I’m at the STEAM ed Finland conference in Jyväskylä. Harri Ketamo is giving a talk on “micro-learning.” He recently won a prestigious prize for the best new ideas in Finland. He is interested in the use of AI for learning.

We don’t have enough good teachers globally, so we have to think about ed in new ways, Harri says. Can we use AI to bring good ed to everyone without hiring 200M new teachers globally? If we paid teachers equivalent to doctors and lawyers, we could hire those 200M. But we apparently not willing to do that.

One challenge: Career coaching. What do you want to study? Why? What are the skills you need? What do you need to know?

His company does natural language analysis — not word matches, but meaning. As an example he shows a shareholder agreement. Such agreements always have the same elements. After being trained on law, his company’s AI can create a map of the topic and analyze a block of text to see if it covers the legal requirements…the sort of work that a legal assistant does. For some standard agreements, we may soon not need lawyers, he predicts.

The system’s language model is a mess of words and relations. But if you zoom out from the map, the AI has clustered the concepts. At the Slush Sanghai conference, his AI could develop a list of the companies a customer might want to meet based on a text analysis of the companies’ web sites, etc. Likewise if your business is looking for help with a project.

Finland has a lot of public data about skills and openings. Universities’ curricula are publicly available.[Yay!] Unlike LinkedIn, all this data is public. Harri shows a map that displays the skills and competencies Finnish businesses want and the matching training offered by Finnish universities. The system can explore public information about a user and map that to available jobs and the training that is required and available for it. The available jobs are listed with relevancy expressed as a percentage. It can also look internationally to find matches.

The AI can also put together a course for a topic that a user needs. It can tell what the core concepts are by mining publications, courses, news, etc. The result is an interaction with a bot that talks with you in a Whatsapp like way. (See his paper “Agents and Analytics: A framework for educational data mining with games based learning”). It generates tests that show what a student needs to study if she gets a question wrong.

His newest project, in process: Libraries are the biggest collections of creative, educational material, so the AI ought to point people there. His software can find the common sources among courses and areas of study. It can discover the skills and competencies that materials can teach. This lets it cluster materials around degree programs. It can also generate micro-educational programs, curating a collection of readings.

His platform has an open an API. See Headai.

Q&A

Q: Have you done controlled experiments?

A: Yes. We’ve found that people get 20-40% better performance when our software is used in blended model, i.e., with a human teacher. It helps motivate people if they can see the areas they need to work on disappear over time.

Q: The sw only found male authors in the example you put up of automatically collated materials.

A: Small training set. Gender is not part of the metadata in Finland.

A: Don’t you worry that your system will exacerbate bias?

Q: Humans are biased. AI is a black box. We need to think about how to manage this

Q: [me] Are the topics generated from the content? Or do you start off with an ontology?

A: It creates its ontology out of the data.

Q: [me] Are you committing to make sure that the results of your AI do not reflect the built in biases?

A: Our news system on the Web presents a range of views. We need to think about how to do this for gender issues with the course software.

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I’m at the STEAM ed Finland conference in Jyväskylä. Geun-Sik Jo is a professor at Inha University in Seoul. He teaches AI and is an augmented reality expert. He also has a startup using AR for aircraft maintenance [pdf].

Why do we need mashups? To foster innovation. To create new systems very efficiently. He uses the integration of Google Maps into Craigslist as a simple example.

Interactive video — video with clickable spots — is often a mashup: click and go to a product page or send a text to a friend. E.g., https://www.wirewax.com or http://www.raptmedia or http://www.zentrick.com
http://www.zentrick.com .

TVs today are computers with their own operating systems and applications. Prof. Jo shows a video of AR TV. The “screen” is a virtual image displayed on special glasses.

If we mash up services, linked data clouds, TV content, social media, int an AR device, “we can do nice things.”

He shows some videos created by his students that present AR objects that are linked to the Internet: a clickable travel ad, location-based pizza ordering, a very cool dance instruction video, a short movie.

He shows a demo of the AI Content Creation System that can make movies. In the example, it creates a drama, mashing up scenes from multiple movies. The system identifies the characters, their actions, and their displayed emotions.

Is this creative? “I’m not a philosopher. I’m explaining this from the engineering point of view,” he says modestly. [If remixes count as creativity — and I certainly think they do — then it’s creative. Does that mean the AI system is creative? Not necessarily in any meaningful sense. Debate amongst yourselves.]

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I’m at the STEAM ed Finland conference in Jyväskylä. Sonja Amadae at Swansea University (also currently at Helsinki U.) works on robotic ethics. She will argue in this talk that computers are algorithmic, that they only do what they’re programmed to do, that they don’t understand what they’re doing and they don’t feel human experience. AI is, she concludes, a tool.

AI is like a human prosthetic that helps us walk. AI is an enhancement of human capabilities.

She will talk about about three cases.

Case 1: Generating a Rembrandt

A bank funded a project [cool site about it] to see what would happen if a computer had all of the data about Rembrandt’s portraits. They quantified the paintings: types, facial aspects including the size and distance of facial features, depth, contour, etc. They programmed the algorithm to create a portrait. The result was quite goood. People were pleased. Of course, it painted a white male. Is that creativity?

We are now recogizing the biases widespread in AI. E.g., “Biased algorithms are everywhere and one seeems to care” in MIT Tech Review by Will Knight. She also points to the time that Google mistakenly tagged black people as “gorillas.” So, we know there are limitations.

So, we fix the problem…and we end up with facial recognition systems so good that China can identify jaywalkers from surveillance cams, and then they post their images and names on large screens at the intersections.

Case 2: Forgery detection

The aim of one project was to detect forgeries. It was built on work done by Marits Michel van Dantzig in the 1950s. He looked at the brushstrokes on a painting; artists have signature brushstrokes. Each painting has on average 80,000 brushstrokes. A computer can compare a suspect painting’s brushstrokes with the legitimate brushstrokes of the artist. The result: the AI could identify forgeries 80% of the time from a single stroke.

Case 3: Computational creativity

She cites Wikipedia on Computational Creativity because she thinks it gets it roughly right:

Computational creativity (also known as artificial creativity, mechanical creativity, creative computing or creative computation) is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts.
The goal of computational creativity is to model, simulate or replicate creativity using a computer, to achieve one of several ends:[

• To construct a program or computer capable of human-level creativity.

• To better understand human creativity and to formulate an algorithmic perspective on creative behavior in humans.

• To design programs that can enhance human creativity without necessarily being creative themselves.

She also quotes John McCarthy:

‘To ascribe certain beliefs, knowledge, free will, intentions, consciousness, abilities, or wants to a machine or computer program is legitimate when such an ascription expresses the same information about the machine that it expresses about a person.’

If you google “computational art” and you’ll see many pictures created computationally. [Or here.] Is it genuine creativity? What’s going on here?

We know that AI’s products can be taken as human. A poem created by AI won a poetry contest for humans. E.g., “A home transformed by the lightning the balanced alcoves smother” But the AI doesn’t know it’s making a poem.

Can AI make art? Well, art is in the eye of the beholder, so if you think it’s art, it’s art. But philosophically, we need to recall Turing-Church Computability which states that the computation “need not be intelligible to the one calculating.” The fact that computers can create works that look creative does not mean that the machines have the awareness required for creativity.

Can the operations of the brain be simulated on a computer? The Turing-Church statement says yes. But now we have computing so advanced that it’s unpredictable, probabilistic, and is beyond human capability. But the computations need not be artistic to the one computing.

Computability has limits:

1. Information and data are not meaning or knowledge.

2. Every single moment of existence is unique in the universe. Every single moment we see a unique aspect of the world. A Turing computer can’t see the outside world. It only has what it’s internal to it.

3. Human mind has existential experience.

4. The mind can reflect on itself.

5. Scott Aaronson says that humans can exercise free will and AI cannot, based on quantum theory. [Something about quantum free states.]

6.The universe has non-computable systems. Equilibrium paths?

“Aspect seeing” means that we can make a choice about how we see what we see. And each moment of each aspect is unique in time.

In SF, the SPCA uses a robot to chase away homeless people. Robots cannot exercise compassion.

Computers compute. Humans create. Creativity is not computable.

Q&A

Q: [me] Very interesting talk. What’s at stake in the question?

A: AI has had such a huge presence in our lives. There’s a power of thinking about rationality as computation. Gets best articulated in game theory. Can we conclude that this game theoretical rationality — the foundational understanding of rationality — is computable? Do human brings anything to the table? This leads to an argument for the obsolescence of the human. If we’re just computational, then we aren’t capable of any creativity. Or free will. That’s what’s ultimately at stake here.

Q: Can we create machines that are irrational, and have them bring a more human creativity?

A: There are many more types of rationality than game theory sort. E.g., we are rational in connection with one another working toward shared goals. The dichotomy between the rational and irrational is not sufficient.
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I’ve been at a two-day workshop sponsored by the Michigan State Uiversity and the National Science Foundation: “Workshop on Trustworthy Algorithmic Decision-Making.” After multiple rounds of rotating through workgroups iterating on five different questions, each group presented its findings — questions, insights, areas of future research.

Seriously, I cannot capture all of this.

Conduct of Data Science

What are the problems?

• Who defines and how do we ensure good practice in data science and machine learning?

Why is the topic important? Because algorithms are important. And they have important real-world effects on people’s lives.

Why is the problem difficult?

• Wrong incentives.

• It can be difficult to generalize practices.

• Best practices may be good for one goal but not another, e.g., efficiency but not social good. Also: Lack of shared concepts and vocabulary.

How to mitigate the problems?

• Change incentives

• Increase communication via vocabularies, translations

• Education through MOOCS, meetups, professional organizations

• Enable and encourage resource sharing: an open source lesson about bias, code sharing, data set sharing

Accountability group

The problem: How to integratively assess the impact of an algorithmic system on the public good? “Integrative” = the impact may be positive and negative and affect systems in complex ways. The impacts may be distributed differently across a population, so you have to think about disparities. These impacts may well change over time

We aim to encourage work that is:

• Aspirationally casual: measuring outcomes causally but not always through randomized control trials.

• The goal is not to shut down algorithms to to make positive contributions that generat solutions.

This is a difficult problem because:

• Lack of variation in accountability, enforcements, and interventions.

• It’s unclear what outcomes should be measure and how. This is context-dependent

• It’s unclear which interventions are the highest priority

Why progress is possible: There’s a lot of good activity in this space. And it’s early in the topic so there’s an ability to significantly influence the field.

What are the barriers for success?

• Incomplete understanding of contexts. So, think it in terms of socio-cultural approaches, and make it interdisciplinary.

• The topic lies between disciplines. So, develop a common language.

• High-level triangulation is difficult. Examine the issues at multiple scales, multiple levels of abstraction. Where you assess accountability may vary depending on what level/aspect you’re looking at.

Handling Uncertainty

The problem: How might we holistically treat and attribute uncertainty through data analysis and decisions systems. Uncertainty exists everywhere in these systems, so we need to consider how it moves through a system. This runs from choosing data sources to presenting results to decision-makers and people impacted by these results, and beyond that its incorporation into risk analysis and contingency planning. It’s always good to know where the uncertainty is coming from so you can address it.

Why difficult:

• Uncertainty arises from many places

• Recognizing and addressing uncertainties is a cyclical process

• End users are bad at evaluating uncertain info and incorporating uncertainty in their thinking.

• Many existing solutions are too computationally expensive to run on large data sets

Progress is possible:

• We have sampling-based solutions that provide a framework.

• Some app communities are recognizing that ignoring uncertainty is reducing the quality of their work

How to evaluate and recognize success?

• A/B testing can show that decision making is better after incorporating uncertainty into analysis

• Statistical/mathematical analysis

Barriers to success

• Cognition: Train users.

• It may be difficult to break this problem into small pieces and solve them individually

• Gaps in theory: many of the problems cannot currently be solved algorithmically.

The presentation ends with a note: “In some cases, uncertainty is a useful tool.” E.g., it can make the system harder to game.

Adversaries, workarounds, and feedback loops

Adversarial examples: add a perturbation to a sample and it disrupts the classification. An adversary tries to find those perturbations to wreck your model. Sometimes this is used not to hack the system so much as to prevent the system from, for example, recognizing your face during a protest.

Feedback loops: A recidivism prediction system says you’re likely to commit further crimes, which sends you to prison, which increases the likelihood that you’ll commit further crimes.

What is the problem: How should a trustworthy algorithm account for adversaries, workarounds, and feedback loops?

Who are the stakeholders?

System designers, users, non-users, and perhaps adversaries.

Why is this a difficult problem?

• It’s hard to define the boundaries of the system

• From whose vantage point do we define adversarial behavior, workarounds, and feedback loops.

Unsolved problems

• How do we reason about the incentives users and non-users have when interacting with systems in unintended ways.

• How do we think about oversight and revision in algorithms with respect to feedback mechanisms

• How do we monitor changes, assess anomalies, and implement safeguards?

• How do we account for stakeholders while preserving rights?

How to recognize progress?

• Mathematical model of how people use the system

• Define goals

• Find stable metrics and monitor them closely

• Proximal metrics. Causality?

• Establish methodologies and see them used

• See a taxonomy of adversarial behavior used in practice

Likely approaches

• Security methodology to anticipating and unintended behaviors and adversarial interactions’. Monitor and measure

• Record and taxonomize adversarial behavior in different domains

• Test . Try to break things.

Barriers

• Hard to anticipate unanticipated behavior

• Hard to define the problem in particular cases.

• Goodhardt’s Law

• Systems are born brittle

• What constitutes adversarial behavior vs. a workaround is subjective.

• Dynamic problem

Algorithms and trust

How do you define and operationalize trust.

The problem: What are the processes through which different stakeholders come to trust an algorithm?

Multiple processes lead to trust.

• Procedural vs. substantive trust: are you looking at the weights of the algorithms (e.g.), or what were the steps to get you there?

• Social vs personal: did you see the algorithm at work, or are you relying on peers?

These pathways are not necessarily predictive of each other.

Stakeholders build truth through multiple lenses and priorities

• the builders of the algorithms

• the people who are affected

• those who oversee the outcomes

Mini case study: a child services agency that does not want to be identified. [All of the following is 100% subject to my injection of errors.]

• The agency uses a predictive algorithm. The stakeholders range from the children needing a family, to NYers as a whole. The agency knew what into the model. “We didn’t buy our algorithm from a black-box vendor.” They trusted the algorithm because they staffed a technical team who had credentials and had experience with ethics…and who they trusted intuitively as good people. Few of these are the quantitative metrics that devs spend their time on. Note that FAT (fairness, accountability, transparency) metrics were not what led to trust.

Temporality:

• Processes that build trust happen over time.

• Trust can change or maybe be repaired over time. “

• The timescales to build social trust are outside the scope of traditional experiments,” although you can perhaps find natural experiments.

Barriers:

• Assumption of reducibility or transfer from subcomponents

• Access to internal stakeholders for interviews and process understanding

• Some elements are very long term

What’s next for this workshop

We generated a lot of scribbles, post-it notes, flip charts, Slack conversations, slide decks, etc. They’re going to put together a whitepaper that goes through the major issues, organizing them, and tries to capture the complexity while helping to make sense of it.

There are weak or no incentives to set appropriate levels of trust

Key takeways:

• Trust is irreducible to FAT metrics alone

• Trust is built over time and should be defined in terms of the temporal process

• Isolating the algorithm as an instantiation misses the socio-technical factors in trust.

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I’m at a two-day inter-disciplinary workshop on “Trustworthy Algorithmic Decision-Making” put on by the National Science Foundation and Michigan State University. The 2-page whitepapers
from the participants are online. (Here’s mine.) I may do some live-blogging of the workshops.

Goals:

– Key problems and critical qustionos?

– What to tell pol;icy-makers and others about the impact of these systems?

– Product approaches?

– What ideas, people, training, infrastructure are needed for these approaches?

Excellent diversity of backgrounds: CS, policy, law, library science, a philosopher, more. Good diversity in gender and race. As the least qualified person here, I’m greatly looking forward to the conversations.

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I’m at a weekly AI talk put on by Harvard’s Berkman Klein Center for Internet & Society and the MIT Media Lab. Doaa Abu-Elyounes is giving a talk called “Bail or Jail? Judicial vs. Algorithmic decision making”.

Doaa tells us that this talk is a work in progress.

We’ve all heard now about AI-based algorithms that are being used to do risk assessments in pretrial bail decisions. She thinks this is a good place to start using algorithms, although it’s not easy.

The pre-trial stage is supposed to be very short. The court has to determine if the defendant, presumed innocent, will be released on bail or jailed. The sole considerations are supposed to be whether the def is likely to harm someone else or flee. Preventive detention has many efffects, mostly negative for the defendant.
(The US is a world leader in pre-trial detainees. Yay?)

Risk assessment tools have been used for more than 50 years. Actuarial tools have shown greater predictive power than clinical judgment, and can eliminate some of the discretionary powers of judges. Use of these tools have long been controversy What type of factors to include in the power? Is the use of demographic factors to make predictions fair to individuals?

Existing tools use regression analysis. Now machine learning can learn from much more data. Mechanical predictions [= machine learning] are more accurate than statistical predictions, but may not be explicable.

We think humans can explain their decisions and we want machines to be able to as well. But look at movie reviews. Humans can tell if a review is positive. We can teach which words are positive or negative, getting 60% accuracy. Or we can have a human label the reviews as positive or negative and let the machine figure out what the factor are — via machine leaning — in which case we get 80% accuracy but may lose explicability.

With pretrial situations, what is the automated task is that the machine should be performing?

There’s a tension between accuracy and fairness. Computer scientists are trying to quantify these questions What does a fair algorithm look like? John Kleinberg and colleagues did a study of this [this one?]. Their algorithms reduced violent crime by 25% with no change in jailing rates, without increasing racial disparities. In short, the algorithm seems to have done a more accurate job with less bias.

Doaa lists four assessment tools she will be looking at: the Pretrial Risk Assessment [this one?], the Public Safety Assessment, the Virginia Pretrial Risk assessment Instrument and the Colorado Pretrial Assessment Tool.

Doaa goes through questions that should be asked of these tools, beginning with: Which factors are considered in each? [She dives into the details for all four tools. I can’t capture it. Sorry.]

What are the sources of data? (3 out of 4 rely on interviews and databases.)

What is the quality of the data? “This is the biggest problem jurisdictions are dealing with when using such a tool.” “Criminal justice data is notoriously poor.” And, of course, if a machine learning system is trained on discriminatory data, its conclusions are likely to reflect those biases.

The tools neeed to be periodically validated using data from its own district’s population. Local data matters.

There should be separate scores for flight risk and public safety All but the PSA provide only a single score. This is important because there are separate remedies for the two concerns. E.g., you might want to lock up someone who is a risk to public safety, but take away the passport of someone who is a flight risk.

Finally, the systems should discriminate among reasons for flight risk. E.g., because the defendant can’t afford the cost of making it to court or because she’s fleeing?

Conclusion: Pretrial is the front door of the criminal justice system and affects what happens thereafter. Risk assessment tools should not replace judges, but they bring benefits. They should be used, and should be made as transparent as possible. There are trade offs. The tool will not eliminate all bias but might help reduce it.

Q&A

Q: Do the algorithms recognize the different situations of different defendants?

A: Systems do recognize this, but not in sophisticated ways. That’s why it’s important to understand why a defendant might be at risk of missing a court date. Maybe we could provide poor defendants with a Metro card.

Q: Could machine learning be used to help us be more specific in the types of harm? What legal theories might we drawn on to help with this?

A: [The discussion got too detailed for me to follow. Sorry.]

Q: There are different definitions of recidivism. What do we do when there’s a mismatch between the machines and the court?

A: Some states give different weights to different factors based on how long ago the prior crimes were committed. I haven’t seen any difference in considering how far ahead the risk of a possible next crime is.

Q: [me] While I’m very sympathetic to allowing machine learning to be used without always requiring that the output be explicable, when it comes to the justice system, do we need explanations so not only is justice done, but we can have trust that it’s being done?

A: If we can say which factors are going into a decision — and it’s not a lot of them — if the accuracy rate is much higher than manual systems, then maybe we can give up on always being able to explain exactly how it came to its decisions. Remember, pre-trial procedures are short and there’s usually not a lot of explaining going on anyway. It’s unlikely that defendants are going to argue over the factors used.

Q: [me] Yes, but what about the defendant who feels that she’s being treated differently than some other person and wants to know why?

A: Judges generally don’t explain how they came to their decisions anyway. The law sets some general rules, and the comparisons between individuals is generally within the framework of those rules. The rules don’t promise to produce perfectly comparable results. In fact, you probably can’t easily find two people with such similar circumstances. There are no identical cases.

Q: Machine learning, multilevel regression level, and human decision making all weigh data and produce an outcome. But ML has little human interaction, statistical analysis has some, and the human decision is all human. Yet all are in fact algorithmic: the judge looks at a bond schedule to set bail. Predictability as fairness is exacerbated by the human decisions since the human cannot explain her model.

Q: Did you find any logic about why jurisdictions picked which tool? Any clear process for this?

A: It’s hard to get that information about the procurement process. Usually they use consultants and experts. There’s no study I know of that looks at this.

Q: In NZ, the main tool used for risk assessment for domestic violence is a Canadian tool called ODARA. Do tools work across jurisdictions? How do you reconcile data sets that might be quite different?

A: I’m not against using the same system across jurisdictions — it’s very expensive to develop one from scratch — but they need to be validated. The federal tool has not been, as far as I know. (It was created in 2009.) Some tools do better at this than others.

Q: What advice would you give to a jurisdiction that might want to procure one? What choices did the tools make in terms of what they’re optimized for? Also: What about COMPAS?

A: (I didn’t talk about COMPAS because it’s notorious and not often used in pre-trial, although it started out as a pre-trial tool.) The trade off seems to be between accuracy and fairness. Policy makers should define more strictly where the line should be drawn.

Q: Who builds these products?

A: Three out of the four were built in house.

Q: PSA was developed by a consultant hired by the Arnold Foundation. (She’s from Luminosity.) She has helped develop a number of the tools.

Q: Why did you decide to research this? What’s next?

A: I started here because pre-trial is the beginning of the process. I’m interested in the fairness question, among other things.

Q: To what extent are the 100+ factors that the Colorado tool considers available publicly? Is their rationale for excluding factors public? Because they’re proxies for race? Because they’re hard to get? Or because back then 100+ seemed like too many? And what’s the overlap in factors between the existing systems and the system Kleinberg used?

A: Interviewing defendants takes time, so 100 factors can be too much. Kleinberg only looked at three factors. Another tool relied on six factors.

Q: Should we require private companies to reveal their algorithms?

A: There are various models. One is to create an FDA for algorithms. I’m not sure I support that model. I think private companies need to expose at least to the govt the factors that they’re including. Others would say I’m too optimistic about the government.

Q: In China we don’t have the pre-trial part, but there’s an article saying that they can make the sentencing more fair by distinguishing among crimes. Also, in China the system is more uniform so the data can be aggregated and the system can be made more accurate.

A: Yes, states are different because they have different laws. Exchanging data between states is not very common and may not even be possible.

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I’m at one of the weekly Harvard’s Berkman Klein Center for Internet & Society and MIT Media Lab talks. Alexandra Wood and Micah Altman are talking about “A harm reduction framework for algorithmic accountability over personal information” — a snapshot of their ongoing research at the Privacy Tools Project. The PTP is an interdisciplinary project that investigates tools for sharing info while preserving privacy.

Alexandra says they’ve been developing frameworks for assessing privacy risk when collecting personal data, and have been looking at the controls that can be used to protect individuals. They’ve found that privacy tools address a narrow slice of the problem; other types of misuse of data require other approaches.

She refers to some risk assessment algorithms used by the courts that have turned out to be racially biased, to have unbalanced error rates (falsely flagging black defendants as future criminals at twice the rate as white defendents), and are highly inaccurate. “What’s missing is an analysis of harm”What’s missing is an analysis of harm. “Current approaches to regulating algorithmic classification and decision-making largely elide harm,” she says. “The ethical norms in the law point to the broader responsibilities of the algorithms’ designers.”

Micah says there isn’t a lot of work mapping the loss privacy to other harms. The project is taking an interdisciplinary approach to this. [I don’t trust my blogging here. Don’t get upset with Micah for my ignorance-based reporting!]

Social science treats harm pragmatically. It finds four main dimensions of well-being: wealth, health, life satisfaction and the meaningful choices available to people. Different schools take different approaches to this, some emphasizing physical and psychological health, others life satisfaction, etc.

But to assess harm, you need to look at people’s lives over time. E.g., how does going to prison affect people’s lives? Being sentenced decreases your health, life-satisfaction, choices, and loer income. “The consequences of sentencing are persistent and individually catastrophic.”

He shows a chart from ProPublica based on Broward County data that shows that the risk scores for white defendants skews heavily toward lower scores while the scores for black defendants is more evenly distributed. This by itself doesn’t prove that it’s unfair. You have to look at the causes of the differences in those distributions.

Modern inference theory says something different about harm. A choice is harmful if the causal effect of that outcome is worse, and the causal effect is measured by potential outcomes. “The causal impact of smoking is not simply that you may get cancer, but includes the impact of not smoking”The causal impact of smoking is not simply that you may get cancer, but includes the impact of not smoking, such as possibly gaining weight. You have to look at the counter-factuals

The COMPAS risk assessment tool that has been the subject of much criticism is affected by which training data you use, choice of algorithm, the application of it to the individual, and the use of the score in sentencing. Should you exclude information about race? Or exclude any info that might violate people’s privacy? Or make it open or not? And how to use the outcomes?

Can various protections reduce harm from COMPAS? Racial features were not explicitly included in the COMPAS model. But there are proxies for race. Removing the proxies could lead to less accurate predictions, and make it difficult to study and correct for bias. That is, removing that data (features) doesn’t help that much and might prevent you from applying corrective measures.

Suppose you throw out the risk score. Judges are still biased. “The adverse impact is potentially greater when the decision is not informed by an algorithm’s prediction.” A recent paper by John Kleinberg showed that “algorithms predicting pre-trial assessments were less biased than decisions made by human judges”algorithms predicting pre-trial assessments were less biased than decisions made by human judges. [I hope I got that right. It sounds like a significant finding.]

There’s another step: going from the outcomes to the burdens these outcomes put on people. “An even distribution of outcomes can produce disproportionate burdens.” E.g. juvenile defendants have more to lose — more of their years will be negatively affected by a jail sentence — so having the same false positive and negatives for adults and juveniles would impost a greater burden on the juveniles. When deciding it an algorithmic decision is unjust, you can’t just look at the equality of error rates.

A decision is unjust when it is: 1. Dominated (all groups pay a higher burden for the same social benefit); 2. Unprogressive (higher relative burdens on members of classes who are less well off); 3. Individually catastrophic (wrong decisions are so harmful that it reduces the well being of individuals in members of a known class); 4) Group punishment (an effect on an entire disadvantaged class.)

For every decision, theere are unavoidable constraints: a tradeoff between the individual and the social group; a privacy cost; can’t be equally accurate in all categories; can’t be fair without comparing utility across people; it’s impossible to avoid constraints by adding human judgment because the human is still governed by these constraints.

Micah’s summary for COMPAS: 1. Some protections would be irrelevant (inclusion of sensitive characteristics and and protection of indvidual information). Other protections would be insufficient (no intention to discriminate, open source/open data/FCRA).

Micah ends with a key question about fairness that has been too neglected: “Do black defendants bear a relatively higher cost than whites from bad decisions that prevent the same social harms?”

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Stefania Druga, a graduate student in the Personal Robots research group at the MIT Media Lab, is leading a discussion focusing on how children can help us to better understand and utilize AI. She’s going to talk about some past and future research projects.

She shows two applications of AI developed for kids The first is Cayla, a robotic doll. “It got hacked three days after it was released in Germany” and was banned there. The second is Aristotle, which was supposed to be an Alexa for kids. A few weeks ago Mattel decided not to release it, after “parents worried about their kids’ privacy signed petitions”parents worried about their kids’ privacy signed petitions.

Stefania got interested in what research was being done in this field. She found a couple of papers. One (Lovato & Piper 2015
) showed that children mirrored how they interact with Siri, e.g., how angry or assertive. Antother (McReynolds et al., 2017 [pdf]) found that how children and parents interact with smart toys revealed how little parents and children know about how much info is being collected by these toys, e.g. Hello Barbie’s privacy concerns. It also looked at how parents and children were being incentivized to share info on social media.

Stefania’s group did a pilot study, having parents and 27 kids interact with various intelligent agents, including Alexa, Julie Chatbot, Tina the T.Rex, and Google Home. Four or five chidlren would interact with the agent at a time, with an adult moderator. Their parents were in the room.

Stefania shows a video about this project. After the kids interacted with the agent, they asked if it was smarter than the child, if it’s a friend, if it has feelings. Children anthropomorphize AIs in playful ways. Most of the older children thought the agents were more intelligent than they were, while the younger children weren’t sure. Two conclusions: Makers of these devices should pay more attention to how children interact with them, and we need more research.

What did the children think? They thought the agents were friendly and truthful. “They thought two Alexa devices were separate individuals.”They thought two Alexa devices were separate individuals. The older children thought about these agents differently than the younger ones do. This latter may be because of how children start thinking about smartness as they progress through school. A question: do they think about artificial intelligence as being the same as human intelligence?

After playing with the agents, they would probe the nature of the device. “They are trying to place the ontology of the device.”

Also, they treated the devices as gender ambiguous.

The media glommed onto this pilot study. E.g., MIT Technology Review: “Growing Up with Alexa.” Or NYTimes: “Co-Parenting with Alexa.” Wired: Understanding Generation Alpha. From these articles, it seems that people are really polarized about the wisdom of introducing children to these devices.

Is this good for kids? “It’s complicated,” Stefania says. The real question is: How can children and parents leverage intelligent agents for learning, or for other good ends?

Her group did another study, this summer, that had 30 pairs of children and parents navigate a robot to solve a maze. They’d see the maze from the perspective of the robot. They also saw a video of a real mouse navigating a maze, and of another robot solving the maze by itself. “Does changing the agent (themselves, mouse, robot) change their idea of intelligence?”Does changing the agent (themselves, mouse, robot) change their idea of intelligence? Kids and parents both did the study. Most of the kids mirrored their parents’ choices. They even mirrored the words the parents used…and the value placed on those words.

What next? Her group wants to know how to use these devices for learning. They build extensions using Scratch, including for an open source project called Poppy. (She shows a very cool video of the robot playing, collaborating, painting, etc.) Kids can program it easily. Ultimately, she hopes that this might help kids see that they have agency, and that while the robot is smart at some things, people are smart at other things.

Q&A

Q: You said you also worked with the elderly. What are the chief differences?

A: Seniors and kids have a lot in common. They were especially interested in the fact that these agents can call their families. (We did this on tablets, and some of the elderly can’t use them because their skin is too dry.)

Q: Did learning that they can program the robots change their perspective on how smart the robots are?

A: The kids who got the bot through the maze did not show a change in their perspective. When they become fluent in customizing it and understanding how it computes, it might. It matters a lot to have the parents involved in flipping that paradigm.

Q: How were the parents involved in your pilot study?

A: It varied widely by parent. It was especially important to have the parents there for the younger kids because the device sometimes wouldn’t understand the question, or what sorts of things the child could ask it about.

Q: Did you look at how the participants reacted to robots that have strong or weak characteristics of humans or animals.

A: We’ve looked at whether it’s an embodied intelligent agent or not, but not at that yet. One of our colleagues is looking at questions of empathy.

Q: [me] Do the adults ask their children to thank Siri or other such agents?

A: No.

Q: [me] That suggests they’re tacitly shaping them to think that these devices are outside of our social norms?

Q: In my household, the “thank you” extinguishes itself: you do it a couple of times, and then you give it up.

A: This indicates that these systems right now are designed in a very transactional way. You have to say the wake up call every single phrase. But these devices will advance rapidly. Right now it’s unnatural conversation. But wth chatbots kids have a more natural conversation, and will say thank you. And kids want to teach it things, e.g, their names or favorite color. When Alexa doesn’t know what the answer is, the natural thing is to tell it, but that doesn’t work.

Q: Do the kids think these are friends?

A: There’s a real question around animism. Is it ok for a device to be designed to create a relationship with, say, a senior person and to convince them to take their pills? My answer is that people tend to anthropomorphize everything. Over time, kids will figure out the limitations of these tools.

Q: Kids don’t have genders for the devices? The speaking ones all have female voices. The doll is clearly a female.

A: Kids were interchanging genders because the devices are in a fluid space in the spectrum of genders. “They’re open to the fact that it’s an entirely new entity.”

Q: When you were talking about kids wanting to teach the devices things, I was thinking maybe that’s because they want the robot to know them. My question: Can you say more about what you observed with kids who had intelligent agents at home as opposed to those who do not?

A: Half already had a device at home. I’m running a workshop in Saudi Arabia with kids there. I’m very curious to see the differences. Also in Europe. We did one in Colombia among kids who had never seen an Alexa before and who wondered where the woman was. They thought there must be a phone inside. They all said good bye at the end.

Q: If the wifi goes down, does the device’s sudden stupidness concern the children? Do they think it died?

A: I haven’t tried that.

[me] Sounds like that would need to go through an IRB.

Q: I think your work is really foundational for people who want to design for kids.

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J. Nathan Matias is giving a talk at the weekly AI session held by MIT Media Lab and Harvard’s Berkman Klein Center for Internet & Society. The title is: Testing the social impact of real-time algorithm decisions. (SPOILER: Nate is awesome.) Nathan will be introducing CivilServant.io to us, a service for researching the effects of tech and how it can be better directed to toward the social outcomes we (the civil society “we”) desire. (That’s my paraphrase.)

In 2008, the French government approved a law against Web sites that encourage anorexia and bulimia. In 2012, Instagram responded to pressure to limit hashtags that “actively promote self-harm.” Instagram had 40M users, almost as many as France’s 55M active Net users. Researchers at Georgia Tech several years later found that some self-harm sites on Instagram had higher engagement after Instagram’s actions. “ If your algorithm reliably detects people who are at risk of committing suicide, what next? ” If your algorithm reliably detects people who are at risk of committing suicide, what next? If the intervention isn helpful, your algorithm is doing harm.

Nathan shows a two-axis grid for evaluating algorithms: fair-unfair and benefits-harms. Accuracy should be considered to be on the same axis as fairness because it can be measured mathematically. But you can’t test the social impact without putting it into the field. “I’m trying to draw attention to the vertical axis [harm-benefit].”

We often have in mind a particular pipeline: training > model > prediction > people . Sometimes there are rapid feedback loops where the decisions made by people feed back into the model. A judicial system’s prediction risk scores may have no such loop. But the AI that manages a news feed is probably getting the readers’ response as data that tunes the model.

We have organizations that check the quality of items we deal with: UL for electrical products, etc. But we don’t have that sort of consumer protection for social tech. The results are moral panics, bad policies, etc. This is the gap Nate is trying to fill with CivilServant.io, a project supported by the Media Lab and GlobalVoices.

Here’s an example of one of CivilServant’s projects:

Managing fake news is essential for democracy. The social sciences have been dealing with this for quite a while by doing research on individual perception and beliefs, on how social context and culture influence beliefs … and now on algorithms that make autonomous decisions that affect us as citizens e.g., newsfeeds. Newsfeeds work this way: someone posts a link. People react to it, e.g. upvote, discuss, etc. The feed service watches that behavior and uses it to promote or demote the item. And then it feeds back in.

We’ve seen lots of examples of pernicious outcomes of this. E.g., at Reddit an early upvote can have dramatic impact on its ratings over time.

What can we do to govern online misinfo? We could surveill and censor. We could encourage counter-speech. We can imagine some type of algorithmic governance. We can use behavioral nudges, e.g. Facebook tagging articles as “disputed.” But all of these assume that these interventions change behaviors and beliefs. Those assumptions are not always tested.

Nate was approached by /r/worldnews at Reddit, a subreddit with14M subscribers and 70 moderators. At Reddit, moderating can be a very time consuming effort. (Nate spoke to a Reddit mod who had stopped volunteering at a children’s hospital in order to be a mod because she thought she could do more good that way.) This subreddit’s mods wanted to know if they could question the legitimacy of an item without causing it to surge on the platform. Fact-checking a post could nudge Reddit’s AI to boost its presence because of the increased activity.

So, they did an experiment asking people to fact check an article, or fact check and downvote if you can’t verify it. They monitored the ranking of the articles by Reddit for 3 months. [Nate now gives some math. Sorry I can’t capture (or understand) it.] The result: to his surprise, “encouraging fact checking reduced the average rank position of an article”encouraging fact checking reduced the average rank position of an article. Encouraging fact checking and down-voting reduced the spread of inaccurate news by Reddit’s algorithms. [I’m not confident I’m getting that right

Why did encouraging fact checking reduce rankings, but fact checking and voting did not? The mods think this might be because it gave users a constructive way to handle articles from reviled sources, reducing the number of negative comments about them. [I hope I’m getting this right.] Also, “reactance” may have nudged people to upvote just to spite the instructions. Also, users may have mobilized friends to vote on the artciles. Also, encouraging two tasks (fact check and then vote) rather than one may have influenced he timing of the algorithm, making the down-votes less impactful.

This is what Nate calls an “AI-Nudge”: a “second-order effect of influencing human behavior on the behavior of an algorithmic system.” It means you have to think about how humans interact with AI.

Often when people are working on AI, they’re starting from computer science and math. The question is: how can we use social science methods to research the effect of AI? Paluck and Cialdini see a cycle of Pilot/Lab experiments > qualitative methods > field experiences > theory / policy / design. In the Reddit example, Nathan spent considerable time with the community to understand their issues and how they interact with the AI.

Another example of a study: identifying and reducing side-effects of automated copyright law enforcement on Twitter. When people post something to Twitter, bots monitor it to see if violates copyright, resulting in a DMCA takedown notice being issued. Twitter then takes it down. The Lumen Project from BKC archives these notices. The CivilService project observes those notices in real time to study the effects. E.g., “a user’s tweets per day tends to drop after they receive a takedown notice … for a 42-day period”a user’s tweets per day tends to drop after they receive a takedown notice, and then continues dropping throughout the 42-day period they researched. Why this long-term decrease in posting? Maybe fear and risk. Maybe awareness of surveillance.

So, how can these chilling effects be reduced? The CivilService project automatically sends users info about their rights and about surveillance. The results of this intervention are not in yet. The project hopes to find ways to lessen the public’s needless withdrawal from social media. The research can feed empirical legal studies. Policymakers might find it useful. Civil rights orgs as well. And the platforms themselves.

In the course of the Q&As, Nathan mentions that he’s working on ways to explain social science research that non-experts can understand. CivilService’s work is with user communities and it’s developed a set of ways for communicating openly with the users.

Q: You’re trying to make AI more fair…

A: I’m doing consumer protection, so as experts like you work on making AI more fair, we can see the social effects of interventions. But there are feedback loops among them.

Q: What would you do with a community that doesn’t want to change?

A: We work with communities that want our help. In the 1970s, Campbell wrote an essay: “The Experimenting Society.” He asked if by doing behavioral research we’re becoming an authoritarian society because we’re putting power in the hands of the people who can afford to do the research. He proposed enabling communities to do their own studies and research. He proposed putting data scientists into towns across the US, pool their research, and challenge their findings. But this was before the PC. Now it’s far more feasible.

Q: What sort of pushback have you gotten from communities?

A: Some decide not to work with us. In others, there’s contention about the shape of the project. Platforms have changed how they view this work. Three years ago, the platforms felt under siege and wounded. That’s why I decided to create an independent organization. The platforms have a strong incentive to protect their reputations.

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John Palfrey is doing a launch event at the Berkman Klein Center for his new book, Safe Spaces, Brave Spaces: Diversity and Free Expression in Education. John is the Head of School at Phillips Academy Andover, and for many years was the executive director of the Berkman Klein Center and the head of the Harvard Law School Library. He’s also the chairman of the board of the Knight Foundation. This event is being put on by the BKC, the Law Library, and Andover. His new book is available on paper, or online as an open access book. (Of course it is. It’s John Palfrey, people!)

[Disclosure: Typical conversations about JP, when he’s not present, attempt — and fail — to articulate his multi-facted awesomeness. I’ll fail at this also, so I’ll just note that JP is directly responsible for my affiliation with the BKC and and for my co-directorship of the Harvard Library Innovation Lab…and those are just the most visible ways in which he has enabled me to flourish as best I can. ]

Also, at the end of this post I have some reflections on rules vs. models, and the implicit vs. explicit.

John begins by framing the book as an attempt to find a balance between diversity and free expression. Too often we have pitted the two against each other, especially in the past few years, he says: the left argues for diversity and the right argues for free expression. It’s important to have both, although he acknowledges that there are extremely hard cases where there is no reconciliation; in those cases we need rules and boundaries. But we are much better off when we can find common ground.

“This may sound old-fashioned in the liberal way. And that’s true,” he says. But we’re having this debate in part because young people have been advancing ideas that we should be listening to. We need to be taking a hard look.

Our institutions should be deeply devoted to diversity, equity and inclusion. Our institutions haven’t been as supportive of these as they should be, although they’re getting better at it, e.g. getting better at acknowledging the effects of institutional racism.

The diversity argument pushes us toward the question of “safe spaces.” Safe spaces are crucial in the same way that every human needs a place where everyone around them supports them and loves them, and where you can say dumb things. We all need zones of comfort, with rules implicit or explicit. It might be a room, a group, a virtual space… E.g., survivors of sexual assault need places where they know there are rules and they can express themselves without feeling at risk.

But, John adds, there should also be spaces where people are uncomfortable, where their beliefs are challenged.

Spaces of both sorts are experienced differently by different people. Privileged people like John experience spaces as safe that others experience as uncomfortable.

The examples in his book include: trigger warnings, safe spaces, the debates over campus symbols, the disinvitation of speakers, etc. These are very hard to navigate and call out for a series of rules or principles. Different schools might approach these differently. E.g.,students from the Gann Academy are here tonight, a local Jewish high school. They well might experience a space differently than students at Andover. Different schools well might need different rules.

Now John turns it over to students for comments. (This is very typical JP: A modest but brilliant intervention and then a generous deferral to the room. I had the privilege of co-teaching a course with him once, and I can attest that he is a brilliant, inspiring teacher. Sorry, but to be such a JP fanboy, but I am at least an evidence-based fanboy.) [I have not captured these student responses adequately, in some cases simply because I had trouble hearing them. They were remarkable, however. And I could not get their names with enough confidence to attempt to reproduce them here. Sorry!]

Student Responses

Student: I graduated from Andover and now I’m at Harvard. I was struck by the book’s idea that we need to get over the dichotomy between diversity and free expression. I want to address Chapter 5, about hate speech. It says each institution ought to assess its own values to come up with its principles about speech and diversity, and those principles ought to be communicated clearly and enforced consistently. But, I believe, we should in fact be debating what the baseline should be for all institutions. We don’t all have full options about what school we’re going to go to, so there ought to be a baseline we all can rely on.

JP: Great critique. Moral relativism is not a good idea. But I don’t think one size fits all. In the hardest cases, there might be sharpest limits. But I do agree there ought to be some sort of baseline around diversity, equity, and inclusion. I’d like to see that be a higher baseline, and we’ve worked on this at Andover. State universities are different. E.g., if a neo-Nazi group wants to demonstrate on a state school campus and they follow the rules laid out in the Skokie case, etc., they should be allowed to demonstrate. If they came to Andover, we’d say no. As a baseline, we might want to change the regulations so that the First Amendment doesn’t apply if the experience is detrimental to the education of the students; that would be a very hard line to draw. Even if we did, we still might want to allow local variations.

Student: Brave spaces are often build from safe spaces. E.g., at Andover we used Facebook to build a safe space for women to talk, in the face of academic competitions where misogyny was too common. This led to creating brave places where open, frank discussion across differences was welcomed.

JP: Yes, giving students a sense of safety so they can be brave is an important point. And, yes, brave spaces do often grow from safe spaces.

Andover student: I was struck by why diversity is important: the cross-pollination of ideas. But from my experience, a lot of that hasn’t occurred because we’re stuck in our own groups. There’s also typically a divide between the students and the faculty. Student activitsts are treated as if they’re just going through a phase. How do we bridge that gap?

JP: How do we encourage more cross-pollination? It’s a really hard problem for educators. I’ve been struck by the difference between teaching at Harvard Law and Andover in terms of the comfort with disagreeing across political divides; it was far more comfortable at the Law School. I’ve told students if you present a paper that disagrees with my point of view and argues for it beautifully, you’ll do better than parroting ideas back to me. Second, we have to stop using demeaning language to talk about student activists. BTW, there is an interesting dynamic, as teachers today may well have been activists when they were young and think of themselves as the reformers.

Student: [hard to hear] At Andover, our classes were seminar-based, which is a luxury not all students have. Also: Wouldn’t encouraging a broader spread of ideas create schisms? How would you create a school identity?

JP: This echoes the first student speaker’s point about establishing a baseline. Not all schools can have 12 students with two teachers in a seminar, as at Andover. We need to find a dialectic. As for schisms: we have to communicate values. Institutions are challenged these days but there is a huge place for them as places that convey values. There needs to be some top down communication of those values. Students can challenge those values, and they should. This gets at the heart of the problem: Do we tolerate the intolerant?

Student: I’m a graduate of Andover and currently at Harvard. My generation has grown up with the Internet. What happens when what is supposed to be a safe space becomes a brave space for some but not all? E.g., a dorm where people speak freely thinking it’s a safe space. What happens when the default values overrides what someone else views as comfortable? What is the power of an institution to develop, monitor, and mold what people actually feel? When communities engage in groupthink, how can an institution construct space safes?

JP: I don’t have an easy answer to this. We do need to remember that these spaces are experienced differently by different people, and the rules ought to reflect this. Some of my best learning came from late night bull sessions. It’s the duty of the institution to do what it can to enable that sort of space. But we also have to recognize that people who have been marginalized react differently. The rule sets need to reflect that fact.

Student: Andover has many different forum spaces available, from hallways to rooms. We get to decide to choose when and where these conversations will occur. For a more traditional public high school where you only have 30-person classroom as a forum, how do we have the difficult conversations that students at Andover choose to have in more intimate settings?

JP: The size and rule-set of the group matters enormously. Even in a traditional HS you can still break a class into groups. The answer is: How do you hack the space?

Student: I’m a freshman at Harvard. Before the era of safe spaces, we’d call them friends: people we can talk with and have no fear that our private words will be made public, and where we will not be judged. Safe spaces may exclude people, e.g., a safe space open only to women.

JP Andover has a group for women of color. That excludes people, and for various reasons we think that’s entirely appropriate an useful.

Q&A

Q [Terry Fisher]: You refer frequently to rule sets. If we wanted to have a discussion in a forum like this, you could announce a set of rules. Or the organizer could announce values, such as: we value respect, or we want people to take the best version of what others say. Or, you could not say anything and model it in your behavior. When you and I went to school, there were no rules in classrooms. It was all done by modeling. But this also meant that gender roles were modeled. My experience of you as a wonderful teacher, JP, is that you model values so well. It doesn’t surprise me that so many of your students talk with the precision and respectfulness that you model. I am worried about relying on rule sets, and doubt their efficacy for the long term. Rather, the best hope is people modeling and conveying better values, as in the old method.

JP: Students, Terry Fischer was my teacher. May answer will be incredibly tentative: It is essential for an institution to convey its values. We do this at Andover. Our values tell us, for example, that we don’t want gender-based balance and are aware that we are in a misogynist culture, and thus need reasonable rules. But, yes, modeling is the most powerful.

Q [Dorothy Zinberg]: I’ve been at Harvard for about 70 yrs and I have seen the importance of an individual in changing an institution. For example, McGeorge Bundy thought he should bring 12 faculty to Harvard from non-traditional backgrounds, including Erik Erikson who did not have a college degree. He had been a disciple of Freud’s. He taught a course at Harvard called “The Lifecycle.” Every Harvard senior was reading The Catcher in the Rye. Erikson was giving brilliant lectures, but I told him it was from his point of view as a man, and had nothing to do with the young women. So, he told me, a grad student, to write the lectures. No traditional professor would have done that. Also: for forming groups, there’s nothing like closing the door. People need to be able to let go and try a lot of ideas.

Q: I am from the Sudan. How do you create a safe space in environments that are exclusive. [I may have gotten that wrong. Sorry.] How do you acknowledge the native American tribes whose land this institution is built on, or the slaves who did the building?

JP: We all have that obligation. [JP gives some examples of the Law School recently acknowledging the slave labor, and the money from slave holders, that helped build the school.]

Q: You used a kitchen as an example of a safe space. Great example. But kitchens are not established or protected by any authority. It’s a new idea that institutions ought to set these up. Do you think there should be safe spaces that are privately set up as well as by institutions? Should some be permitted to exclude people or not?

(JP asks a student to respond): Institutional support can be very helpful when you have a diversity of students. Can institutional safe spaces supplement private ones? I’m not sure. And I do think exclusive groups have a place. As a consensus forms, it’s important to allow the marginalized voices to connect.

Q [ head of Gann]: I’m a grad of Phillips Academy. As head of a religious school, we’re struggling with all these questions. Navigating these spaces isn’t just a political or intellectual activity. It is a work of the heart. If the institution thinks of this only as a rational activity and doesn’t tend to the hearts of our students, and is not explicit about the habits of heart we need to navigate these sensitive waters, only those with natural emotional skills will be able to flourish. We need to develop leaders who can turn hard conversations into generative ones. What would it look like to take on the work of developing social and emotional development?

JP: Ive been to Gann and am confident that’s what you’re doing. And you can see evidence of Andover’s work on it in the students who spoke tonight. Someone asked me if a student became a Nazi, would you expel him? Yes, if it were apparent in his actions, but probably not for his thoughts. Ideally, our students won’t come to have those views because of the social and emotional skills they’re learning. But people in our culture do have those views. Your question brings it back to the project of education and of democracy.

[This session was so JP!]

A couple of reactions to this discussion without having yet read the book.

First, about Prof. Fisher’s comment: I think we are all likely to agree that modeling the behavior we want is the most powerful educational tool. JP and Prof. Fisher, are both superb, well, models of this.

But, as Prof. Fisher noted in his question, the dominant model of discourse for our generation silently (and sometimes explicitly) favored males, white middle class values, etc. Explicit rules weren’t as necessary because we had internalized them and had stacked the deck against those who were marginalized by them. Now that diversity has thankfully become an explicit goal, and now that the Internet has thrown us into conversations across differences, we almost always need to make those rules explicit; a conversation among people from across divides of culture, economics, power, etc. that does not explicitly acknowledge the different norms under which the participants operate is almost certainly going to either fragment or end in misunderstanding.

(Clay Shirky and I had a collegial difference of opinion about this about fifteen years ago. Clay argued for online social groups having explicit constitutions. I argued
for the importance of the “unspoken” in groups, and the damage that making norms explicit can cause.)

Second, about the need for setting a baseline: I’m curious to see what JP’s book says about this, because the evidence is that we as a culture cannot agree about what the baseline is: vociferous and often nasty arguments about this have been going on for decades. For example, what’s the baseline for inviting (or disinviting) people with highly noxious views to a private college campus? I don’t see a practical way forward for establishing a baseline answer. We can’t even get Texas schools to stop teaching Creationism.

So, having said that modeling is not enough, and having despaired at establishing a baseline, I think I am left being unhelpfully dialectical:

1. Modeling is essential but not enough.

2. We ought to be appropriately explicit about rules in order to create places where people feel safe enough to be frank and honest…

3. …But we are not going to be able to agree on a meaningful baseline for the U.S., much less internationally — “meaningful” meaning that it is specific enough that it can be applied to difficult cases.

4. But modeling may be the only way we can get to enough agreement that we can set a baseline. We can’t do it by rules because we don’t have enough unspoken agreement about what those rules should be. We can only get to that agreement by seeing our leading voices in every field engage across differences in respectful and emotionally truthful ways. So at the largest level, I find I do agree with Prof. Fisher: we need models.

5. But if our national models are to reflect the values we want as a baseline, we need to be thoughtful, reflective, and explicit about which leading voices we want to elevate as models. We tend to do this not by looking for rules but by looking for Prof. Fisher’s second alternative: values. For example, we say positively that we love John McCain’s being a “maverick” or Kamala Harris’ careful noting of the evidence for her claims, and we disdain Trump’s name-calling. Rules derive from values such as those. Values come before rules.

I just wish I had more hope about the direction we’re going in…although I do see hopeful signs in some of the model voices who are emerging, and most of all, in the younger generation’s embrace of difference.

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