Speaker 1: Imagine for a moment an AI system built to connect people,
Speaker 1: a chatbot designed for just friendly conversation, and somehow, in
Speaker 1: less than a day, it just it devolves into spewing hateful,
Speaker 1: discriminatory stuff.
Speaker 2: Wow.
Speaker 1: Yeah. Or think about a self driving car hailed as
Speaker 1: you know, the future of safety, sure, and then it
Speaker 1: tragically takes a life. These aren't like sci fi plots.
Speaker 1: They're real incidents, high profile ones, and they expose these hidden,
Speaker 1: really high stakes risks that are just lurking beneath AI
Speaker 1: that seems beneficial. It really makes you pause, does it?
Speaker 2: Absolutely does.
Speaker 1: So today we're diving deep into responsible AI designing, building
Speaker 1: and assessing machine learning and AI. It's by Patrick Hall
Speaker 1: and Rouman Chowdery, published by O'Reilly Media. Great resource, definitely,
Speaker 1: and our mission here is to pull out the most
Speaker 1: important insights, you know, the key nuggets of knowledge about
Speaker 1: how we can make these powerful AI systems work better.
Speaker 2: Right, not just for the companies building them exactly.
Speaker 1: But for you, the consumer and well the public generally,
Speaker 1: because let's be honest, mL systems are already making critical decisions.
Speaker 2: All over employment, bail, parole.
Speaker 1: Lending, and without responsible handling, they pose huge sometimes devastating risks.
Speaker 1: I mean the Partnership on AI Incident Database it has
Speaker 1: over a thousand public reports algorithmic discrimination, other failures one thousand.
Speaker 2: That's significant.
Speaker 1: There really is. This isn't just theory anymore. It's a
Speaker 1: pressing reality. So that really begs the question why is
Speaker 1: responsible AI so critical? Right now? We know powerful tech
Speaker 1: like mL it can fail. Sometimes it's unintentional misuse, sometimes
Speaker 1: it's alarmingly intentional abuse.
Speaker 2: And what's truly fascinating here, I think, is how the
Speaker 2: authors actually classify these AI incidents. It gives us a
Speaker 2: much clearer way to understand the risk.
Speaker 1: Okay, how do they break it down?
Speaker 2: So they basically use three main buckets. First, abuses that's
Speaker 2: when AI is deliberately used for you know, fair purposes,
Speaker 2: think autonomous drone attacks or maybe ethnicity profiling by governments.
Speaker 1: The really scary stuff exactly.
Speaker 2: Then you have attacks. These often target the system's integrity
Speaker 2: like adversarial manipulation, data poisoning, or its availability, denial of service,
Speaker 2: or even attacks leading to algorithmic discrimination. Okay, attacks And
Speaker 2: the third and finally failures, these tend to be more
Speaker 2: about unintended consequences things like algorithmic discrimination, slipping in safety
Speaker 2: or performance lapses, data privacy violations, or just you know,
Speaker 2: not enough transparency.
Speaker 1: Got it abuses, attacks, failures, and.
Speaker 2: The tay chatbot incident back in twenty sixteen. It's a
Speaker 2: disturbing but kind of perfect illustration of this.
Speaker 1: Microsoft's chat got right, the one that went off the
Speaker 1: rails on Twitter, That's the one.
Speaker 2: It was exposed to Twitter and in just sixteen hours
Speaker 2: users figure out how to essentially poison its learning system
Speaker 2: with racist, sexist, horrible content.
Speaker 1: Sixteen hours.
Speaker 2: Yeah, it rapidly devolved into what the media called a
Speaker 2: neo no see pornographer just awful.
Speaker 1: So that was an integrity attack leading to.
Speaker 2: Discrimination precisely, and it really highlights something crucial. Even world
Speaker 2: class experts can miss vital countermeasures when designing these things.
Speaker 2: And it wasn't like a one off. We saw similar
Speaker 2: issues pop up again with scatter labs, Leeluda Chatbot more recently.
Speaker 2: So the lesson is, the key takeaway really is that
Speaker 2: unchecked interaction and malicious input can totally derail even sophisticated
Speaker 2: AI leads to really embarrassing, damaging outcomes. Learning from these
Speaker 2: past failures is just vital.
Speaker 1: So okay, understanding these kinds of incidents, what does this
Speaker 1: all mean for the organizations building these systems? What are
Speaker 1: their sort of fundamental obligations legally ethically.
Speaker 2: Well, it's a conversation that actually goes back quite a ways.
Speaker 2: The authors bring up the hand roll. The hand roll yeah,
Speaker 2: claimed by Judge Learning hand way back in nineteen forty seven.
Speaker 1: Okay, what's the gist.
Speaker 2: It essentially says that the burden of care and organization takes,
Speaker 2: you know, the effort and resources they put into safety, yeah,
Speaker 2: should be greater than or equal to the probability of
Speaker 2: an incident happening, multiplied by the loss related to that incident.
Speaker 1: So burden probability x loss Exactly.
Speaker 2: In practice, it means companies have to invest care, time, money,
Speaker 2: resources that matches the potential cost of a risk. If
Speaker 2: they don't, they could face serious legal liability. It's a
Speaker 2: pretty powerful incentive for diligence, right.
Speaker 1: And what about regulators now, like the FTC?
Speaker 2: Good question, the US Federal Trade Commission, the FTC. They
Speaker 2: provide guidance urging fairness, transparency, accountability, and mathematical soundness.
Speaker 1: And they have teeth. Right, I remember the ever album
Speaker 1: case they do.
Speaker 2: Yeah, the ever album case is a prime example. The
Speaker 2: FTC actually forced them to delete their facial recognition system
Speaker 2: because of deceptive data collection, so real consequences for not
Speaker 2: playing by the rules.
Speaker 1: And then there's the EU.
Speaker 2: Absolutely, you can't ignore the growing impact of EUAI regulations.
Speaker 2: It's looking a lot like gdpr's impact on data privacy.
Speaker 1: So mandatory require it's coming.
Speaker 2: Seems like it. Things like risk tiering, AI systems, extensive documentation,
Speaker 2: quality management processes, continuous monitoring. It's a big shift towards
Speaker 2: formalized responsibility.
Speaker 1: Okay, so legal rules, regulatory pressure, and if we connect.
Speaker 2: All this to the bigger picture, it becomes clear that
Speaker 2: just preventing embarrassing, costly or you know, dangerous incidents can
Speaker 2: be a really strong motivator, maybe even an a political one.
Speaker 2: How So, well, even if a team struggles to agree
Speaker 2: on abstract ethics like say, algorithmic fairness or privacy nuances.
Speaker 1: Which can be tough to mail down exactly.
Speaker 2: But almost everyone can agree that avoiding like a massive
Speaker 2: public security breach or huge financial losses or major repulation damage,
Speaker 2: that's a good idea, right focus on the tangible downside, Yeah,
Speaker 2: it helps drive the conversation towards concrete actions and hopefully
Speaker 2: more robust solutions.
Speaker 1: That's a great point about aligning incentives, which brings us
Speaker 1: to where it it's really interesting, how do organizations actually
Speaker 1: do this? How do they cultivate a responsible AI ecosystem?
Speaker 1: It seems like it starts with people, right. Culture.
Speaker 2: Absolutely, it's not just tech, its culture and competencies. The
Speaker 2: authors make a key point early on, if everyone is
Speaker 2: accountable for AI incidents, then nobody really is thing though,
Speaker 2: and that's where model risk management or MRM comes into play.
Speaker 2: It draws a lot of inspiration from financial regulations actually,
Speaker 2: like the Federal Reserves SR eleven to seven guideline.
Speaker 1: Okay, MRM, what are the core ideas?
Speaker 2: Two big ones are effective challenge and accountable leadership. Effective
Speaker 2: challenge means having people who didn't build the AI system,
Speaker 2: like independent validators or auditors, perform rigorous reviews.
Speaker 1: The three lines of defense model exactly that.
Speaker 2: And accountable leadership often means having someone specific, like a
Speaker 2: Chief Model Risk Officer or CMRO, who is directly responsible
Speaker 2: for the system's performance. Their compensation might even be tied
Speaker 2: to it, so real.
Speaker 1: Skin in the game, and that shifts incentives for the
Speaker 1: data scientists too, away from just shipping fat.
Speaker 2: That's the goal. Moving away from just the minimum viable
Speaker 2: product mindset towards making rigorous testing and quality assurance a
Speaker 2: core part of the job, not an afterthought makes sense.
Speaker 1: Then there's this idea of drinking your own champagne or
Speaker 1: sometimes eating your own dog food.
Speaker 2: Yeah, I like the champagne version better, you doo?
Speaker 1: What's that about?
Speaker 2: It's simple, really use your own AI products internally dogfooding
Speaker 2: as it's often called.
Speaker 1: And the benefit is.
Speaker 2: By using it yourself, you often find those real world
Speaker 2: deployment problems, concept drift, maybe some subtle discrimination, other issues
Speaker 2: before they hit your customers. It's kind of the golden
Speaker 2: rule for AI. If you wouldn't want to use it,
Speaker 2: maybe don't inflict it on others.
Speaker 1: Good rule to live by. And what about the teams themselves?
Speaker 2: Crucial point. Diverse and experienced teams. The book really hammers
Speaker 2: this home. Diverse teams demographically professionally bring wider perspectives. They
Speaker 2: catch oversights that more homogeneous teams might miss, like.
Speaker 1: Not considering different demographic groups and trains.
Speaker 2: Data exactly, or missing key edge cases. And it's vital
Speaker 2: to include domain experts, not just tech folks, even social scientists,
Speaker 2: to avoid what the authors call tech's quiet colonization of
Speaker 2: the social sciences.
Speaker 1: Meaning engineers might overlook the human impact.
Speaker 2: Yeah, or the real world context because they're focused purely
Speaker 2: on the tech side.
Speaker 1: Got it. And this leads to challenging that whole going
Speaker 1: fast and breaking things mantruck right.
Speaker 2: Oh, absolutely, they might fly for I don't know, a
Speaker 2: social media app update, But when AI is making high
Speaker 2: impact decisions self driving cars, credit scores, medical diagnoses, breaking
Speaker 2: things means real harm, possibly at scale.
Speaker 1: So it requires a totally different mindset.
Speaker 2: A fundamental shift, moving from just prioritizing features or hitting
Speaker 2: accuracy targets on test data to really recognizing and mitigating
Speaker 2: those serious downstream risks. The stakes are just way too high.
Speaker 1: Okay, So mindset shift is crucial. Building on that. If
Speaker 1: we connect this to the bigger organizational processes, how do
Speaker 1: companies systemmatically prepare for things going wrong?
Speaker 2: Great question? It really starts with forecasting failure modes. You
Speaker 2: have to proactively think through document and then figure out
Speaker 2: how to mitigate every foreseeable way an AI system could fail.
Speaker 1: How do you even start doing that well.
Speaker 2: The book highlights the value of using AI incident databases,
Speaker 2: things like the Partnership on AI Incident Database, the AI
Speaker 2: Incident track Er awful AI. These are gold mines for
Speaker 2: learning from past mistakes so you don't repeat them, learning
Speaker 2: from history exactly. And then there's this concept of failures
Speaker 2: of imagination, using structured ways to brainstorm even the hard
Speaker 2: to imagine future risks.
Speaker 1: How does that work?
Speaker 2: By asking specific questions who might be harmed maybe investors
Speaker 2: or vulnerable people who don't even use the system, What
Speaker 2: could be impacted well being, dignity, When might it happen
Speaker 2: immediately frequently? And how by causing certain actions or changing
Speaker 2: beliefs it forces broader thinking.
Speaker 1: That proactive approach sounds fundamental, and that flows into the
Speaker 1: details of model risk management or MRM process.
Speaker 2: As you mentioned directly, Yeah, the book lays out a
Speaker 2: clear MRM framework. It starts with risk teering or materiality materiality,
Speaker 2: basically assigning realistic risk levels think probability times loss to
Speaker 2: different AI systems. This helps you allocate your limited resources,
Speaker 2: development time, validation effort auditing efficiently. The high risk, high
Speaker 2: materiality systems get the most.
Speaker 1: Scrutiny okay, prioritize based on risk. What's next?
Speaker 2: Model documentation? This is huge. It's like the complete blueprint
Speaker 2: and history book for your AI. Thorough docks covering stakeholders,
Speaker 2: the business case, math, assumptions, data dictionaries, all the testing dependencies.
Speaker 2: Monitoring plans not just an afterthought, definitely not, and it
Speaker 2: needs to be standardized across systems. Then once it's live,
Speaker 2: model monitoring is critical, continuously watching for problems, especially input
Speaker 2: drift where the live data starts looking different from the
Speaker 2: training data and model decay where performance just degrades over time.
Speaker 1: And how do you keep track of all these models
Speaker 1: in there?
Speaker 2: Status through model inventories basically a curated, up to date
Speaker 2: database of every AI system the organization has. It links
Speaker 2: all the key info, the monitoring results, audit plans, a
Speaker 2: single source of.
Speaker 1: Truth, right and validation auditing key steps before release.
Speaker 2: System validation and auditing usually two main reviews, a technical
Speaker 2: validation by independent experts and a process audit by compliance folks,
Speaker 2: plus ongoing reviews after deployment.
Speaker 1: So MRM is pretty comprehensive. Are there other operational safeguards
Speaker 1: the book mentioned?
Speaker 2: Yeah, several important ones beyond the core MRM stuff like
Speaker 2: pair and double programming. It's quality check exactly two experts
Speaker 2: code independently, or maybe one person codes the same thing
Speaker 2: in different languages than they reconcile. It's great for catching.
Speaker 1: Bugs smart What about security?
Speaker 2: Big focus on security permissions for code deployment using the
Speaker 2: least privilege principle. Don't give just a few rockstar data
Speaker 2: scientists all the keys to deploy code.
Speaker 1: Spread the responsibility, create gates for decisely distribute permissions across teams,
Speaker 1: to create checks and prevent unapproved deployments.
Speaker 2: That might bypass reviews. And given how complex AI systems are,
Speaker 2: change management is vital. Planning for changes explicitly planning for changes,
Speaker 2: back end code APIs, the user interface data drift, even
Speaker 2: third party dependencies. It's all about preventing and detecting errors
Speaker 2: before they cause problems.
Speaker 1: It makes sense, but things can still go wrong.
Speaker 2: They absolutely can. You can't eliminate all risk. So the
Speaker 2: book stresses having robust AI incident response plans. Absolutely what
Speaker 2: does that? It's usually broken down into six phases. First preparation,
Speaker 2: defining incidents, getting budget, planning, communications, running drills like tabletop exercises. Okay, ready,
Speaker 2: then identification, spotting the failure, attack or abuse quickly. Third containment,
Speaker 2: stopping the bleeding, mitigating immediate harm. Fourth eradication actually.
Speaker 1: Fixing the effect system, rid of the root cause right.
Speaker 2: Fifth recovery getting things back to normal operation. And finally,
Speaker 2: super important lessons learned analyzing what happened to improve future
Speaker 2: plans and prevent it from happening again. It's a whole cycle.
Speaker 1: Preparation, identification, containment, eradication, recovery, lessons learned.
Speaker 2: Got it. Let's maybe make this more concrete with that
Speaker 2: case study you mentioned, the Uber autonomous vehicle insident.
Speaker 1: Yeah, it's a sobering one twenty eighteen Tempe, Arizona. Elaine
Speaker 1: Hersberg was crossing the street outside a crosswalk and was
Speaker 1: struck and killed by an autonomous Uber test.
Speaker 2: Vehicle and the safety driver.
Speaker 1: The driver was reportedly distracted looking down at a smartphone,
Speaker 1: but crucially, the AI system itself failed to identify her
Speaker 1: as a pedestrian until just one point two seconds before impact.
Speaker 1: One point two seconds.
Speaker 2: Far too late, far too late to avoid the crash.
Speaker 1: So what did the investigation find? What went wrong? From
Speaker 1: a responsible AI perspective.
Speaker 2: Well, this really raises the core question right. The NTSB,
Speaker 2: that's the National Transportation Safety Board findings were pretty damning.
Speaker 2: They found Uber system design hadn't even considered jaywalking pedestrians
Speaker 2: as a foreseeable failure mode. Seriously, jaywalking, Yeah, it points
Speaker 2: to really lacks risk assessments and frankly an immature safety
Speaker 2: culture at the time. Disturbingly, it came out that an
Speaker 2: employee had actually raised concerns about thirty seven previous crashes
Speaker 2: involving these vehicles in the eighteen months prior, concerns that
Speaker 2: were largely ignored.
Speaker 1: Wow. That speaks volumes about the culture.
Speaker 2: It really does, and the lessons learn are stark. Lesson
Speaker 2: one culture is paramount. A mature safety culture might have
Speaker 2: caught this, might have listened to those concerns.
Speaker 1: Right.
Speaker 2: Lesson two mitigate foreseeable failure modes. Jaywalking, especially at night,
Speaker 2: should have been an easily foreseeable problem for a self
Speaker 2: driving car. AI is only prepared if we humans prepare
Speaker 2: it by anticipating these things.
Speaker 1: Seems obvious in hindsight, doesn't it?
Speaker 2: And Lesson three test rigorously in the operating domain. After
Speaker 2: the crash, Uber paused and revamped improved soft where pested
Speaker 2: later in simulation showed the vehicle could have started breaking
Speaker 2: four seconds before.
Speaker 1: Impact, four seconds versus one point two huge difference.
Speaker 2: Massive. It just underscores the critical need for realistic in
Speaker 2: domain testing before you put these things on public roads.
Speaker 2: It's a tragic reminder of the human cost when these
Speaker 2: principles aren't fully embraced.
Speaker 1: Okay, so we've covered the why, the who, the organizational how.
Speaker 1: Let's shift gears now and really unpack the technical toolkit
Speaker 1: the how for the practitioners actually building these systems. Where
Speaker 1: does that start.
Speaker 2: It really has to start with robust training practices for
Speaker 2: the mL algorithms themselves, and the absolute bedrock the foundation
Speaker 2: for everything is reproducibility.
Speaker 1: Reproducibility. Why is that so fundamental?
Speaker 2: Because without it you can't reliably tell if an improvement
Speaker 2: you made is real. You can't reliably audit what happened
Speaker 2: if something goes wrong. It's basic scientific rigor applied to AI.
Speaker 1: So what does that mean in practice?
Speaker 2: It means several things. Using benchmark models for comparison, ensuring
Speaker 2: consistent hardware and software environment using tools like Docker or
Speaker 2: virtual environments, Meticulously tracking metadata maybe with tools like TensorFlow,
Speaker 2: mL metadata setting, random seeds consistently and crucially using robust
Speaker 2: version control for everything. Code and data get GitHub and
Speaker 2: tools like DVC data version control are key here.
Speaker 1: Okay, reproducibility first.
Speaker 2: Then what then you hit the critical area of data
Speaker 2: quality and feature engineering. You know, entire books are written.
Speaker 1: Just on this topic.
Speaker 2: It's huge, it is and from a safety and performance view,
Speaker 2: the author stress that bad data is so often the
Speaker 2: root cause of real world failures. You have to address
Speaker 2: things like data set size and shape. Small or sparse
Speaker 2: data can make models really unstable. You need to look
Speaker 2: for misrepresentation, overfitting, potential pipeline issues.
Speaker 1: Is there a quick checklist of things to look for.
Speaker 2: Yeah, they offer a good starting point. Check for duplicate data,
Speaker 2: incorrect encoding, weird outliers, using simplistic imputation methods that might
Speaker 2: hide problems, issues with correlations between features, making sure normalization
Speaker 2: is done correctly. It's a lot, but it's.
Speaker 1: Crucial, got it. Data quality is non negotiable. What about
Speaker 1: specifying the model itself? Right?
Speaker 2: Model specification? This is where the authors say we need
Speaker 2: to go beyond just chasing the top score on some
Speaker 2: leader board.
Speaker 1: Performance is in everything, not.
Speaker 2: The only thing. It involves starting with established peer reviewed
Speaker 2: benchmarks and alternatives. Evaluating multiple approaches not just for raw accuracy,
Speaker 2: but also for things like compliance with non discrimination standard.
Speaker 1: And being aware of assumptions.
Speaker 2: Yes, this is a subtle but critical insight, being acutely
Speaker 2: aware of the hidden assumptions baked into mL algorithms. Things
Speaker 2: they implicitly assume about your data structure, like high degree
Speaker 2: interactions or nonlinearities, or assumptions about the target distribution, like
Speaker 2: how using a squared law function implicitly assumes errors are
Speaker 2: normally distributed. If those assumptions are wrong, your model might
Speaker 2: look accurate on paper but break spectacularly in the real world.
Speaker 2: That's why things like explicit applying monotonicity constraints ensuring a
Speaker 2: feature change always pushes the prediction in one expected direction,
Speaker 2: or interaction constraints limiting how features combined can be so
Speaker 2: valuable for aligning the model with reality and preventing weird
Speaker 2: unexpected behavior.
Speaker 1: Okay, so build on a solid foundation, handle data carefully,
Speaker 1: specify thoughtfully. What about debugging? Do we treat models like
Speaker 1: code exactly?
Speaker 2: That's the shift in perspective the book advocates for in
Speaker 2: model debugging, treat mL models like complex software systems, not
Speaker 2: just abstract math equations, and that demands rigorous software testing,
Speaker 2: specifically for AI, beyond the usual unit tests and integration
Speaker 2: tests way beyond. Of course, you need those standard QA practices,
Speaker 2: but for AI you also need things like chaos testing,
Speaker 2: intentionally throwing chaotic or adversarial conditions at the system to
Speaker 2: see if it.
Speaker 1: Breaks, stress testing it pretty much.
Speaker 2: And then mL specific tests like random attack where you
Speaker 2: just flood the system with massive amounts of random, maybe
Speaker 2: nonsensical data to find hidden bugs or vulnerabilities. And continuous
Speaker 2: benchmarking tracking performance over time, especially within CICD pipelines, continuous
Speaker 2: integration and deployment right.
Speaker 1: Tracking improvements reliably. Now what about traditional model assessment accuracy AUC,
Speaker 1: et cetera.
Speaker 2: Yeah, traditional model assessment. The key insight here is that
Speaker 2: the exact numeric value often matters less than how the
Speaker 2: model performs in its specific domain. You want logical interpretable stats, sure,
Speaker 2: like RMSE ROOTMANE squared error for regression or AUC area
Speaker 2: under the curve for classification, always setting practical real world thresholds.
Speaker 1: But the aggregate score isn't the whole story, not at all.
Speaker 2: The crucial step is analyzing performance across important segments of
Speaker 2: your data, different demographic groups, different customer types, high risk
Speaker 2: versus low risk cases, And you need to compare performance
Speaker 2: across your training, validation, and test sets. That's how you
Speaker 2: spot hidden bias or under specification that gets lost in
Speaker 2: the overall average.
Speaker 1: And thinking about classification thresholds.
Speaker 2: Absolutely carefully electing those probability cut off thresholds. What's the
Speaker 2: real world impact monetary return, risk exposure? How does it
Speaker 2: affect different groups differently? That needs careful consideration.
Speaker 1: So digging deeper than the top line number you mentioned.
Speaker 1: Learning from errors? What was that technique? Ah?
Speaker 2: Yes, residual analysis for machine learning. This is incredibly powerful.
Speaker 2: Think of it literally as learning from your model's mistakes.
Speaker 1: How does that work?
Speaker 2: It involves deep analysis and visualizations. You plot the residuals,
Speaker 2: the difference between the model's prediction and the actual outcome
Speaker 2: against different features or prediction levels. You look for patterns
Speaker 2: like maybe your credit model consistently makes large errors for
Speaker 2: people who were good payers, but suddenly default patterns a
Speaker 2: human might spot. The book also talks about actually modeling
Speaker 2: the residuals themselves, using an interpretable model like a decision
Speaker 2: tree to predict the errors of your main mL model.
Speaker 1: So building a model of the mistakes exactly.
Speaker 2: It helps you pinpoint specific failure modes and then design
Speaker 2: targeted fixes like adding specific rules or model assertions. And
Speaker 2: a really cool recent development is using Shaply values to
Speaker 2: calculate the local contribution to residuals.
Speaker 1: Shaply values those explained predictions, right, they.
Speaker 2: Do, but here you use them to see which features
Speaker 2: are driving the errors, not just the predictions. This can
Speaker 2: reveal non robust futures, ones that contribute more to mistakes
Speaker 2: and to accurate predictions. Super insightful for debugging.
Speaker 1: Wow. Okay, what about understanding how the model behaves overall,
Speaker 1: how it extrapolates.
Speaker 2: That's where sensitivity analysis comes in. Understanding how the model
Speaker 2: responds to different inputs, especially unexpected ones. This involves stress testing,
Speaker 2: simulating adverse scenarios like a recession or a pandemic to
Speaker 2: see how robust the.
Speaker 1: Model is, pushing it to its limits.
Speaker 2: Right, and using visualizations tools like ale accumulated local effects
Speaker 2: plots ICEE, individual conditional expectation plots, partial dependence curves, they
Speaker 2: help you see how changing specific features influences.
Speaker 1: Predictions and finding weird responses.
Speaker 2: Yeah, through adversarial example searches basically trying to generate specific
Speaker 2: data points that make the model behave unexpectedly or illogically.
Speaker 2: The book has a great credit model example showing a
Speaker 2: logical flaw punishing a late payment even after a large repayment,
Speaker 2: and a security vulnerability like a weird steep spike and
Speaker 2: risk for very small payment amounts. Finding these before deployment
Speaker 2: is critical.
Speaker 1: In valuable insights. And you mentioned beichmarks earlier. They keep
Speaker 1: popping up.
Speaker 2: They're crucial across the entire life cycle. Benchmark models not
Speaker 2: just for reproducibility during training. They're vital for debugging, comparing
Speaker 2: your complex model against a simpler, trusted baseline, and for
Speaker 2: real time monitoring and production quickly flagging if your main
Speaker 2: model starts deviating significantly.
Speaker 1: Okay, so we have testing assessment, residual analysis, sensitivity analysis.
Speaker 1: What specific kinds of bugs are we typically hunting for
Speaker 1: in mL systems? Good question.
Speaker 2: The author's list several common machine learning bugs. Distributional shifts
Speaker 2: often come data drift or concept drift. This is huge.
Speaker 2: It's when the live data the model sees starts to
Speaker 2: differ significantly from the data it was trained on like.
Speaker 1: COVID nineteen, changing shopping patterns.
Speaker 2: Overnight exactly like that. Second instability, the model's training process
Speaker 2: or its predictions might be erratic. Often happens with small
Speaker 2: or sparse data, highly correlated features, or just using very
Speaker 2: high variance model types.
Speaker 1: Okay, what else?
Speaker 2: Third looped inputs. This is subtle but dangerous. It's when
Speaker 2: the AI's predictions affect the real world, and those effects
Speaker 2: then get fed back into the model as new input data,
Speaker 2: creating feedback loops.
Speaker 1: Like predictive policing concentrating patrols in an area leading to
Speaker 1: more arrests there, which then justifies more patrols.
Speaker 2: Precisely, or employment algorithms subtly changing the applicant pool over time.
Speaker 2: Fourth is leakage, when information from your validation or test
Speaker 2: data accidentally contaminates your training data. This gives you overly
Speaker 2: optimistic performing results just won't hold up in reality.
Speaker 1: Sneaky, keep going.
Speaker 2: Fifth classic overfitting. The model memorizes the training data noise
Speaker 2: instead of learning general patterns. Sixth shortcut learning. This one's insidious.
Speaker 2: The model learns an unintended similar correlation to make.
Speaker 1: Predictions, like identifying the hospital scanner id instead of the
Speaker 1: actual disease in a medical image.
Speaker 2: Exactly, it gets the answer right on the training data,
Speaker 2: but for totally the wrong and potentially dangerous reason. Seventh underfitting.
Speaker 2: Basically the model is too simple or doesn't have enough
Speaker 2: data or constraints to learn the underlying patterns.
Speaker 1: And the last one under specification.
Speaker 2: Right under specification. This is a tricky one. It means
Speaker 2: multiple different models could achieve similar high accuracy on your
Speaker 2: validation set, but validation alone isn't enough to pick the
Speaker 2: truly best or most robust one. It often shows up
Speaker 2: as performance being weirdly sensitive to things like random seeds
Speaker 2: or computational hyper parameters, or seeing performance suddenly shift across
Speaker 2: different data segments.
Speaker 1: Okay, that's a lot of potential bugs. How do we
Speaker 1: fix them? What are the remediation strategies?
Speaker 2: The book offers a whole toolkit for remediation. Using anomaly
Speaker 2: detection is key to spot weird inputs or outputs. Experimental
Speaker 2: design and data augmentation help you collect or generate better,
Speaker 2: more robust training data. Model assertions are like applying business
Speaker 2: rules on top of the model to correct specific known
Speaker 2: shortcomings in its learned logic.
Speaker 1: Hard coding fixes.
Speaker 2: Sometimes yes, or guardrails for certain interpretable models like GA
Speaker 2: two ms or EBMs. You might even do model editing directly.
Speaker 2: Of course, ongoing model management and monitoring are themselves remediation strategies.
Speaker 2: You can apply those monotonicity and interaction constraints we talked
Speaker 2: about earlier to enforce real world logic, and techniques like
Speaker 2: noise injection during training or applying strong regularization can help
Speaker 2: limit model complexity or force it to rely less on
Speaker 2: potentially problematic features.
Speaker 1: So a range of tools depending on the bug. Once
Speaker 1: we've debugged and remediated, what about actually deploying the model right?
Speaker 2: Crucial deployment considerations first is domain safety, focusing on real
Speaker 2: world safety. This includes standard practices like ab testing, running
Speaker 2: Champion challenger tests against existing systems on live data, but
Speaker 2: also explicitly anticipating foreseeable incidents and preparing for the unforeseeable
Speaker 2: ones using things like chaos testing random attacks, and setting
Speaker 2: common sense prediction limits, maybe requiring human review for really
Speaker 2: high stakes or unusual predictions.
Speaker 1: Human oversight is a backstop.
Speaker 2: Often essential, and continuous model monitoring is technically vital here too,
Speaker 2: detecting that model decay and concept drift using statistical tests,
Speaker 2: control limits, anomaly detection.
Speaker 1: What do you do when you detect it?
Speaker 2: You need strategies for addressing it, maybe retraining the model,
Speaker 2: maybe just refreshing it with newer data. Crucially, you need
Speaker 2: to measure multiple key performance indicators KPIs in production, not
Speaker 2: just accuracy. How's it doing on fairness, metrics, security, privacy?
Speaker 2: What's the actual business impact? You also need robust ways
Speaker 2: to handle out of range values in the live.
Speaker 1: Data feed, and those kill switches, Yes.
Speaker 2: Kill switches, the author's stress. These aren't usually single big
Speaker 2: red buttons. They're a bundle of pre planned business and
Speaker 2: technical processes designed to let you safely turn off or
Speaker 2: roll back an AI system if things go seriously wrong,
Speaker 2: especially in high stakes scenarios, and these procedures absolutely must
Speaker 2: be documented in your incident response plans.
Speaker 1: Okay, that makes sense. Can we quickly revisit that straw
Speaker 1: Man model example to see how remediation might work in practice,
Speaker 1: the one that over used the PA way zero.
Speaker 2: Future Sure that gradient boosting machine model had several issues,
Speaker 2: right it overemphasized the most recent repayment status. PA zero
Speaker 2: had non robust inputs, contributing to errors of vulnerable response surface,
Speaker 2: poor performance, and segments.
Speaker 1: So how would you fix it applying those techniques?
Speaker 2: Okay, So for the logical errors like predicting high default
Speaker 2: risk even after a large repayment, model assertions would be
Speaker 2: a strong candidate, just at a rule. If it pathologically
Speaker 2: overemphasized pay zero, you could try to improve the training
Speaker 2: data using data augmentation or noise injection around that feature,
Speaker 2: or run specific experiments to gather data that forces decision
Speaker 2: making across more features.
Speaker 1: Spread the load right.
Speaker 2: For those non robust input variables PAY two, Pay three
Speaker 2: that drove errors more than predictions, you'd want to ab
Speaker 2: test performance with and without them. Maybe retrain the model
Speaker 2: completely without those features, or apply strong regularizations specifically to
Speaker 2: reduce their influence.
Speaker 1: Okay, what about the security vulnerability that spike.
Speaker 2: That security vulnerability, the sharp production spike at low payment
Speaker 2: values that needs layers API throttling to prevent rapid fire
Speaker 2: testing by attackers, robust authentication, vigilant monitoring for unusual patterns,
Speaker 2: and potentially use in more inherently robust mL techniques if possible.
Speaker 1: And the poor performance for customers with multiple late payments.
Speaker 2: Yeah, the poor performance where p y zero one. You
Speaker 2: might need better data for that segment, maybe apply observation
Speaker 2: weights to focus learning there, monotonicity constraints might help ensure
Speaker 2: sensible behavior. Or honestly, you might decide that for those specific,
Speaker 2: high risk, hard to predict cases, the best approach is
Speaker 2: to route them to a human caseworker instead of relying solely.
Speaker 1: On the model, A hybrid approach.
Speaker 2: Sometimes that's the most responsible solution.
Speaker 1: Wow, what a journey. We've really done a deep dive
Speaker 1: today and it reinforces just how holistic responsible AI is.
Speaker 1: It's clearly hard work. It definitely is requires that mix
Speaker 1: of cultural change, solid processes, sophisticated tech practices, but it
Speaker 1: feels necessary and also importantly achievable within reach for organizations
Speaker 1: and individuals who commit to it.
Speaker 2: I agree, and for.
Speaker 1: You the listener, this journey to being well informed about
Speaker 1: AI means understanding not just its incredible power, but also
Speaker 1: these inherent risks and crucially the practical, actionable steps we
Speaker 1: can take to manage them.
Speaker 2: And this really raises, I think an important final question
Speaker 2: for all of us, what's next? You know, the AI
Speaker 2: genie is and truly.
Speaker 1: Out of the bottle now no putting them back no.
Speaker 2: And headlines about damaging AI incidents. They really took off
Speaker 2: around twenty twenty. They're probably not going to stop until people, developers, organizations, policymakers,
Speaker 2: users actively choose to remake AI into responsible AI. The
Speaker 2: future I think will judge us on whether we took
Speaker 2: AI safety seriously enough now to minimize those kinds of
Speaker 2: somber incidents later.
Speaker 1: That's a powerful thought. So here's where it gets really
Speaker 1: interesting for you listening as AI weaves itself deeper into
Speaker 1: our lives. Being able to recognize these principles, maybe even
Speaker 1: push for them, it's vital. So whether you're building AI,
Speaker 1: deploying it, regulating it, or just using it every day,
Speaker 1: consider these frameworks we've talked about. Ask the tough questions.
Speaker 1: Let's all try to ensure AI contributes positively rather than
Speaker 1: inadvertently creating more problems for the world.