The (non-)Ethics of Capitalism

Back in 2018, there was a survey on Gallup, about honesty and ethical standards, per profession

More than four in five Americans (84%) again rate the honesty and ethical standards of nurses as “very high” or “high,” earning them the top spot among a diverse list of professions for the 17th consecutive year. At the same time, members of Congress are again held in the lowest esteem, as nearly 58% of Americans say they have “low” or “very low” ethical standards. Telemarketers join members of Congress as having a majority of low/very low ratings.

One might wonder if there is a correlation between ethics and salary. Using the U.S. Bureau of Labor Statistics (BLS) National Occupational Employment and Wage Estimates data, one could easily get the average salary in the U.S. for some professions. For the others, one has to dig a bit more

Store all those information in a csv file, and then, add the honesty/ethics high-very high percentage,

download.file("https://freakonometrics.hypotheses.org/files/2025/08/gallup_ethics_salaries.csv", destfile = "data.csv")
df = read.csv("data.csv", stringsAsFactors = FALSE)

and we can get a plot.

plot(df$percent_high, df$avg_salary_usd,
     xlab = "Honesty/Ethics rated 'Very high/High' (%)",
     ylab = "Average salary (USD)",
     pch = 19)
# label points (may overlap a bit)
text(df$percent_high, df$avg_salary_usd, labels = df$profession, pos = 4, cex = 0.6)

At best, there is no link between salaries and honesty / ethics.

IJCAI 2025 Workshop on Explainable Artificial Intelligence (XAI)

This week, Marouane will present recent work at the Workshop on Explainable Artificial Intelligence (XAI), at IJCAI in Montréal,

Explainable Artificial Intelligence (XAI) addresses the challenge of how to communicate and explain the decision-making of AI systems. The need for explainability increases as AI systems are deployed in critical applications, raising questions such as: how should explainable AI systems be designed? What queries should AI systems be able to answer about their models and decisions? How should user interfaces communicate decision making? What types of user interactions should be supported? And how should explanation quality be assessed?

The Explainable AI (XAI) workshop at IJCAI provides a forum for discussing recent research on XAI methods, highlighting and documenting promising approaches, and encouraging further work, thereby fostering connections among researchers interested in AI, human-computer interaction, and cognitive theories of explanation and transparency. This topic is of particular importance but not limited to machine learning, AI planning, and knowledge reasoning & representation.

In addition to encouraging descriptions of original or recent contributions to XAI (i.e., theory, simulation studies, subject studies, demonstrations, applications), we will welcome contributions that: survey related work; describe key issues that require further research; or highlight relevant challenges of interest to the AI community and plans for addressing them.

The paper, Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models, is available on ArXiv.