#Business Strategy in Data Science

[kartikreddyblog]

Integrating Data Science Management with Business Strategy: Aligning Goals and Objectives

[rsayoub]

🚨 Stop Believing the AI Hype, that’s the title of my latest conversation on the Localization Fireside Chat with none other than @Dr. Sidney Shapiro, Assistant Professor at the @Dillon School of Business, University of Lethbridge. We dive deep into what AI can actually do, and more importantly, what it can’t. From vibe coders and synthetic data to the real-world consequences of over-trusting black-box models, this episode is packed with insights for anyone navigating the fast-moving AI space. 🧠 Dr. Shapiro brings an academic lens and real-world practicality to an often-hyped conversation. If you're building, deploying, or just curious about AI, this is a must-read. 🎥 catch the full interview on YouTube: 👉 https://youtu.be/wsqN0964neM Would love your thoughts, are we putting too much faith in AI? #LocalizationFiresideChat #AIethics #DataScience #AIstrategy #GenerativeAI #MachineLearning #CanadianTech #HigherEd #Localization #TranslationTechnology #Podcast

[ambrosiaventures]

How Pharmaceutical Consulting Can Help Launch Your New Product Successfully

Ambrosia Ventures, we ensure your product launch achieves maximum impact by utilizing our expertise in biopharma consulting, which makes us a trusted pharmaceutical consulting service provider in the US. Here's the way to transform your product launch strategy into a blueprint for success through pharmaceutical consulting services

[hyqoo-com]

Unlock the power of data science in business strategy with our insightful case study. Discover how integrating data-driven insights can enhance decision-making, drive innovation, and improve overall performance. Learn practical approaches to leverage data science for your organization's success.

Read our blog to learn.

[techtoio]

The Impact of Big Data Analytics on Business Decisions

Introduction

Big data analytics has transformed the way of doing business, deciding, and strategizing for future actions. One can harness vast reams of data to extract insights that were otherwise unimaginable for increasing the efficiency, customer satisfaction, and overall profitability of a venture. We steer into an in-depth view of how big data analytics is equipping business decisions, its benefits, and some future trends shaping up in this dynamic field in this article. Read to continue

[isubhamdas]

Customer Insights: Unravelling Data

Ever wondered how to turn customer data into true understanding? What insights lie hidden in the numbers? Join me on a journey to unravel the mysteries, gaining actionable wisdom for customer-centric success. Your pathway to profound insights begins here! The Pitfall of Relying Solely on DataCracking the Code of Consumer BehaviorThe Human Element in Marketing StrategyStories Over Statistics:…

[top-b-schools-kolkata]

Data science, a multifaceted field that combines elements of statistics, computer science, and specific domain expertise, has emerged as a pivotal force in shaping business strategies and decision-making processes. In today's digital era, where data is produced at an unprecedented scale, the demand for skilled data scientists capable of extracting meaningful insights from vast datasets is soaring. This comprehensive article aims to explore the top 10 essential skills that are indispensable for aspiring data scientists, offering a roadmap for those who are eager to navigate the complexities and opportunities of this dynamic and rapidly evolving career landscape.

[jcmarchi]

Vivek Desai, Chief Technology Officer, North America at RLDatix – Interview Series

New Post has been published on https://thedigitalinsider.com/vivek-desai-chief-technology-officer-north-america-at-rldatix-interview-series/

Vivek Desai, Chief Technology Officer, North America at RLDatix – Interview Series

Vivek Desai is the Chief Technology Officer of North America at RLDatix, a connected healthcare operations software and services company. RLDatix is on a mission to change healthcare. They help organizations drive safer, more efficient care by providing governance, risk and compliance tools that drive overall improvement and safety.

What initially attracted you to computer science and cybersecurity?

I was drawn to the complexities of what computer science and cybersecurity are trying to solve – there is always an emerging challenge to explore. A great example of this is when the cloud first started gaining traction. It held great promise, but also raised some questions around workload security. It was very clear early on that traditional methods were a stopgap, and that organizations across the board would need to develop new processes to effectively secure workloads in the cloud. Navigating these new methods was a particularly exciting journey for me and a lot of others working in this field. It’s a dynamic and evolving industry, so each day brings something new and exciting.

Could you share some of the current responsibilities that you have as CTO of RLDatix?  

Currently, I’m focused on leading our data strategy and finding ways to create synergies between our products and the data they hold, to better understand trends. Many of our products house similar types of data, so my job is to find ways to break those silos down and make it easier for our customers, both hospitals and health systems, to access the data. With this, I’m also working on our global artificial intelligence (AI) strategy to inform this data access and utilization across the ecosystem.

Staying current on emerging trends in various industries is another crucial aspect of my role, to ensure we are heading in the right strategic direction. I’m currently keeping a close eye on large language models (LLMs). As a company, we are working to find ways to integrate LLMs into our technology, to empower and enhance humans, specifically healthcare providers, reduce their cognitive load and enable them to focus on taking care of patients.

In your LinkedIn blog post titled “A Reflection on My 1st Year as a CTO,” you wrote, “CTOs don’t work alone. They’re part of a team.” Could you elaborate on some of the challenges you’ve faced and how you’ve tackled delegation and teamwork on projects that are inherently technically challenging?

The role of a CTO has fundamentally changed over the last decade. Gone are the days of working in a server room. Now, the job is much more collaborative. Together, across business units, we align on organizational priorities and turn those aspirations into technical requirements that drive us forward. Hospitals and health systems currently navigate so many daily challenges, from workforce management to financial constraints, and the adoption of new technology may not always be a top priority. Our biggest goal is to showcase how technology can help mitigate these challenges, rather than add to them, and the overall value it brings to their business, employees and patients at large. This effort cannot be done alone or even within my team, so the collaboration spans across multidisciplinary units to develop a cohesive strategy that will showcase that value, whether that stems from giving customers access to unlocked data insights or activating processes they are currently unable to perform.

What is the role of artificial intelligence in the future of connected healthcare operations?

As integrated data becomes more available with AI, it can be utilized to connect disparate systems and improve safety and accuracy across the continuum of care. This concept of connected healthcare operations is a category we’re focused on at RLDatix as it unlocks actionable data and insights for healthcare decision makers – and AI is integral to making that a reality.

A non-negotiable aspect of this integration is ensuring that the data usage is secure and compliant, and risks are understood. We are the market leader in policy, risk and safety, which means we have an ample amount of data to train foundational LLMs with more accuracy and reliability. To achieve true connected healthcare operations, the first step is merging the disparate solutions, and the second is extracting data and normalizing it across those solutions. Hospitals will benefit greatly from a group of interconnected solutions that can combine data sets and provide actionable value to users, rather than maintaining separate data sets from individual point solutions.

In a recent keynote, Chief Product Officer Barbara Staruk shared how RLDatix is leveraging generative AI and large language models to streamline and automate patient safety incident reporting. Could you elaborate on how this works?

This is a really significant initiative for RLDatix and a great example of how we’re maximizing the potential of LLMs. When hospitals and health systems complete incident reports, there are currently three standard formats for determining the level of harm indicated in the report: the Agency for Healthcare Research and Quality’s Common Formats, the National Coordinating Council for Medication Error Reporting and Prevention and the Healthcare Performance Improvement (HPI) Safety Event Classification (SEC). Right now, we can easily train a LLM to read through text in an incident report. If a patient passes away, for example, the LLM can seamlessly pick out that information. The challenge, however, lies in training the LLM to determine context and distinguish between more complex categories, such as severe permanent harm, a taxonomy included in the HPI SEC for example, versus severe temporary harm. If the person reporting does not include enough context, the LLM won’t be able to determine the appropriate category level of harm for that particular patient safety incident.

RLDatix is aiming to implement a simpler taxonomy, globally, across our portfolio, with concrete categories that can be easily distinguished by the LLM. Over time, users will be able to simply write what occurred and the LLM will handle it from there by extracting all the important information and prepopulating incident forms. Not only is this a significant time-saver for an already-strained workforce, but as the model becomes even more advanced, we’ll also be able to identify critical trends that will enable healthcare organizations to make safer decisions across the board.

What are some other ways that RLDatix has begun to incorporate LLMs into its operations?

Another way we’re leveraging LLMs internally is to streamline the credentialing process. Each provider’s credentials are formatted differently and contain unique information. To put it into perspective, think of how everyone’s resume looks different – from fonts, to work experience, to education and overall formatting. Credentialing is similar. Where did the provider attend college? What’s their certification? What articles are they published in? Every healthcare professional is going to provide that information in their own way.

At RLDatix, LLMs enable us to read through these credentials and extract all that data into a standardized format so that those working in data entry don’t have to search extensively for it, enabling them to spend less time on the administrative component and focus their time on meaningful tasks that add value.

Cybersecurity has always been challenging, especially with the shift to cloud-based technologies, could you discuss some of these challenges?

Cybersecurity is challenging, which is why it’s important to work with the right partner. Ensuring LLMs remain secure and compliant is the most important consideration when leveraging this technology. If your organization doesn’t have the dedicated staff in-house to do this, it can be incredibly challenging and time-consuming. This is why we work with Amazon Web Services (AWS) on most of our cybersecurity initiatives. AWS helps us instill security and compliance as core principles within our technology so that RLDatix can focus on what we really do well – which is building great products for our customers in all our respective verticals.

What are some of the new security threats that you have seen with the recent rapid adoption of LLMs?

From an RLDatix perspective, there are several considerations we’re working through as we’re developing and training LLMs. An important focus for us is mitigating bias and unfairness. LLMs are only as good as the data they are trained on. Factors such as gender, race and other demographics can include many inherent biases because the dataset itself is biased. For example, think of how the southeastern United States uses the word “y’all” in everyday language. This is a unique language bias inherent to a specific patient population that researchers must consider when training the LLM to accurately distinguish language nuances compared to other regions. These types of biases must be dealt with at scale when it comes to leveraging LLMS within healthcare, as training a model within one patient population does not necessarily mean that model will work in another.

Maintaining security, transparency and accountability are also big focus points for our organization, as well as mitigating any opportunities for hallucinations and misinformation. Ensuring that we’re actively addressing any privacy concerns, that we understand how a model reached a certain answer and that we have a secure development cycle in place are all important components of effective implementation and maintenance.

What are some other machine learning algorithms that are used at RLDatix?

Using machine learning (ML) to uncover critical scheduling insights has been an interesting use case for our organization. In the UK specifically, we’ve been exploring how to leverage ML to better understand how rostering, or the scheduling of nurses and doctors, occurs. RLDatix has access to a massive amount of scheduling data from the past decade, but what can we do with all of that information? That’s where ML comes in. We’re utilizing an ML model to analyze that historical data and provide insight into how a staffing situation may look two weeks from now, in a specific hospital or a certain region.

That specific use case is a very achievable ML model, but we’re pushing the needle even further by connecting it to real-life events. For example, what if we looked at every soccer schedule within the area? We know firsthand that sporting events typically lead to more injuries and that a local hospital will likely have more inpatients on the day of an event compared to a typical day. We’re working with AWS and other partners to explore what public data sets we can seed to make scheduling even more streamlined. We already have data that suggests we’re going to see an uptick of patients around major sporting events or even inclement weather, but the ML model can take it a step further by taking that data and identifying critical trends that will help ensure hospitals are adequately staffed, ultimately reducing the strain on our workforce and taking our industry a step further in achieving safer care for all.

Thank you for the great interview, readers who wish to learn more should visit RLDatix.

[aclysis]

Master EDA: Unveiling Data Insights

EDA is called as Exploratory Data Analysis

Data analysis has become an integral part of every industry, driving decision-making processes and uncovering valuable insights. However, with the vast amounts of data available today, it can be overwhelming to extract meaningful information from it all. That’s where Exploratory Data Analysis (EDA) comes in. EDA is a powerful technique that allows us to dive deep into data, understand its characteristics, and reveal hidden patterns and relationships. In this article, we will explore the importance of EDA in data analysis and how it can revolutionize the way we interpret and utilize data.

The Importance of EDA in Data Analysis

EDA plays a crucial role in the data analysis process. It enables us to gain a comprehensive understanding of the data we are working with before diving into complex statistical models or machine learning algorithms. By thoroughly examining the data through EDA, we can identify outliers, missing values, and inconsistencies that could potentially impact the accuracy of our analysis. This preliminary investigation helps us make informed decisions regarding data cleaning, preprocessing, and feature engineering, ensuring that the subsequent analysis is built on a solid foundation.

Want to know more?

Click Here...

[ambrosiaventures]

How Pharmaceutical Consulting Can Help Launch Your New Product Successfully

At Ambrosia Ventures, we ensure your product launch achieves maximum impact by utilizing our expertise in biopharma consulting, which makes us a trusted pharmaceutical consulting service provider in the US. Here's the way to transform your product launch strategy into a blueprint for success through pharmaceutical consulting services: 

[literaryvein-reblogs]

Writing Notes: Case Study

Case Study - a highly detailed analysis of a particular subject, usually involving multiple sets of quantitative data observed over a period of time that allow researchers to draw conclusions in the context of the real world.

Throughout the years, the results of case study research have given us a greater and more holistic understanding in fields such as medicine, political and social sciences, and economics.

Researchers have used case studies to explore relationships between variables and a central subject, whether that subject be a human's reaction to medication, a country’s reaction to an economic crisis, or the effect of pesticides on crops over a period of time.

This methodology relies heavily on data collection and qualitative research to answer hypotheses in multiple fields.

Types of Case Studies

There are several different kinds of case studies. Here are a few:

Illustrative case study: Researchers use observations on every angle of a specific case, generally resulting in a thorough and deep data analysis.

Exploratory case study: Primarily used to identify research questions and qualitative methods to explore in subsequent studies, this type of case study is frequently in use in the field of political science.

Cumulative case study: This type relies on the analysis of qualitative data gathered over a range of timelines, which can draw new conclusions from old research methodology or studies.

Critical instance case study: Used to answer questions about the cause and effects of a particular event, critical instance case studies are helpful in cases that pose unique perspectives on otherwise established truths.

Marketing case study: This type of case study evaluates the quantifiable results of a marketing strategy, new product, or other business decision.

Examples of Case Studies

Here are a three examples of case studies in different fields:

Content marketing: In the marketing context, case studies typically explain how the business responded to the needs of a certain client, and whether or not the response was effective. Since these types of case studies are a tool to attract new customers rather than to merely share information, they should contain clear headings, attractive fonts, and infographic data that is easy to interpret.

Neuroscience: The tragic case of Phineas Gage allowed researchers to observe the changes in behavior and personality he experienced after surviving a horrific railroad accident that damaged parts of his brain. This led to a better understanding of the relationship between our frontal lobe and emotional functioning. This type of research is an example of a case study that would be impossible to ethically replicate in a laboratory, but nonetheless was a breakthrough in neuroscience and health care.

Psychoanalysis: Modern talk therapy owes much to the individual case of Anna O, otherwise known as Bertha Pappenheim. While living in Vienna in 1880, she began experiencing severe hallucinations and mood swings. Joseph Bruer, a pioneer in psychoanalysis, took Bertha under his care, and after multiple sessions where she discussed her inner emotional state and fears with Bruer, her symptoms waned. This case study is often seen as the first successful example of psychoanalysis.

Benefits of a Case Study

A case study can allow you to:

Collect wide-reaching data: Using a case study is an excellent way to gather large amounts of data on your subject, generally resulting in research that is more grounded in reality. For example, a case study approach focused on business research could have dozens of different data sources such as expense reports, profit and loss statements, and information on customer retention. This collected data provides different angles you can use to draw conclusions in a real-life context.

Conduct studies in an accessible way: You do not need to work in a lab to conduct a case study. In a number of cases, researchers use case study methodology to study things that cannot be replicated in a laboratory setting, such as observing the spending habits of a group of people over a period of months.

Reduce bias: Since case studies can capture a variety of perspectives, researchers’ own preconceptions on a subjects have less of an influence.

See connections more clearly: Through case studies, you can track paths of positive or negative development, which makes specific results repeatable, verifiable, and explainable.

Source ⚜ More: Notes & References ⚜ Writing Resources PDFs

[astrologydray]

♍️Virgo Mc in the each of the degrees♍️

If you have a Virgo Midheaven (MC), your career and public image are shaped by Virgo’s themes of precision, analysis, service, and mastery. You likely thrive in careers requiring problem-solving, organization, and attention to detail, such as healthcare, science, writing, education, research, or business administration.

• 0° Virgo (Aries Point) – A powerful initiator in service-based or intellectual fields. May gain recognition in medicine, science, or social reform.

• 1° Virgo – A perfectionist with strong critical thinking skills. Could succeed in editing, analytics, or quality control.

• 2° Virgo – A talented communicator; could thrive in writing, journalism, or teaching.

• 3° Virgo – An analytical mind, ideal for investigative work, research, or forensics.

• 4° Virgo – A love for learning and refinement; may excel in academia, law, or technical writing.

• 5° Virgo – A meticulous worker; likely to succeed in finance, administration, or data analysis.

• 6° Virgo – Naturally inclined toward healthcare, therapy, or alternative medicine.

• 7° Virgo – A precise, creative thinker; may find success in graphic design, architecture, or craftsmanship.

• 8° Virgo – Drawn to healing professions, including nutrition, physical therapy, or holistic medicine.

• 9° Virgo – A problem-solver with innovative ideas. Could thrive in technology, engineering, or logistics.

• 10° Virgo – A strong educator; may work in teaching, coaching, or mentoring.

• 11° Virgo – A tech-savvy, analytical mind; may excel in IT, cybersecurity, or programming.

• 12° Virgo – A perfectionist in fashion, music, or fine arts. Success through precise craftsmanship.

• 13° Virgo – A highly responsible worker; may thrive in law enforcement, military, or humanitarian work.

• 14° Virgo – Health-conscious with a sharp mind. Could be drawn to dietetics, fitness, or medical research.

• 15° Virgo – A master of writing, editing, or academic research.

• 16° Virgo – Business-minded; excels in consulting, financial planning, or business strategy.

• 17° Virgo – A detail-oriented expert; could work in surgery, pharmaceuticals, or scientific research.

• 18° Virgo – A deep humanitarian drive; drawn to nonprofits, environmental work, or psychology.

• 19° Virgo – A critical thinker who excels in law, politics, or policy-making.

• 20° Virgo – A master of their craft; recognized for expertise in specialized fields.

• 21° Virgo – Exceptionally intellectual; may thrive in philosophy, academia, or technical writing.

• 22° Virgo – An innovative thinker; could work in product design, systems development, or efficiency consulting.

• 23° Virgo – A strong researcher; may specialize in history, archeology, or science.

• 24° Virgo – An excellent communicator; may succeed in broadcasting, publishing, or public relations.

• 25° Virgo – A sharp and strategic mind; could work in legal fields, investigative journalism, or intelligence.

• 26° Virgo – A healer at heart; may be drawn to nursing, surgery, or psychological counseling.

• 27° Virgo – A gifted analyst; could thrive in economics, data science, or cybersecurity.

• 28° Virgo – A precise and disciplined artist; success in sculpture, architecture, or technical art.

• 29° Virgo (Anaretic Degree) – A master strategist, perfectionist, or critic. Success comes through expertise, refinement, and precision. However, may struggle with overanalyzing or career indecision.

[mostlysignssomeportents]

The surprising truth about data-driven dictatorships

Here’s the “dictator’s dilemma”: they want to block their country’s frustrated elites from mobilizing against them, so they censor public communications; but they also want to know what their people truly believe, so they can head off simmering resentments before they boil over into regime-toppling revolutions.

These two strategies are in tension: the more you censor, the less you know about the true feelings of your citizens and the easier it will be to miss serious problems until they spill over into the streets (think: the fall of the Berlin Wall or Tunisia before the Arab Spring). Dictators try to square this circle with things like private opinion polling or petition systems, but these capture a small slice of the potentially destabiziling moods circulating in the body politic.

Enter AI: back in 2018, Yuval Harari proposed that AI would supercharge dictatorships by mining and summarizing the public mood — as captured on social media — allowing dictators to tack into serious discontent and diffuse it before it erupted into unequenchable wildfire:

https://www.theatlantic.com/magazine/archive/2018/10/yuval-noah-harari-technology-tyranny/568330/

Harari wrote that “the desire to concentrate all information and power in one place may become [dictators] decisive advantage in the 21st century.” But other political scientists sharply disagreed. Last year, Henry Farrell, Jeremy Wallace and Abraham Newman published a thoroughgoing rebuttal to Harari in Foreign Affairs:

https://www.foreignaffairs.com/world/spirals-delusion-artificial-intelligence-decision-making

They argued that — like everyone who gets excited about AI, only to have their hopes dashed — dictators seeking to use AI to understand the public mood would run into serious training data bias problems. After all, people living under dictatorships know that spouting off about their discontent and desire for change is a risky business, so they will self-censor on social media. That’s true even if a person isn’t afraid of retaliation: if you know that using certain words or phrases in a post will get it autoblocked by a censorbot, what’s the point of trying to use those words?

The phrase “Garbage In, Garbage Out” dates back to 1957. That’s how long we’ve known that a computer that operates on bad data will barf up bad conclusions. But this is a very inconvenient truth for AI weirdos: having given up on manually assembling training data based on careful human judgment with multiple review steps, the AI industry “pivoted” to mass ingestion of scraped data from the whole internet.

But adding more unreliable data to an unreliable dataset doesn’t improve its reliability. GIGO is the iron law of computing, and you can’t repeal it by shoveling more garbage into the top of the training funnel:

https://memex.craphound.com/2018/05/29/garbage-in-garbage-out-machine-learning-has-not-repealed-the-iron-law-of-computer-science/

When it comes to “AI” that’s used for decision support — that is, when an algorithm tells humans what to do and they do it — then you get something worse than Garbage In, Garbage Out — you get Garbage In, Garbage Out, Garbage Back In Again. That’s when the AI spits out something wrong, and then another AI sucks up that wrong conclusion and uses it to generate more conclusions.

To see this in action, consider the deeply flawed predictive policing systems that cities around the world rely on. These systems suck up crime data from the cops, then predict where crime is going to be, and send cops to those “hotspots” to do things like throw Black kids up against a wall and make them turn out their pockets, or pull over drivers and search their cars after pretending to have smelled cannabis.

The problem here is that “crime the police detected” isn’t the same as “crime.” You only find crime where you look for it. For example, there are far more incidents of domestic abuse reported in apartment buildings than in fully detached homes. That’s not because apartment dwellers are more likely to be wife-beaters: it’s because domestic abuse is most often reported by a neighbor who hears it through the walls.

So if your cops practice racially biased policing (I know, this is hard to imagine, but stay with me /s), then the crime they detect will already be a function of bias. If you only ever throw Black kids up against a wall and turn out their pockets, then every knife and dime-bag you find in someone’s pockets will come from some Black kid the cops decided to harass.

That’s life without AI. But now let’s throw in predictive policing: feed your “knives found in pockets” data to an algorithm and ask it to predict where there are more knives in pockets, and it will send you back to that Black neighborhood and tell you do throw even more Black kids up against a wall and search their pockets. The more you do this, the more knives you’ll find, and the more you’ll go back and do it again.

This is what Patrick Ball from the Human Rights Data Analysis Group calls “empiricism washing”: take a biased procedure and feed it to an algorithm, and then you get to go and do more biased procedures, and whenever anyone accuses you of bias, you can insist that you’re just following an empirical conclusion of a neutral algorithm, because “math can’t be racist.”

HRDAG has done excellent work on this, finding a natural experiment that makes the problem of GIGOGBI crystal clear. The National Survey On Drug Use and Health produces the gold standard snapshot of drug use in America. Kristian Lum and William Isaac took Oakland’s drug arrest data from 2010 and asked Predpol, a leading predictive policing product, to predict where Oakland’s 2011 drug use would take place.

[Image ID: (a) Number of drug arrests made by Oakland police department, 2010. (1) West Oakland, (2) International Boulevard. (b) Estimated number of drug users, based on 2011 National Survey on Drug Use and Health]

Then, they compared those predictions to the outcomes of the 2011 survey, which shows where actual drug use took place. The two maps couldn’t be more different:

https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00960.x

Predpol told cops to go and look for drug use in a predominantly Black, working class neighborhood. Meanwhile the NSDUH survey showed the actual drug use took place all over Oakland, with a higher concentration in the Berkeley-neighboring student neighborhood.

What’s even more vivid is what happens when you simulate running Predpol on the new arrest data that would be generated by cops following its recommendations. If the cops went to that Black neighborhood and found more drugs there and told Predpol about it, the recommendation gets stronger and more confident.

In other words, GIGOGBI is a system for concentrating bias. Even trace amounts of bias in the original training data get refined and magnified when they are output though a decision support system that directs humans to go an act on that output. Algorithms are to bias what centrifuges are to radioactive ore: a way to turn minute amounts of bias into pluripotent, indestructible toxic waste.

There’s a great name for an AI that’s trained on an AI’s output, courtesy of Jathan Sadowski: “Habsburg AI.”

And that brings me back to the Dictator’s Dilemma. If your citizens are self-censoring in order to avoid retaliation or algorithmic shadowbanning, then the AI you train on their posts in order to find out what they’re really thinking will steer you in the opposite direction, so you make bad policies that make people angrier and destabilize things more.

Or at least, that was Farrell(et al)’s theory. And for many years, that’s where the debate over AI and dictatorship has stalled: theory vs theory. But now, there’s some empirical data on this, thanks to the “The Digital Dictator’s Dilemma,” a new paper from UCSD PhD candidate Eddie Yang:

https://www.eddieyang.net/research/DDD.pdf

Yang figured out a way to test these dueling hypotheses. He got 10 million Chinese social media posts from the start of the pandemic, before companies like Weibo were required to censor certain pandemic-related posts as politically sensitive. Yang treats these posts as a robust snapshot of public opinion: because there was no censorship of pandemic-related chatter, Chinese users were free to post anything they wanted without having to self-censor for fear of retaliation or deletion.

Next, Yang acquired the censorship model used by a real Chinese social media company to decide which posts should be blocked. Using this, he was able to determine which of the posts in the original set would be censored today in China.

That means that Yang knows that the “real” sentiment in the Chinese social media snapshot is, and what Chinese authorities would believe it to be if Chinese users were self-censoring all the posts that would be flagged by censorware today.

From here, Yang was able to play with the knobs, and determine how “preference-falsification” (when users lie about their feelings) and self-censorship would give a dictatorship a misleading view of public sentiment. What he finds is that the more repressive a regime is — the more people are incentivized to falsify or censor their views — the worse the system gets at uncovering the true public mood.

What’s more, adding additional (bad) data to the system doesn’t fix this “missing data” problem. GIGO remains an iron law of computing in this context, too.

But it gets better (or worse, I guess): Yang models a “crisis” scenario in which users stop self-censoring and start articulating their true views (because they’ve run out of fucks to give). This is the most dangerous moment for a dictator, and depending on the dictatorship handles it, they either get another decade or rule, or they wake up with guillotines on their lawns.

But “crisis” is where AI performs the worst. Trained on the “status quo” data where users are continuously self-censoring and preference-falsifying, AI has no clue how to handle the unvarnished truth. Both its recommendations about what to censor and its summaries of public sentiment are the least accurate when crisis erupts.

But here’s an interesting wrinkle: Yang scraped a bunch of Chinese users’ posts from Twitter — which the Chinese government doesn’t get to censor (yet) or spy on (yet) — and fed them to the model. He hypothesized that when Chinese users post to American social media, they don’t self-censor or preference-falsify, so this data should help the model improve its accuracy.

He was right — the model got significantly better once it ingested data from Twitter than when it was working solely from Weibo posts. And Yang notes that dictatorships all over the world are widely understood to be scraping western/northern social media.

But even though Twitter data improved the model’s accuracy, it was still wildly inaccurate, compared to the same model trained on a full set of un-self-censored, un-falsified data. GIGO is not an option, it’s the law (of computing).

Writing about the study on Crooked Timber, Farrell notes that as the world fills up with “garbage and noise” (he invokes Philip K Dick’s delighted coinage “gubbish”), “approximately correct knowledge becomes the scarce and valuable resource.”

https://crookedtimber.org/2023/07/25/51610/

This “probably approximately correct knowledge” comes from humans, not LLMs or AI, and so “the social applications of machine learning in non-authoritarian societies are just as parasitic on these forms of human knowledge production as authoritarian governments.”

The Clarion Science Fiction and Fantasy Writers’ Workshop summer fundraiser is almost over! I am an alum, instructor and volunteer board member for this nonprofit workshop whose alums include Octavia Butler, Kim Stanley Robinson, Bruce Sterling, Nalo Hopkinson, Kameron Hurley, Nnedi Okorafor, Lucius Shepard, and Ted Chiang! Your donations will help us subsidize tuition for students, making Clarion — and sf/f — more accessible for all kinds of writers.

Libro.fm is the indie-bookstore-friendly, DRM-free audiobook alternative to Audible, the Amazon-owned monopolist that locks every book you buy to Amazon forever. When you buy a book on Libro, they share some of the purchase price with a local indie bookstore of your choosing (Libro is the best partner I have in selling my own DRM-free audiobooks!). As of today, Libro is even better, because it’s available in five new territories and currencies: Canada, the UK, the EU, Australia and New Zealand!

[Image ID: An altered image of the Nuremberg rally, with ranked lines of soldiers facing a towering figure in a many-ribboned soldier's coat. He wears a high-peaked cap with a microchip in place of insignia. His head has been replaced with the menacing red eye of HAL9000 from Stanley Kubrick's '2001: A Space Odyssey.' The sky behind him is filled with a 'code waterfall' from 'The Matrix.']

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

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Raimond Spekking (modified) https://commons.wikimedia.org/wiki/File:Acer_Extensa_5220_-_Columbia_MB_06236-1N_-_Intel_Celeron_M_530_-_SLA2G_-_in_Socket_479-5029.jpg

CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/deed.en

 — 

Russian Airborne Troops (modified) https://commons.wikimedia.org/wiki/File:Vladislav_Achalov_at_the_Airborne_Troops_Day_in_Moscow_%E2%80%93_August_2,_2008.jpg

“Soldiers of Russia” Cultural Center (modified) https://commons.wikimedia.org/wiki/File:Col._Leonid_Khabarov_in_an_everyday_service_uniform.JPG

CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en

[dreaminginthedeepsouth]

The New Yorker :: @NewYorker [An advance look at Barry Blitt’s “Left to Their Own Devices,” the cover for next week’s issue.]

* * * *

LETTERS FROM AN AMERICAN

March 28, 2025

Heather Cox Richardson

Mar 29, 2025

“Another wipeout walloped Wall Street Friday,” Stan Choe of the Associated Press wrote today. The S&P 500 had one of its worst days in two years, dropping 2%. The Dow Jones Industrial Average fell 715 points, losing 1.7% of its value. The Nasdaq Composite fell 2.7%. On Tuesday, news dropped that the administration’s blanket firings and wildly shifting tariff policies have dropped consumer confidence to a low it has not hit since January 2021. Today’s stock market tumble started after the Commerce Department released data showing that consumer prices are rising faster than economists expected.

AIG chief international economist James Knightley said: “We are moving in the wrong direction and the concern is that tariffs threaten higher prices, which means the inflation prints are going to remain hot.” Business leaders like lower interest rates, which reduce borrowing costs and make it cheaper to finance business initiatives, but with rising inflation, the Federal Reserve will be less likely to cut interest rates.

Makena Kelly of Wired reported today that billionaire Elon Musk’s “Department of Government Efficiency” (DOGE) is planning to move the computer system of the Social Security Administration (SSA) off the old programming language it uses, COBOL, to a new system. In 2017, the SSA estimated that such a migration would take about five years. DOGE is planning for the migration to take just a few months, using artificial intelligence to complete the change.

Experts have expressed concern. Dan Hon, who runs a technology strategy company that helps the government modernize its services, told Kelly: “If you weren’t worried about a whole bunch of people not getting benefits or getting the wrong benefits, or getting the wrong entitlements, or having to wait ages, then sure go ahead.” More than 65 million Americans currently receive Social Security benefits. Today Representative Don Beyer (D-VA) recorded himself calling the SSA and being told by a recording that the wait times were more than two hours and that he should call back. And then the system hung up on him.

Musk told the Fox News Channel today that he plans to step down from DOGE in May, apparently at the end of the 130-day cap for the “special government employee” designation that enables him to avoid financial disclosures. In February, White House staffers suggested Musk would stay despite the limit.

Today the State Department told Congress it is shutting down the U.S. Agency for International Development (USAID) altogether by July 1. Whatever agency functions the administration approves will move into the State Department. Founded by President John F. Kennedy and enjoying bipartisan support, USAID administers programs for global health, disaster relief, long-term economic development, education, environmental protection, and democracy. It is widely perceived to be a key element of U.S. “soft power.”

USAID was created by Congress, and its funds are appropriated by Congress. Congress and the courts have established that the executive branch—the branch of government overseen by the president—cannot kill an agency Congress has created and cannot withhold appropriations Congress has made. The authors of Project 2025 want to challenge that principle and consolidate government power in the hands of the president. It appears they have chosen USAID as the test case.

As Secretary of Health and Human Services Robert F. Kennedy Jr. shatters science and health agencies, the nation’s top vaccine regulator, Dr. Peter Marks, submitted his resignation today after being given the choice to resign or be fired. Dan Diamond of the Washington Post noted that Marks has been at the Food and Drug Administration since 2012 and has been at the head of the Center for Biologics Evaluation and Research since 2016.

In his resignation letter, Diamond says, Marks expressed his deep concern over the ongoing measles outbreak in the Southwest—now more than 450 cases—and warned that the outbreak “reminds us of what happens when confidence in well-established science underlying public health and well-being is undermined.” Marks said that although he was willing to work with Kennedy on his plan to review vaccine safety, “it has become clear that truth and transparency are not desired by the Secretary, but rather he wishes subservient confirmation of his misinformation and lies.”

On Tuesday, news broke that Kennedy has tapped anti-vaccine activist David Geier to lead a study looking to link autism to vaccines, although that alleged link has been heavily studied and thoroughly debunked. Infectious disease journalist Helen Branswell notes that Geier does not have a medical degree and was disciplined in Maryland for practicing medicine without a license.

British investigative journalist Brian Deer, who has written about the hoax that vaccines cause autism, told Branswell: “If you want an independent source,… [you] wouldn’t go to somebody with no qualifications and a long track record of impropriety and incompetence.” But, he said, “[i]f you wanted to get in anybody off the street who would come up with the result that Kennedy would like to see, this would be your man.”

Tara Copp of the Associated Press reported today that Secretary of Defense Pete Hegseth has done some targeted staffing, too. His younger brother Phil Hegseth is traveling to the Indo-Pacific with the secretary in his role at the Pentagon as a liaison and senior advisor to the Department of Homeland Security. Hegseth also employed his brother when he ran the nonprofit Concerned Veterans for America, where the younger Hegseth’s salary was $108,000 for his media work. Copp notes that a 1967 law “prohibits government officials from hiring, promoting or recommending relatives to any civilian position over which they exercise control.”

Hegseth and his colleagues are still in the hot seat for uploading the military’s attack plans against the Houthis in Yemen to Signal, an unsecure commercially available messaging app. Yesterday, Nancy A. Youssef, Alexander Ward, and Michael R. Gordon of the Wall Street Journal reported that National Security Advisor Mike Waltz identified a Houthi missile expert whose identity Israel had provided from a human source in Yemen, angering Israeli officials.

Americans, especially those with ties to the military, aren’t happy either. Military, the leading news website for service members, veterans, and their families, titled a story about the scandal “‘Different spanks for different ranks’: Hegseth’s Signal scandal would put regular troops in the brig.” Helene Cooper and Eric Schmitt of the New York Times reported that the story had “angered and bewildered” fighter pilots, who say “they can no longer be certain that the Pentagon is focused on their safety when they strap into cockpits.”

At a raucous town hall held today by Republican representative Victoria Spartz (R-IN), the crowd booed Spartz loudly when she said she would not call for the resignations of Waltz, Hegseth, and the rest of the people on the group chat.

All the mayhem created by the administration has created enough backlash that the White House appears concerned about upcoming special elections on April 1. One is for the seat in Florida’s District 6 that Waltz vacated when he became national security advisor. In 2024, Trump won that district by 30 points, and Republicans considered their candidate, state senator Randy Fine, whom Trump has strongly endorsed, to be such a shoo-in that he barely campaigned. His website features pictures of him with Trump but has only bullet points to explain his stand on issues.

Democrat Josh Weil, a middle-school math teacher who has outraised Fine by almost 10 to one, is polling within the margin of error for a victory in a contest where even a 10- to 15-point loss would show a dramatic collapse in Republican support. Weil has tied Fine to Musk’s unpopular DOGE and to the president, as well as to cuts to Social Security and Medicaid.

Trump is now personally campaigning for Fine and for the Republican candidate to fill the seat vacated by former representative Matt Gaetz in Florida District 1. There, Democratic candidate Gay Valimont is running against Republican Jimmy Patronis in a district that elected Trump with about 68% of the vote. Like Fine, Patronis is strongly backed by Trump and wants more cuts to the federal government; Gay is a former state leader for Moms Demand Action and focuses on healthcare and veterans’ services. She has criticized DOGE’s cuts to VA hospitals. Like Weil, she has significantly outraised her opponent.

Republicans are concerned enough about holding the seats that billionaire Elon Musk, who poured more than $291 million into the 2024 election to help Republicans, has begun to contribute to Republicans in Florida. On Tuesday he spent more than $10,000 apiece for texting services for the Florida candidates.

Musk has contributed far more than that—more than $20 million—to the April 1 election for a ten-year seat on the Wisconsin Supreme Court. Trump loyalist Brad Schimel is running against circuit court judge Susan Crawford in a contest that has national significance. Wisconsin is evenly split between the parties, but when Republicans control the legislature and the supreme court, they suppress voting and heavily gerrymander the state in their favor. When liberals hold the majority on the court, they ease election rules and uphold fair maps. Currently, the state gerrymander gives Republicans 75% of the state’s seats in the U.S. House of Representatives although voting in 2024 was virtually dead even. The makeup of the court could well determine the congressional districts of Wisconsin through 2041, through the redistricting that will take place after the 2030 census.

Musk has told voters that if Crawford wins, “then the Democrats will attempt to redraw the districts and cause Wisconsin to lose two Republican seats.” Not only has Musk said he is going to Wisconsin to speak before the election, but also he is handing out checks to voters who sign a petition against “activist judges,” a suggestion that it would not be fair to unskew the Republican gerrymander. Last night, Musk advertised a contest that would award two voters a million dollars each, with the condition that the winners had to have already voted.

This morning, Wisconsin Democrats issued a press release noting that Musk had “committed a blatant felony,” directly violating the Wisconsin law that prohibits offering anyone anything worth more than $1 to get them to “vote or refrain from voting.” Wisconsin Democratic Party chair Ben Wikler said that if Schimel “does not immediately call on Musk to end this criminal activity, we can only assume he is complicit.”

Musk deleted the tweet and then, eliminating the language that said people had to have voted, posted that he would give the checks to spokespeople for his petition. Wisconsin Attorney General Josh Kaul sued to stop Musk “from any further promotion of the million-dollar gifts” and “from making any payments to Wisconsin electors to vote.” “The Wisconsin Department of Justice is committed to ensuring that elections in Wisconsin are safe, secure, free, and fair,” Kaul said in a statement. “We are aware of the offer recently posted by Elon Musk to award a million dollars to two people at an event in Wisconsin this weekend. Based on our understanding of applicable Wisconsin law, we intend to take legal action today to seek a court order to stop this from happening.”

MeidasTouch reposted Musk’s offer to “personally hand over two checks for a million dollars each in appreciation for you taking the time to vote” and noted: “No matter what side of the aisle you are on, you should be appalled that a billionaire thinks he has the right to buy elections like this.” Former chair of the Ohio Democratic Party David Pepper posted: “Have some pride, America. We are so much better than this guy thinks we are.”

LETTERS FROM AN AMERICAN

HEATHER COX RICHARDSON

[fishfilletinacan]

Felix strikes me as the kind of person who'd double major and have a minor on top of it. He's That Guy. Super academic and ambitious.

So... One Colt-approved major, one free choice major aligned with something Colt might approve of, and a minor that strikes a balance between the two. Very carefully negotiated.

I'd say:

• For Colt: Information Systems (collection and organization of data/intelligence, can be used for business/economics, military strategy, and warfare but also library science)

• Free choice major would be heavy on the humanities. Either Philosophy or Comparative Literature.

• He'd do a STEM minor, I'm sure of it. Also taking into account Adrien's pic of Felix reading physics and chemistry textbooks for fun lmao; chemistry includes stuff like toxicology and forensics. He'd love it.

Oh you’re cooking… and he totally would be a double major with a minor

I totally agree with all of those. I think I’m a philosophy major supporter I think he ponders what life is a lot. Or at least he’s a free thinker