AI regulation, data privacy, and ethics - 2023 summarized

In this episode, the hosts focus on the basics of anomaly detection in machine learning and AI systems, including its importance, and how it is implemented. They also touch on the topic of large language models, the (in)accuracy of data scraping, and the importance of high-quality data when employing various detection methods. You'll even gain some techniques you can use right away to improve your training data and your models.

Intro and discussion (0:03)

• Questions about Information Theory from our non-parametric statistics episode.
• Google CEO calls out chatbots (WSJ)
• A statement about anomaly detection as it was regarded in 2020 (Forbes)
• In the year 2024, are we using AI to detect anomalies, or are we detecting anomalies in AI? Both? 

Understanding anomalies and outliers in data (6:34)

• Anomalies or outliers are data that are so unexpected that their inclusion raises warning flags about inauthentic or misrepresented data collection. 
• The detection of these anomalies is present in many fields of study but canonically in: finance, sales, networking, security, machine learning, and systems monitoring
• A well-controlled modeling system should have few outliers
• Where anomalies come from,  including data entry mistakes, data scraping errors, and adversarial agents 
• Biggest dinosaur example: https://fivethirtyeight.com/features/the-biggest-dinosaur-in-history-may-never-have-existed/

Detecting outliers in data analysis (15:02)

• High-quality, highly curated data is crucial for effective anomaly detection. 
• Domain expertise plays a significant role in anomaly detection, particularly in determining what makes up an anomaly.

Anomaly detection methods (19:57)

• Discussion and examples of various methods used for anomaly detection 
  • Supervised methods
  • Unsupervised methods
  • Semi-supervised methods
  • Statistical methods

Anomaly detection challenges and limitations (23:24)

• Anomaly detection is a complex process that requires careful consideration of various factors, including the distribution of the data, the context in which the data is used, and the potential for errors in data entry
• Perhaps we're detecting anomalies in human research design, not AI itself?
• A simple first step to anomaly detection is to visually plot numerical fields. "Just look at your data, don't take it at face value and really examine if it does what you think it does and it has what you think it has in it." This basic practice, devoid of any complex AI methods, can be an effective starting point in identifying potential anomalies.

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We're taking a slight detour from modeling best practices to explore questions about AI and consciousness. 

With special guest Michael Herman, co-founder of Monitaur and TestDriven.io, the team discusses different philosophical perspectives on consciousness and how these apply to AI. They also discuss the potential dangers of AI in its current state and why starting fresh instead of iterating can make all the difference in achieving characteristics of AI that might resemble consciousness. 

Show notes

Why consciousness for this episode?

• Enough listeners have randomly asked the hosts if Skynet is on the horizon
• Does modern or future AI have the wherewithal to take over the world, and is it even conscious or intelligent? 
• Do we even have a good definition of consciousness?

Introducing Michael Herman as guest speaker

• Co-founder of Monitaur, Engineer extraordinaire, and creator of TestDriven.io, a training company that focuses on educating and upskilling mid-level senior-level web developers.
• Degree and studies in philosophy and technology

Establishing the philosophical foundation of consciousness

• Consciousness is around us everywhere. It can mean different things to different people.
• Most discussion about the subject bypasses the Mind-Body Problem and a few key theories:
  • Dualism - the mind and body are distinct
  • Materialism - matter is king and consciousness arises in complex material systems
  • Panpsychism - consciousness is king. It underlies everything at the quantum level

The potential dangers of achieving consciousness in AI

• While there is potential for AI to reach consciousness, we're far from that point. 
• Dangers are more related to manipulation and misinformation, rather than the risk of conscious machines turning against humanity.

The need for a new approach to developing AI systems

• There's a need to start from scratch if the goal is to achieve consciousness in AI systems.
• Current modeling techniques might not lead to AI achieving consciousness. A new paradigm might be required.
• There's a need to define what consciousness in AI means and to develop a test for it. 

Final thoughts and wrap-up

• If consciousness is truly the goal, the case for starting from scratch allows for fairness and ethics to be established foundationally
• AI systems should be built with human values in mind

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Data scientists, researchers, engineers, marketers, and risk leaders find themselves at a crossroads to expand their skills or risk obsolescence. The hosts discuss how a growth mindset and "the fundamentals" of AI can help.

Our episode shines a light on this vital shift, equipping listeners with strategies to elevate their skills and integrate multidisciplinary knowledge. We share stories from the trenches on how each role affects robust AI solutions that adhere to ethical standards, and how embracing a T-shaped model of expertise can empower data scientists to lead the charge in industry-specific innovations.

Zooming out to the executive suite, we dissect the complex dance of aligning AI innovation with core business strategies. Business leaders take note as we debunk the myth of AI as a panacea and advocate for a measured, customer-centric approach to technology adoption. We emphasize the decisive role executives play in steering their companies through the AI terrain, ensuring that every technological choice propels the business forward, overcoming the ephemeral allure of AI trends. 

Suggested courses, public offerings:

• Undergrad level Stanford course (Coursera):  Machine Learning Specialization
• Graduate-level MIT Open Courseware: Machine Learning

We hope you enjoy this candid conversation that could reshape your outlook on the future of AI and the roles and responsibilities that support it.

Resources mentioned in this episode

• LinkedIn's jobs on the rise 2024
• 3 questions to separate AI from marketing hype
• Disruption or distortion? The impact of AI on future operating models
• The Obstacle is the Way by Ryan Holiday

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Get ready for 2024 and a brand new episode! We discuss non-parametric statistics in data analysis and AI modeling. Learn more about applications in user research methods, as well as the importance of key assumptions in statistics and data modeling that must not be overlooked,

After you listen to the episode, be sure to check out the supplement material in Exploring non-parametric statistics.

Welcome to 2024  (0:03)

• AI, privacy, and marketing in the tech industry
• OpenAI's GPT store launch. (The Verge)
• Google's changes to third-party cookies. (Gizmodo)

Non-parametric statistics and its applications (6:49)

• A solution for modeling in environments where data knowledge is limited.
• Contrast non-parametric statistics with parametric statistics, plus their respective strengths and weaknesses.

Assumptions in statistics and data modeling (9:48)

• The importance of understanding statistics in data science, particularly in modeling and machine learning. (Probability distributions, Wikipedia)
• Discussion about a common assumption of representing data with a normal distribution; oversimplifies complex real-world phenomena.
• The importance of understanding data assumptions when using statistical models

Statistical distributions and their importance in data analysis (15:08)

• Discuss the importance of subject matter experts in evaluating data distributions, as assumptions about data shape can lead to missed power and incorrect modeling. 
• Examples of different distributions used in various situations, such as Poisson for wait times and counts, and discrete distributions like uniform and Gaussian normal for continuous events.
• Consider the complexity of selecting the appropriate distribution for statistical analysis; understand the specific distribution and its properties.

Non-parametric statistics and its applications in data analysis (19:31)

• Non-parametric statistics are more robust to outliers and can generalize across different datasets without requiring domain expertise or data massaging.
• Methods rely on rank ordering and have less statistical power compared to parametric methods, but are more flexible and can handle complex data sets better.
• Discussion about the usefulness and limitations, which require more data to detect meaningful changes compared to parametric tests.

Non-parametric tests for comparing data sets (24:15)

• Non-parametric tests, including the K-S test and chi-square test, which can compare two sets of data without assuming a specific distribution.
• Can also be used for machine learning, classification, and regression tasks, even when the underlying data distribution is unknown.
• Normalize data before conducting hypothesis tests.
• Feature engineering and scaling before u

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It's the end of 2023 and our first season. The hosts reflect on what's happened with the fundamentals of AI regulation, data privacy, and ethics. Spoiler alert: a lot! And we're excited to share our outlook for AI in 2024.

• AI regulation and its impact in 2024.
  • Hosts reflect on AI regulation discussions from their first 10 episodes, discussing what went well and what didn't.
  • Its potential impact on innovation. 2:36
  • AI innovation, regulation, and best practices. 7:05
• AI, privacy, and data security in healthcare. 11:08
  • Data scientists face contradictory goals of fairness and privacy in AI, with increased interest in balancing both in 2024.
  • HIPAA privacy and the increasing use of machine learning and AI in healthcare.
  • Special thanks to the team at Shifting Privacy Left for a more in-depth discussion about privacy and synthetic data.
• AI safety and ethics in NLP research. 15:40
  • Does OpenAI's closed model set a bad precedent for research?
  • The tension in NLP research: AI safety, and OpenAI's approach 
• Modeling mindset and reality between scientists and AI experts. 18:44
  • Thanks to special guests who joined us in 2023
  • Josh Pyle and his insights on bias and actuarial bias, and how they've applied to their work. See episode 10.
  • Christoph Molnar for explaining the differences in mindset between scientists and AI scientists when modeling reality.  See episode 4.
• AI ethics and its challenges in the industry. 21:46
  • Andrew Clark emphasizes the importance of understanding the basics of time-series analysis and choosing the right tools for the job, rather than relying on automated methods or blindly using existing techniques.
  • Sid Mangalik: AI ethics discussion needs to level up, as people are confusing models with AGI and not addressing practical issues.
  • Andrew Clark: AI ethics is at a bad crossroads, with high-level discussions divorced from reality and companies paying lip service to ethical concerns.
• AI ethics and responsible development. 26:10
  • Companies with ethical bodies and practices are better equipped to handle AI ethics concerns.
  • Andrew expresses concern about the ethical implications of AI, particularly in the context of Google's Gemini project.
  • Comparing AI safety to carbon credits and the importance of a proactive approach to ethical considerations.
• AI ethics and its importance in business. 29:31
  • Susan highlights the importance of ethics in AI, citing examples of challenges between board and executive teams.

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Joshua Pyle joins us in a discussion about managing bias in the actuarial sciences. Together with Andrew's and Sid's perspectives from  both the economic and data science fields, they deliver an interdisciplinary conversation about bias that you'll only find here.

• OpenAI news plus new developments in language models. 0:03
  • The hosts get to discuss the aftermath of OpenAI and Sam Altman's return as CEO
  • Tension between OpenAI's board and researchers on the push for slow, responsible AI development vs fast, breakthrough model-making.
  • Microsoft researchers find that smaller, high-quality data sets can be more effective for training language models than larger, lower-quality sets (Orca 2).
  • Google announces Gemini, a trio of models with varying parameters, including an ultra-light version for phones 
• Bias in actuarial sciences with Joshua Pyle, FCAS. 9:29
  • Josh shares insights on managing bias in Actuarial Sciences, drawing on his 20 years of experience in the field.
  • Bias in actuarial work defined as differential treatment leading to unfavorable outcomes, with protected classes including race, religion, and more.
• Actuarial bias and model validation in ratemaking. 15:48
  • The importance of analyzing the impact of pricing changes on protected classes, and the potential for unintended consequences when using proxies in actuarial ratemaking.
  • Three major causes of unfair bias in ratemaking (Contingencies, Nov 2023)
  • Gaps in the actuarial process that could lead to bias, including a lack of a standardized governance framework for model validation and calibration.
• Actuarial standards, bias, and credibility. 20:45
  • Complex state-level regulations and limited data pose challenges for predictive modeling in insurance.
  • Actuaries debate definition and mitigation of bias in continuing education.
• Bias analysis in actuarial modeling. 27:16
  • The importance of identifying dislocation analysis in bias analysis.
  • Analyze two versions of a model to compare predictive power of including vs. excluding protected class (race).
• Bias in AI models in actuarial field. 33:56
  • Actuaries can learn from data scientists' tendency to over-engineer models.
  • Actuaries may feel excluded from the Big Data era due to their need to explain their methods
  • Standardization is needed to help actuaries identify and mitigate bias.
• Interdisciplinary approaches to AI modeling and governance. 42:11
  • Sid hopes to see more systematic and published approaches to addressing bias in the data science field.
  • Andrew emphasizes the importance of interdisciplinary collaboration between actuaries, data scientists, and economists to create more accurate and fair modeling systems.
  • Josh agrees and highlights the need for better governance structures to support this collaboration, citing the lack of good journals and academic silos as a challenge.

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Episode 9. Continuing our series run about model validation. In this episode, the hosts focus on aspects of performance, why we need to do statistics correctly, and not use metrics without understanding how they work, to ensure that models are evaluated in a meaningful way.

• AI regulations, red team testing, and physics-based modeling. 0:03
  • The hosts discuss the Biden administration's executive order on AI and its implications for model validation and performance.
• Evaluating machine learning models using accuracy, recall, and precision. 6:52
  • The four types of results in classification: true positive, false positive, true negative, and false negative.
  • The three standard metrics are composed of these elements: accuracy, recall, and precision.
• Accuracy metrics for classification models. 12:36
  • Precision and recall are interrelated aspects of accuracy in machine learning.
  • Using F1 score and F beta score in classification models, particularly when dealing with imbalanced data.
• Performance metrics for regression tasks. 17:08
  • Handling imbalanced outcomes in machine learning, particularly in regression tasks.
  • The different metrics used to evaluate regression models, including mean squared error.
• Performance metrics for machine learning models. 19:56
  • Mean squared error (MSE) as a metric for evaluating the accuracy of machine learning models, using the example of predicting house prices.
  • Mean absolute error (MAE) as an alternative metric, which penalizes large errors less heavily and is more straightforward to compute.
• Graph theory and operations research applications. 25:48
  • Graph theory in machine learning, including the shortest path problem and clustering. Euclidean distance is a popular benchmark for measuring distances between data points. 
• Machine learning metrics and evaluation methods. 33:06
• Model validation using statistics and information theory. 37:08
  • Entropy, its roots in classical mechanics and thermodynamics, and its application in information theory, particularly Shannon entropy calculation. 
  • The importance the use case and validation metrics for machine learning models.

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Episode 8. This is the first in a series of episodes dedicated to model validation. Today, we focus on model robustness and resilience. From complex financial systems to why your gym might be overcrowded at New Year's, you've been directly affected by these aspects of model validation.

AI hype and consumer trust (0:03) 

• FTC article highlights consumer concerns about AI's impact on lives and businesses (Oct 3, FTC)
• Increased public awareness of AI and the masses of data needed to train it led to increased awareness of potential implications for misuse.
• Need for transparency and trust in AI's development and deployment.

Model validation and its importance in AI development (3:42)

• Importance of model validation in AI development, ensuring models are doing what they're supposed to do.
• FTC's heightened awareness of responsibility and the need for fair and unbiased AI practices.
• Model validation (targeted, specific) vs model evaluation (general, open-ended).

Model validation and resilience in machine learning (8:26)

• Collaboration between engineers and businesses to validate models for resilience and robustness.
• Resilience: how well a model handles adverse data scenarios.
• Robustness: model's ability to generalize to unforeseen data.
• Aerospace Engineering: models must be resilient and robust to perform well in real-world environments.

Statistical evaluation and modeling in machine learning (14:09)

• Statistical evaluation involves modeling distribution without knowing everything, using methods like Monte Carlo sampling.
• Monte Carlo simulations originated in physics for assessing risk and uncertainty in decision-making.

Monte Carlo methods for analyzing model robustness and resilience (17:24)

• Monte Carlo simulations allow exploration of potential input spaces and estimation of underlying distribution.
• Useful when analytical solutions are unavailable.
• Sensitivity analysis and uncertainty analysis as major flavors of analyses.

Monte Carlo techniques and model validation (21:31)

• Versatility of Monte Carlo simulations in various fields.
• Using Monte Carlo experiments to explore semantic space vectors of language models like GPT.
• Importance of validating machine learning models through negative scenario analysis.

Stress testing and resiliency in finance and engineering (25:48)

• Importance of stress testing in finance, combining traditional methods with Monte Carlo techniques.
• Synthetic data's potential in modeling critical systems.
• Identifying potential gaps and vulnerabilities in critical systems.

Using operations research and model validation in AI development (30:13)

• Operations research can help find an equilibrium in overcrowding in gyms.
• Robust methods for solving complex problems in logistics and healthcare.
• Model validation's importance in addressing issues of bias and fairness in AI systems.

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Episode 7.  To use or not to use? That is the question about digital twins that the fundamentalists explore. Many solutions continue to be proposed for making AI systems safer, but can digital twins really deliver for AI what we know they can do for physical systems? Tune in and find out.

Show notes

• Digital twins by definition. 0:03
  • Digital twins are one-to-one digital models of real-life products, systems, or processes, used for simulations, testing, monitoring, maintenance, or practice decommissioning.
  • The digital twin should be indistinguishable from the physical twin, allowing for safe and efficient problem-solving in a computerized environment.
• Digital twins in manufacturing and aerospace engineering. 2:22
  • Digital twins are virtual replicas of physical processes, useful in manufacturing and space, but often misunderstood as just simulations or models.
  • Sid highlights the importance of identifying digital twin trends and distinguishing them from simulations or sandbox environments.
  • Andrew emphasizes the need for data standards and ETL processes to handle different vendors and data forms, clarifying that digital twins are not a one-size-fits-all solution.
• Digital twins, AI models, and validation in a hybrid environment. 6:51
  • Validation is crucial for deploying mission-critical AI models, including generative AI.
  • Sid clarifies the misconception that AI models can directly replicate physical systems, emphasizing the importance of modeling specific data and context.
  • Andrew and Susan discuss the confusion around modeling and its limitations, including the need to validate models on specific datasets and avoid generalizing across contexts.
  • Referenced article from Venture Beat, 10 digital twin trends for 2023
• Digital twins, IoT, and their applications. 11:05
  • Susan and Sid discuss the limitations of digital twins, including their inability to interact with the real world and the complexity of modeling systems.
  • They reference a 2012 NASA paper that popularized the term "digital twin" and highlight the potential for confusion in its application to various industries.
  • Sid: Digital twinning requires more than just IoT devices, it's a complex process that involves monitoring and IoT devices across the physical system to create a perfect digital twin.
  • Andrew: Digital twins raise security and privacy concerns, especially in healthcare, where there are lots of IoT devices and personal data that need to be protected.
• Data privacy and security in digital twin technology. 17:03
  • Digital twins and data privacy face off in IoT debate.
  • Susan and Andrew discuss data privacy concerns with AI and IoT, highlighting the potential for data breaches and lack of transparency.
• Digital twins in healthcare and technology. 20:16
  • Susan and Andrew discuss digital twins in various industries, emphasizing their importance and technical complexities.
  • Digital twin technology has a higher barrier to entry due to data security and privacy con

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Episode 6. What does systems engineering have to do with AI fundamentals? In this episode, the team discusses what data and computer science as professions can learn from systems engineering, and how the methods and mindset of the latter can boost the quality of AI-based innovations.

Show notes

•  News and episode commentary 0:03
  • ChatGPT usage is down for the second straight month.
  • The importance of understanding the data and how it affects the quality of synthetic data for non-tabular use cases like text. (Episode 5, Synthetic data)
  • Business decisions. The 2012 case of Target using algorithms in their advertising. (CIO,  June 2023)
• Systems engineering thinking. 3:45
  • The difference between building algorithms and building models, and building systems. 
  • The term systems engineering came from Bell Labs in the 1940s, and came into its own with the NASA Apollo program.
  • A system is a way of looking at the world. There's emergent behavior, complex interactions and relationships between data.
  • AI systems and ML systems are often distant from the expertise of people who do systems engineering.
• Learning the hard way. 9:25
  • Systems engineering is about doing things the hard way, learning the physical sciences, math and how things work.
  • What else can be learned from the Apollo program.
  • Developing a system, and how important it is to align the importance of criticality and safety of the project.
  • Systems engineering is often associated incorrectly with waterfall in software engineering, 
• What is a safer model to build? 14:26
  • What is a safer model, and how is systems engineering going to fit in with this world?
  • The data science hacker culture can be counterintuitive to this approach 
  • For example, actuaries have a professional code of ethics and a set way that they learn.
• Step back and review your model. 18:26
  • Peer review your model and see if they can break it and stress-test it. Build monitoring around knowing where the fault points are and also talk to business leaders.
  • Be careful about the other impacts that can have on the business or externally on the people who start using it.
  • Marketing this type of engineering as robustness of the model, identifying what it is good at and what it's bad at, and that in itself can be a piece of selling.
  • Systems thinking gives a chance to create lasting models and lasting systems, not just models.
• How can you think of modeling as a system? 23:23
  • Andrew shares

Good AI Needs Great Governance
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