards which the systems trends and then rests. However, many economic, political and social systems have multiple equilibria. This provokes questions: which equilibrium will emerge, what determines that outcome, and how stable is it?

Understanding this illuminates the robustness and fragility of the systems on which we rely. It also explains why seemingly arbitrary features can be highly influential.

For example...

• Why a bank can be healthy one day and collapse with a bank run next.
• How and why power within an organisation can be concentrated on a single individual.
• Why political norms can remain entrenched for decades until suddenly they are not.
• How your life could change from going great to collapsing quicker than you hoped (and, more optimistically, vice versa).

Organising events in Newspeak Hall is one of the great perks of studying at Newspeak House; a fully equipped event space that holds up to ~100 people. It's hosted thousands of events for the college, everything from meetups to conferences, unconferences, dinners, hackathons, parties, exhibitions, lectures, co-working, debates, book launches, concerts, world cafes, fishbowls, screenings, workshops, and roundtables.

If you're interested in designing something together, I am happy to offer advice and support with event design, facilitation, logistics, catering, promotion, community strategy, and almost anything else you might need. Don’t worry if you don’t have a very clear idea of what you’d like to do yet - we can figure that out together.

I also manage the diary of the hall; it's a busy programme of community events, private hires, and events organised by fellows and faculty. As Fellowship Candidates, you will book dates through your agent. You might not be able to secure a specific exact date, but usually we can find a slot. Typically single day events are booked around a month in advance.

Finally, events should always be considered as part of a wider community strategy. I've written a short guide on how I typically approach this, though community-building is a fast-moving field with many open questions...

Statistics - both wonderfully insightful and easily to screw up.

Let's discuss what statistical estimates actually tell us:

• What's an "average treatment effect"? When's it meaningful vs not? FYI, half the statistics you've ever seen are this.
• What actually are statistical significance and p-values? A favourite tool of statisticians, and misunderstood by 95.6% of individuals.
• What makes the results of a regression model meaningful? Understanding how to make sense of the shape of data and fitting a linear model.
• Getting from correlation to causation. The various techniques in causal inference.
• Does the context of the stat apply to your context? Understanding external validity.

For further reading (aimed at those with a somewhat quantitative background), this little known gem: Regression Analysis: A Practical Introduction by Jeremy Arkes

What are the different forms and ways in which the collectives and political activities you work with are imagined? Getting clear about what metaphor is in play can help us stay honest when it comes to generalizations.

Metaphor often guides processes, obscures assumptions, and impregnates practical processes.
Revealing what the imagined pictures being used in politics are can be an excellent first step in figuring out what on earth is going on out there.