Speaker 1: Welcome to the sentient Code, where intelligence is engineered, autonomy
Speaker 1: is emerging, and a line between human and machine grows thinner.
Speaker 1: Each episode, we decode the algorithms, explore the robotics, and
Speaker 1: examine the ideas shaping the future of artificial minds.
Speaker 2: Imagine you've just found your absolute dream house.
Speaker 3: Oh nice, Yeah, you.
Speaker 2: Have the down payment saved up, your credit history is
Speaker 2: completely solid, and you fill out the mortgage paperwork feeling
Speaker 2: pretty confident, naturally, or picture applying for that perfect job
Speaker 2: you're undeniably qualified for. You hits a bit, and almost instantly,
Speaker 2: an automated email arrives, just saying application denied.
Speaker 3: Ouch. Yeah, that's brutal right.
Speaker 2: So you call the bank or the HR department and
Speaker 2: you ask a very simple question, You just.
Speaker 3: Ask why, good luck getting an answer to that exactly.
Speaker 2: The person on the other end of the they hesitate.
Speaker 2: They pull up your file and they say, we don't
Speaker 2: actually know. The algorithm just flagged you as a high risk.
Speaker 3: It is a uniquely modern kind of bureaucratic nightmare.
Speaker 2: Really, it totally is.
Speaker 3: Because there's no human reasoning to appeal to. There's no
Speaker 3: no supervisor to escalate too. It's just this silent mathematical wall.
Speaker 3: You are just trapped by the output of a system
Speaker 3: that even its creators cannot fully explain.
Speaker 2: Okay, let's unpack this because we are all basically living
Speaker 2: with this black box problem in modern artificial intelligence. It's
Speaker 2: a reality where these systems governing the trajectory of human
Speaker 2: lives just operate in total.
Speaker 3: Opacity, right completely in the dark.
Speaker 2: But today we are going to explore this groundbreaking approach
Speaker 2: engineered by researchers at Osaka Metropolitan University. It was led
Speaker 2: by Professor yusikin Ojima, and they have essentially designed a
Speaker 2: way out of the black box, which is huge. It's massive.
Speaker 2: They aren't just trying to like slap a post trading
Speaker 2: bandage on a biased neural network. They are building AI
Speaker 2: that is inherently, fair fully auditible, and highly accurate, baked
Speaker 2: right into the foundational architecture from day one.
Speaker 3: Yeah, and to understand why this foundational shift is so necessary,
Speaker 3: we really have to look at the architectural flaw in
Speaker 3: how traditional deep learning models process the data we feed them.
Speaker 2: Right, the data is the core issue exactly.
Speaker 3: These algorithms they learn by consuming massive historical data sets,
Speaker 3: so when a conventional AI analyzes, say, decades of past
Speaker 3: loan approvals or hiring decisions, it doesn't just learn the
Speaker 3: isolated markers of a good.
Speaker 2: Candidate, because it's reading everything right.
Speaker 3: It absorbs all the historical systemic biases related to race
Speaker 3: or gender, age, income, all that stuff embedded in the data,
Speaker 3: and it codifies them into its predictive models.
Speaker 2: So the system essentially just acts as a mirror, right,
Speaker 2: reflecting our historical flaws right back at us, but under
Speaker 2: this guise of like objective mathematical certainty.
Speaker 3: Yeah, the math makes it feel neutral when it really isn't.
Speaker 2: And the real crisis, though, is the unexplainability factor. Because
Speaker 2: once a deep learning model is fully trained, the logic
Speaker 2: it uses to make a decision is distributed across billions,
Speaker 2: sometimes literally trillions, of microscopic weights and parameters.
Speaker 3: You're basically looking at a hyperdimensional matrix at that point.
Speaker 2: Right, Because the reasoning is locked away in that dense
Speaker 2: web of math. You can't just query the network and
Speaker 2: to ask if it discriminated against a specific demographic it
Speaker 2: just says no.
Speaker 3: Which means proving systemic bias in a deep learning model
Speaker 3: is often just an exercise and futility. I mean, you
Speaker 3: can test the inputs and observe the outputs, but the
Speaker 3: hidden layers remain completely opique.
Speaker 2: So what does the industry usually do about this?
Speaker 3: Well, the standard response has been to attempt these post
Speaker 3: training fixes, so trying to mathematically adjust the final outputs
Speaker 3: to enforce fairness metrics after the black box has already
Speaker 3: done its processing.
Speaker 2: I mean, it's like baking a cake with spoiled milk
Speaker 2: and then trying to scrape off the bad taste with frosting.
Speaker 3: That is a very accurate, if slightly gross, way to
Speaker 3: put it.
Speaker 2: Yes, you might make the outside look a little better,
Speaker 2: but the underlying structural failure is still right there in
Speaker 2: the cake. These post hawk adjustments almost always degrade the
Speaker 2: system's overall predictive power, and they just fail entirely to
Speaker 2: fix the root cause of the bias locked inside the model.
Speaker 3: This race is an important question though. If patching the
Speaker 3: AI doesn't work, how do we build fairness into the
Speaker 3: foundation exactly?
Speaker 2: We have to rethink the architecture itself. We have to
Speaker 2: move away from rigid, opaque structures, which brings us to
Speaker 2: the core of the Osaka team's innovation. They are shifting
Speaker 2: the paradigm toward fuzzy logic.
Speaker 3: Fuzzy logic, yeah, because traditional computing, and by extension, traditional
Speaker 3: algorithmic decision making, it's built on strict Boolem.
Speaker 2: Presholds, just ones and zeros.
Speaker 3: Exactly yes or no. If a bank sets a hard
Speaker 3: cut off for a prime interest rate at a credit
Speaker 3: score of seven hundred, that is an absolute boundary. An
Speaker 3: applicant with a seven hundred gets.
Speaker 2: The primary and someone with a six ninety nine.
Speaker 3: An applicant with a six ninety nine is automatically categorized
Speaker 3: entirely differently, they might be denied or given a subprime rate.
Speaker 2: So you cross this invisible, totally arbitrary line and your
Speaker 2: outcome flips completely. It's like a simple on off light switch.
Speaker 2: That kind of rigid categorization is practically the definition of
Speaker 2: systemic unfairness because it ignores the reality that a six'
Speaker 2: ninety nine and a seven hundred represent virtually identical financial
Speaker 2: behaviors in the real.
Speaker 3: World, right human beings rarely fit into perfect binary, Boxes.
Speaker 2: So fuzzy logic is more like a dimmer. Switch, right
Speaker 2: he steps in to smooth out that drop.
Speaker 3: Off, yes a dimmer switch is the perfect way to
Speaker 3: think about. It instead of a boolean, threshold fuzzy logic
Speaker 3: maps these variables onto a continuous mathematical. Curve it uses
Speaker 3: what are called degrees of.
Speaker 2: Membership degrees of, membership, okay how does that?
Speaker 3: Work so it assigns a degree of truth between zero and.
Speaker 3: One rather than rigidly categorizing our applicant as just, unqualified
Speaker 3: a fuzzy system might evaluate their score as having say
Speaker 3: a point eight degree of membership in the good credit
Speaker 3: category and a two degree in the average credit.
Speaker 2: Category OH i, See.
Speaker 3: And the rules use plain linguistic. Terms they take the
Speaker 3: form of things like if income is mostly high and
Speaker 3: debt is somewhat, low then the approval chance is very.
Speaker 2: High that makes so much more. Sense by processing data
Speaker 2: through degrees of, truth the system becomes incredibly robust against
Speaker 2: those arbitrary boundary. Issues so a minor perturbation in the,
Speaker 2: input like making one dollar less than a, threshold or
Speaker 2: scoring a fraction of a point lower on some, assessment
Speaker 2: it doesn't trigger a massive disproportionate.
Speaker 3: Penalty, exactly the transition is. Smooth small changes in input
Speaker 3: don't make decisions flip, abruptly.
Speaker 2: Which means similar individuals receive mathematically similar. Evaluations that establishes
Speaker 2: a baseline of structural equity before we even get to
Speaker 2: the complex modeling.
Speaker 3: Part it, does but that smooth transition introduces a massive engineering.
Speaker 3: CHALLENGE i, mean it is one thing to manually write
Speaker 3: a dozen fuzzy rules to control the temperature of an air,
Speaker 3: Conditioner but it is an entirely different universe to write
Speaker 3: the millions of, overlapping nuanced fuzzy rules required to evaluate
Speaker 3: the global credit market or a modern hiring.
Speaker 2: Pipeline, yeah a human programmer just can't sit down and
Speaker 2: manually code all the linguistic rules for a data set
Speaker 2: with hundreds of intersecting.
Speaker 3: Variables no, Way it's too.
Speaker 2: Complex so if human engineering can't scale to meet, it
Speaker 2: and deep neural networks are too opaque to, trust we
Speaker 2: need an algorithm that can autonomously write and optimize its
Speaker 2: own transparent.
Speaker 3: Logic we need the system to build itself.
Speaker 2: Right and that leads us directly to the fascinating. Mechanism
Speaker 2: The osaka team utilized genetics based machine.
Speaker 3: Learning, yeah applying the principles Of darwinian evolution to software.
Speaker 2: Architecture it is so. Cool the algorithm generates these human
Speaker 2: readable fuzzy rules through a process of.
Speaker 3: Natural selection survival of the, fittest.
Speaker 2: Exactly survival of the. Fittest it creates populations of rule,
Speaker 2: sets and the better performing combinations survive and, reproduce while
Speaker 2: the weaker ones are just, discarded.
Speaker 3: And it literally splices the code. Together it might take
Speaker 3: the if condition from one highly successful rule and combine
Speaker 3: it with the then outcome of. Another it performs this
Speaker 3: mathematical crossover to create a new generation of hybrid.
Speaker 2: Rules.
Speaker 3: Wow and it also introduces random, mutations you, know slightly
Speaker 3: altering a threshold or a linguistic, variable just to make
Speaker 3: sure the system continually explores new logical.
Speaker 2: Pathways but, wait if we force THE ai to care
Speaker 2: about fairness and, simplicity doesn't the accuracy completely? Tank you
Speaker 2: can't have your cake and eat it, too.
Speaker 3: Right that is the exact technical hurdle that has stalled
Speaker 3: EQUITABLE ai for, years the assumption that fairness inherently destroys.
Speaker 3: Performance but the evolutionary process fundamentally challenges.
Speaker 2: That how so because usually if you optimize for multiple competing,
Speaker 2: things you just get, mediocrity.
Speaker 3: Right but they use multi objective optimization THE ai isn't
Speaker 3: just trying to evolve to be the most. Accurate it
Speaker 3: is simultaneously optimizing for three things overall, accuracy, fairness metrics
Speaker 3: across demographic, groups and. Interpretability so keeping the rules simple
Speaker 3: and few in, number, Okay.
Speaker 2: But how does it balance all three without just failing
Speaker 2: at all of?
Speaker 3: Them by exploring what is known as The peretto. Front
Speaker 3: because the genetic algorithm explores a vast search space over
Speaker 3: thousands of, generations it doesn't just spit out one compromise rule.
Speaker 3: Set it maps an entire curve of optimal trade off.
Speaker 2: Solutions, ah so it gives you options.
Speaker 3: Exactly in their experiments with real world benchmark data, sets
Speaker 3: they found that a, tiny highly controlled reduction in overall
Speaker 3: accuracy often led to massive sweeping gains in.
Speaker 2: Fairness, really so you just give up a little bit
Speaker 2: of perfection for a lot of equity.
Speaker 3: Exactly and sometimes the fuzzy rules even improved both metrics.
Speaker 2: Simultaneously, wait it got more accurate and more fair at
Speaker 2: the same.
Speaker 3: Time, Yeah by finding nuanced nonlinear relationships in the data
Speaker 3: that the rigid binary deep learning models completely, missed it
Speaker 3: stops penalizing the edge.
Speaker 2: Cases, yes that is, incredible but how does it actually
Speaker 2: know it's being? Fair, like how does it measure that
Speaker 2: during the?
Speaker 3: Evolution it checks its own fairness from the very first
Speaker 3: generation using standard verifiable, indicators things like demographic parity and equal, opportunity.
Speaker 2: Meaning it ensures positive outcomes are distributed evenly regardless of
Speaker 2: sensitive attributes like race or.
Speaker 3: Gender, exactly it mathematically guarantees that positive outcomes aren't. Skewed
Speaker 3: the code literally cannot survive the evolutionary process if it
Speaker 3: relies on biased.
Speaker 2: Logic this changes everything for high stakes. Applications just think about,
Speaker 2: hiring but evaluating candidates and ensuring similar qualifications means similar
Speaker 2: interview chances regardless of demographics or.
Speaker 3: Lending preventing disproportionate loan denials for specific, neighborhoods you, know
Speaker 3: avoiding digital, redlining.
Speaker 2: Or even healthcare recommending treatments without biases and, diagnostics and
Speaker 2: criminal justice balancing public safety with equitable risk assessments for
Speaker 2: bail or.
Speaker 3: Parole the stakes are incredibly high in all those.
Speaker 2: Fields here's where it gets really, interesting, though the audit.
Speaker 2: Power because these fuzzy models use dozens of rules instead
Speaker 2: of thousands or, millions humans can actually read.
Speaker 3: Them that's the interpretability constraint kicking. In it's a massive
Speaker 3: advantage over black.
Speaker 2: Boxes it's. Huge this means, regulators, ethics, boards and even
Speaker 2: everyday people can trace a decision back to the exact
Speaker 2: human readable rule that caused.
Speaker 3: It, right you can point to a rule and say
Speaker 3: this is why you were.
Speaker 2: Denied and that built in auditability builds crucial public. Trust
Speaker 2: when the logic is laid, bare society can actually debate
Speaker 2: the validity of the rules, themselves rather than just fearing
Speaker 2: the hidden machinations of a. Machine so, True so summarizing
Speaker 2: the big picture, here fairness does not have to come
Speaker 2: at the expensive. USEFULNESS ai can deliver accurate predictions while
Speaker 2: treating people equitably and giving clear reasons for its.
Speaker 3: Choices and if we connect this to the bigger, picture
Speaker 3: the future applications are just. Vast future work could extend
Speaker 3: this to dynamic environments where fairness needs change time.
Speaker 2: Oh like as the economy shifts the rules.
Speaker 3: Adapt, exactly or integrating it with federated learning where the
Speaker 3: data stays distributed across different organizations like multiple hospitals for,
Speaker 3: privacy but the algorithm still learns from all of.
Speaker 2: It that would be amazing for medical.
Speaker 3: Research, right there's even potential to hybridize these fuzzy systems
Speaker 3: with larger neural, networks creating incredibly powerful but transparent.
Speaker 2: Solutions it really reframes our entire relationship with. Algorithms we
Speaker 2: actually have the technology now to MAKE ai perfectly transparent
Speaker 2: and fair by human, standards.
Speaker 3: Which is an incredible milestone.
Speaker 2: It, is but it leads us with a lingering question to.
Speaker 2: Ponder since we can now perfectly program THE ai to
Speaker 2: follow our definitions of, equity who gets to decide which
Speaker 2: definition of fairness THE ai should evolve? Toward that is
Speaker 2: the real, Challenge, Right if THE a could balance the scales,
Speaker 2: perfectly the hardest part might just be humans agreeing on
Speaker 2: what a balanced scale actually looks. Like